Together AI vs Cerebras Inference

Agent access

1. ai-native userPoint an agent at llms.txt or agent-oriented docs

   weight 2 · round drawn
   Together AI✓fullprobed9/10

   Together AI publishes a working llms.txt (HTTP 200) with a documentation index, per-page .md docs, and even a live MCP server for docs lookup, all independently confirmed via probes plus first-party docs on agent-skills. missing for 10: no independent third-party report of an agent actually consuming llms.txt successfully in practice.

   • [probe] “PROBE llms.txt: HTTP 200 at https://docs.together.ai/llms.txt # Together AI docs > Documentation for the Together AI platform for inference…”
   • [probe] “PROBE docs-md: HTTP 200 at https://docs.together.ai/intro.md > ## Documentation Index > Fetch the complete documentation index at: https://d…”
   • [probe] “PROBE mcp-endpoint (2026-09-04): POST initialize to https://docs.together.ai/mcp answered HTTP 200 with a JSON-RPC/MCP response (event: mess…”
   • [claimed-docs] “Make your AI coding agent Together-AI-aware with ready-made skills for code generation and an MCP server for live docs lookup.”
   • [claimed-docs] “Docs MCP server: Gives your agent live access to this documentation site so it can look up current information without leaving your editor.”

   proof ↗⚑ flag

   Cerebras Inference✓fullprobed9/10

   Cerebras hosts a live llms.txt index (verified via probe returning HTTP 200 with a structured documentation index) and individual docs pages are also served in markdown form with explicit pointers back to llms.txt, exactly matching the agent-oriented docs pattern. This is directly confirmed by hands-on probes, not just vendor claims. Missing for 10: independent/community confirmation that an agent has actually consumed llms.txt successfully in practice.

   • [probe] “PROBE llms.txt: HTTP 200 at https://inference-docs.cerebras.ai/llms.txt # Cerebras Inference - [Quickstart](https://inference-docs.cerebras…”
   • [probe] “PROBE docs-md: HTTP 200 at https://inference-docs.cerebras.ai/quickstart.md > ## Documentation Index > Fetch the complete documentation inde…”
   • [claimed-docs] “Make your first Cerebras API call in just minutes.”

   proof ↗⚑ flag

2. ai-native userRun the product headlessly / in CI for automation

   weight 2 · round drawn
   Together AI✓fullprobed7/10

   Together AI is an API/SDK-first inference platform with OpenAI-compatible REST endpoints, official server-side SDKs (TypeScript/Python), batch inference, streaming via SSE, and structured/function-calling outputs — all natively usable in headless CI/automation pipelines without any UI dependency. Live probes confirm the API and status endpoints are reachable programmatically. missing for 10: no explicit CI/CD example or GitHub Actions template in evidence, no documented CLI exit-code/scripting conventions, and no independent case study of a CI pipeline using Together AI headlessly.

   • [claimed-docs] “you can point it at models hosted on Together with two changes: the API key and base URL”
   • [claimed-docs] “Call 100+ open-source models with per-token pricing and no provisioning latency.”
   • [claimed-docs] “Run asynchronous batch workloads at up to 50% lower cost.”
   • [github] “We provide support for streaming responses using Server Sent Events (SSE).”
   • [github] “This library provides convenient access to the Together REST API from server-side TypeScript or JavaScript.”
   • [probe] “PROBE models-endpoint (2026-09-04): GET https://api.together.xyz/v1/models without a key returned HTTP 401 (Missing API key) — the OpenAI-st…”
   • [claimed-docs] “Call chat, image, audio, embedding, and more through one API.”

   proof ↗⚑ flag

   Cerebras Inference✓fullcommunity7/10

   Cerebras Inference is a pure REST API with official Python/Node SDKs, OpenAI-compatible endpoints, and a Batch API for asynchronous request processing—all of which are inherently headless and scriptable for CI/automation pipelines (cerebras-docs-1, cerebras-docs-3, cerebras-docs-8, cerebras-docs-17, cerebras-gh-2). Community reports confirm real-world automated/agentic usage (coding agents, voice assistants) via API keys without needing the console UI (cerebras-comm-6, cerebras-comm-16), though some hit rate-limit friction in automated integrations (cerebras-comm-11). Missing for 10: explicit CI/CD examples (e.g., GitHub Actions), dedicated CLI tool documentation, and no first-party guidance on running in headless/CI environments specifically.

   • [claimed-docs] “Make your first Cerebras API call in just minutes.”
   • [claimed-docs] “Existing applications can use Cerebras by changing the API key, base URL, and model ID.”
   • [claimed-docs] “The Batch API lets you process groups of requests asynchronously, making it perfect for workloads where you don't need immediate results”
   • [claimed-docs] “pip install --upgrade cerebras_cloud_sdk”
   • [github] “This library provides convenient access to the Cerebras REST API from server-side TypeScript or JavaScript.”
   • [community] “The DFlash2 draft model we're using was trained on a lot of code, so if you use it in a coding agent you'll probably notice it run a lot fas…”
   • [community] “Here's a video of that running, it's very speedy - used llm-cerebras plugin with an API key from cloud.cerebras.ai, no waiting list needed a…”
   • [community] “It hits the request per minute limit instantly and then you wait a minute. (API Error: 422 ... wrong_api_format when integrating with claude…”

   proof ↗⚑ flag

3. ai-native userPlug MCP servers into this product so it can use their tools

   weight 3 · round drawn
   Together AI—none0/10

   The evidence only shows Together AI publishing its own MCP server (docs lookup) for coding agents to consume — i.e., Together acting as an MCP *server*, not as a client that lets users plug external MCP servers into it. There's no documentation or probe showing Together's platform/API can connect to and use tools from user-supplied MCP servers, despite having function/tool-calling APIs. missing for 10: any evidence of MCP-client support (configuring/connecting external MCP servers within Together's product), integration of MCP tool results into its agentic function-calling flow.

   • [claimed-docs] “Make your AI coding agent Together-AI-aware with ready-made skills for code generation and an MCP server for live docs lookup.”
   • [claimed-docs] “12 domain-specific skills that load on demand and teach your agent how to write correct Together AI code (right model IDs, SDK patterns, bes…”
   • [claimed-docs] “Gives your agent live access to this documentation site so it can look up current information without leaving your editor.”
   • [claimed-docs] “Docs MCP server: Gives your agent live access to this documentation site so it can look up current information without leaving your editor.”
   • [probe] “official MCP server documented at https://docs.together.ai/docs/agent-skills”
   • [probe] “PROBE mcp-endpoint (2026-09-04): POST initialize to https://docs.together.ai/mcp answered HTTP 200 with a JSON-RPC/MCP response (event: mess…”
   • [claimed-docs] “To build agent loops, chain tool calls inside one response (multi-step), and conversations that thread tools across many turns (multi-turn).”
   • [claimed-docs] “Multi-step function calling chains sequential function calls within one conversation turn.”

   proof ↗⚑ flag

   Cerebras Inference—none0/10

   The axis applies to this product kind (peer products hold positive or none verdicts on this story), so lack of evidence for an applicable capability is "none", never "na". (na/none harmonized at arena bring-up — see pipeline/scripts/na-harmonize.ts.)

   ⚑ flag

4. ai-native userConnect an agent via an official MCP server

   weight 3 · round to Together AI
   Together AI✓fullprobed8/10

   Together AI documents an official Docs MCP server that gives agents live access to documentation, and a live probe confirms the MCP endpoint at docs.together.ai/mcp responds correctly to an initialize call with proper protocol capabilities. This is corroborated by both first-party docs and independent hands-on verification. Missing for 10: broader coverage beyond docs lookup (e.g., an MCP server exposing inference/model-management actions rather than just documentation retrieval), and no community discussion confirming real-world agent integration experiences.

   • [claimed-docs] “Make your AI coding agent Together-AI-aware with ready-made skills for code generation and an MCP server for live docs lookup.”
   • [claimed-docs] “12 domain-specific skills that load on demand and teach your agent how to write correct Together AI code (right model IDs, SDK patterns, bes…”
   • [claimed-docs] “Gives your agent live access to this documentation site so it can look up current information without leaving your editor.”
   • [claimed-docs] “Docs MCP server: Gives your agent live access to this documentation site so it can look up current information without leaving your editor.”
   • [probe] “official MCP server documented at https://docs.together.ai/docs/agent-skills”
   • [probe] “PROBE mcp-endpoint (2026-09-04): POST initialize to https://docs.together.ai/mcp answered HTTP 200 with a JSON-RPC/MCP response (event: mess…”

   proof ↗⚑ flag

   Cerebras Inference—none0/10

   The axis applies to this product kind (peer products hold positive or none verdicts on this story), so lack of evidence for an applicable capability is "none", never "na". (na/none harmonized at arena bring-up — see pipeline/scripts/na-harmonize.ts.)

   ⚑ flag

5. ai-native userUse an official CLI

   weight 2 · round to Together AI
   Together AI~partialclaimed5/10

   Docs mention an official CLI (the 'tg' command, e.g. 'tg beta endpoints deploy...') and explicitly state fine-tuning jobs can be launched 'through the API/SDK, or with the CLI', confirming a real CLI exists for agentic/dev workflows. However, evidence lacks a dedicated CLI reference page, install instructions, full command coverage, or independent hands-on corroboration. Missing for 10: dedicated CLI documentation/installation guide, comprehensive command reference, independent user verification of CLI usage.

   • [claimed-docs] “tg beta endpoints deploy google/gemma-4-E4B-it --endpoint my-endpoint”
   • [claimed-docs] “You can launch a fine-tuning job from the console, through the API/SDK, or with the CLI”

   proof ↗⚑ flag

   Cerebras Inference—none0/10

   Evidence only shows Python/Node SDKs and a web playground/quickstart; there is no mention of an official Cerebras CLI tool anywhere in the docs, GitHub repos, or community discussion.

   • [claimed-docs] “pip install --upgrade cerebras_cloud_sdk”
   • [github] “This library provides convenient access to the Cerebras REST API from server-side TypeScript or JavaScript.”
   • [claimed-docs] “Use the playground in the Cloud Console — no key or install needed.”

   proof ↗⚑ flag

6. ai-native userDrive the product through a documented public API

   weight 3 · round drawn
   Together AI✓fullprobed9/10

   Together AI exposes an OpenAI-compatible REST API with official SDKs (TypeScript/JS), documented endpoints for chat, batch, fine-tuning, dedicated deployment, and a live, publicly reachable models endpoint confirmed by probe; docs are extensive and public. Missing for 10: a discoverable formal OpenAPI/swagger spec (probe found all candidate paths 404).

   • [claimed-docs] “you can point it at models hosted on Together with two changes: the API key and base URL”
   • [claimed-docs] “Call 100+ open-source models with per-token pricing and no provisioning latency.”
   • [github] “This library provides convenient access to the Together REST API from server-side TypeScript or JavaScript.”
   • [probe] “PROBE models-endpoint (2026-09-04): GET https://api.together.xyz/v1/models without a key returned HTTP 401 (Missing API key) — the OpenAI-st…”
   • [probe] “PROBE openapi: all candidate paths 404 (https://docs.together.ai/openapi.json, https://docs.together.ai/swagger.json, https://docs.together.…”

   proof ↗⚑ flag

   Cerebras Inference✓fullprobed9/10

   Cerebras ships a well-documented public REST API (OpenAI-compatible), official SDKs (Python/Node), quickstart docs, streaming, structured outputs, tool calling, batch API, and a live API endpoint confirmed by probe (HTTP 403 auth-gated but functional/JSON). Community evidence confirms real-world usage via SDKs and integrations (Cursor, claude-code-router, llm-cerebras plugin). Missing for 10: a discoverable OpenAPI/swagger spec (probe found 404s on all candidate paths), which would round out formal API documentation.

   • [claimed-docs] “Make your first Cerebras API call in just minutes.”
   • [claimed-docs] “Existing applications can use Cerebras by changing the API key, base URL, and model ID.”
   • [claimed-docs] “OpenAI API compatibility lets developers build on Cerebras with just two code changes.”
   • [claimed-docs] “The Cerebras API supports streaming responses, which send messages back in chunks and display them incrementally as the model generates them…”
   • [claimed-docs] “Structured Outputs constrains model responses to a JSON schema so applications can process generated data reliably.”
   • [claimed-docs] “Tool calling, also known as tool use or function calling, lets a model request functions that your application defines.”
   • [claimed-docs] “The Batch API lets you process groups of requests asynchronously, making it perfect for workloads where you don't need immediate results”
   • [github] “This library provides convenient access to the Cerebras REST API from server-side TypeScript or JavaScript.”
   • [probe] “PROBE models-endpoint (2026-09-04): GET https://api.cerebras.ai/v1/models without a key returned HTTP 403 ({"detail":"Not authenticated"}) —…”
   • [probe] “PROBE llms.txt: HTTP 200 at https://inference-docs.cerebras.ai/llms.txt # Cerebras Inference - [Quickstart](https://inference-docs.cerebras…”
   • [community] “I've been waiting on this for a LONG time. Integration with Cursor when Cerebras released their earlier models was patchy at best, even thro…”
   • [community] “Here's a video of that running, it's very speedy - used llm-cerebras plugin with an API key from cloud.cerebras.ai, no waiting list needed a…”
   • [probe] “PROBE openapi: all candidate paths 404 (https://inference-docs.cerebras.ai/openapi.json, https://inference-docs.cerebras.ai/swagger.json, ht…”

   proof ↗⚑ flag

7. ai-native userIssue scoped/least-privilege API credentials for an agent

   weight 2 · round drawn
   Together AI—none0/10

   No evidence of scoped or least-privilege API key issuance (e.g., per-project keys, role/permission scoping, or restricted-scope tokens for agents); docs only mention a single API key used for authentication, with no mention of scoping controls.

   • [claimed-docs] “you can point it at models hosted on Together with two changes: the API key and base URL”

   proof ↗⚑ flag

   Cerebras Inference—none0/10

   No evidence of scoped, least-privilege API key/credential issuance (e.g., role-based keys, permission scopes, or per-agent restricted tokens) — docs only mention basic API key usage for authentication, not fine-grained credential scoping. Missing for 10: any mention of scoped/permissioned API keys, role-based access control, or credential restriction features for agents.

   • [claimed-docs] “Make your first Cerebras API call in just minutes.”
   • [claimed-docs] “Existing applications can use Cerebras by changing the API key, base URL, and model ID.”
   • [probe] “PROBE models-endpoint (2026-09-04): GET https://api.cerebras.ai/v1/models without a key returned HTTP 403 ({"detail":"Not authenticated"}) —…”

   proof ↗⚑ flag

8. ai-native userBuild against official SDKs

   weight 2 · round drawn
   Together AI✓fullclaimed8/10

   Together AI provides an official TypeScript/JavaScript SDK (github repo) and OpenAI-compatible API/SDK usage, with docs covering chat, structured outputs, function calling, streaming, fine-tuning, and dedicated deployment all launchable via API/SDK. This directly supports AI-native, agentic development against official SDKs, including agent loop patterns and tool calling. Missing for 10: independent hands-on developer reviews specifically praising SDK quality/completeness, and no official Python SDK repo cited in evidence pack.

   • [github] “We provide support for streaming responses using Server Sent Events (SSE).”
   • [github] “This library provides convenient access to the Together REST API from server-side TypeScript or JavaScript.”
   • [claimed-docs] “you can point it at models hosted on Together with two changes: the API key and base URL”
   • [claimed-docs] “Function calling (also called tool calling) lets LLMs respond with structured function names and arguments that you can execute in your appl…”
   • [claimed-docs] “To build agent loops, chain tool calls inside one response (multi-step), and conversations that thread tools across many turns (multi-turn).”
   • [claimed-docs] “You can launch a fine-tuning job from the console, through the API/SDK, or with the CLI”
   • [claimed-docs] “Supported models can return JSON that conforms to any schema you supply, so you can read the output directly in code without retries or frag…”

   proof ↗⚑ flag

   Cerebras Inference✓fullcommunity8/10

   Cerebras ships official Python and Node/TypeScript SDKs (pip install cerebras_cloud_sdk, cerebras-cloud-sdk-node on GitHub) plus OpenAI-compatible client support, with docs covering streaming, tool calling, structured outputs, and batch APIs—clearly agentic-workflow-friendly. Community evidence confirms real-world SDK/agent integration (coding agents, Cursor support) though with some rough edges like rate-limit friction. Missing for 10: independent quality assessment of SDK docs/API reference completeness and broader language SDK coverage beyond Python/Node.

   • [claimed-docs] “pip install --upgrade cerebras_cloud_sdk”
   • [github] “This library provides convenient access to the Cerebras REST API from server-side TypeScript or JavaScript.”
   • [github] “This SDK has a mechanism that sends a few requests to `/v1/tcp_warming` upon construction to reduce the TTFT.”
   • [claimed-docs] “Existing applications can use Cerebras by changing the API key, base URL, and model ID.”
   • [claimed-docs] “OpenAI API compatibility lets developers build on Cerebras with just two code changes.”
   • [claimed-docs] “Tool calling, also known as tool use or function calling, lets a model request functions that your application defines.”
   • [claimed-docs] “The Cerebras API supports streaming responses, which send messages back in chunks and display them incrementally as the model generates them…”
   • [claimed-docs] “Structured Outputs constrains model responses to a JSON schema so applications can process generated data reliably.”
   • [community] “I've been waiting on this for a LONG time. Integration with Cursor when Cerebras released their earlier models was patchy at best, even thro…”
   • [community] “The DFlash2 draft model we're using was trained on a lot of code, so if you use it in a coding agent you'll probably notice it run a lot fas…”

   proof ↗⚑ flag

9. ai-native userSubscribe to events via webhooks

   weight 2 · round drawn
   Together AI—none0/10

   No evidence of webhook subscription support anywhere in the docs, SDKs, or probes; Together AI offers SSE streaming and a docs MCP server, but nothing about webhook event subscriptions for async notifications (e.g., fine-tune job completion, batch job status).

   ⚑ flag

   Cerebras Inference—none0/10

   No evidence anywhere in the pack mentions webhooks, event subscriptions, or callback-based notifications; Cerebras Inference appears to be a synchronous/streaming inference API with no webhook mechanism documented.

   ⚑ flag

Agentic features

1. ai-native userSet up automations that run autonomously in the background

   weight 2 · round to Cerebras Inference
   Together AI—none0/10

   The axis applies to this product kind (peer products hold positive or none verdicts on this story), so lack of evidence for an applicable capability is "none", never "na". (na/none harmonized at arena bring-up — see pipeline/scripts/na-harmonize.ts.)

   ⚑ flag

   Cerebras Inference~partialclaimed3/10

   Cerebras is an inference API/hardware provider, not an agent-orchestration platform, but its Batch API lets requests be processed asynchronously in the background without immediate results, which is a thin building block for autonomous background automations. There is no evidence of scheduling, triggers, workflow orchestration, or persistent autonomous agents — missing for 10: scheduled/triggered automation framework, agent orchestration/state management, independent evidence of autonomous background runs beyond simple async batch calls.

   • [claimed-docs] “The Batch API lets you process groups of requests asynchronously, making it perfect for workloads where you don't need immediate results”

   proof ↗⚑ flag

2. ai-native userDelegate tasks to a built-in AI assistant inside the product

   weight 3 · round drawn
   Together AI—none0/10

   The axis applies to this product kind (peer products hold positive or none verdicts on this story), so lack of evidence for an applicable capability is "none", never "na". (na/none harmonized at arena bring-up — see pipeline/scripts/na-harmonize.ts.)

   ⚑ flag

   Cerebras Inference—none0/10

   The axis applies to this product kind (peer products hold positive or none verdicts on this story), so lack of evidence for an applicable capability is "none", never "na". (na/none harmonized at arena bring-up — see pipeline/scripts/na-harmonize.ts.)

   ⚑ flag

3. ai-native userOperate the product with natural-language commands

   weight 2 · round drawn
   Together AI—none0/10

   Together AI is an inference/training API platform operated via REST API, SDKs, CLI, and console — the evidence shows structured commands (API calls, CLI syntax like 'tg beta endpoints deploy...') rather than any natural-language command interface for operating the platform itself. The agent-skills/MCP docs only help external coding agents write correct Together AI code, not let a user operate Together AI's own product surface via natural language.

   ⚑ flag

   Cerebras Inference—none0/10

   The axis applies to this product kind (peer products hold positive or none verdicts on this story), so lack of evidence for an applicable capability is "none", never "na". (na/none harmonized at arena bring-up — see pipeline/scripts/na-harmonize.ts.)

   ⚑ flag

Api quality

1. ai-native userExplore an interactive API reference with runnable examples

   weight 2 · round to Cerebras Inference
   Together AI—none0/10

   The evidence pack shows extensive prose documentation (guides, overview pages) but no reference to an interactive API reference or runnable/try-it-now code examples; a direct probe for OpenAPI/Swagger specs at standard paths returned 404 for all candidates, indicating no interactive spec-driven reference was found. No community or hands-on evidence confirms a runnable API console exists.

   • [probe] “PROBE openapi: all candidate paths 404 (https://docs.together.ai/openapi.json, https://docs.together.ai/swagger.json, https://docs.together.…”
   • [probe] “PROBE llms.txt: HTTP 200 at https://docs.together.ai/llms.txt # Together AI docs > Documentation for the Together AI platform for inference…”
   • [probe] “PROBE docs-md: HTTP 200 at https://docs.together.ai/intro.md > ## Documentation Index > Fetch the complete documentation index at: https://d…”

   proof ↗⚑ flag

   Cerebras Inference~partialprobed5/10

   Cerebras docs offer a quickstart with code snippets and a no-key Cloud Console playground for testing prompts, plus rich per-capability doc pages (streaming, tool use, structured outputs) with example code. However, there's no evidence of a true interactive API reference (e.g., Swagger/OpenAPI 'try it' explorer) — a probe explicitly found no openapi.json/swagger spec at expected paths, and no citation shows runnable code execution directly embedded in the reference docs. missing for 10: an OpenAPI/Swagger-style interactive reference, confirmation that code examples in docs are directly runnable/editable in-browser, independent user confirmation of using such a feature.

   • [claimed-docs] “Make your first Cerebras API call in just minutes.”
   • [claimed-docs] “Use the playground in the Cloud Console — no key or install needed.”
   • [claimed-docs] “Existing applications can use Cerebras by changing the API key, base URL, and model ID.”
   • [probe] “PROBE openapi: all candidate paths 404 (https://inference-docs.cerebras.ai/openapi.json, https://inference-docs.cerebras.ai/swagger.json, ht…”
   • [probe] “PROBE llms.txt: HTTP 200 at https://inference-docs.cerebras.ai/llms.txt # Cerebras Inference - [Quickstart](https://inference-docs.cerebras…”

   proof ↗⚑ flag

2. ai-native userDownload a machine-readable API spec (OpenAPI or equivalent)

   weight 2 · round drawn
   Together AI—none0/10

   Active probing for standard OpenAPI/swagger spec locations (openapi.json, swagger.json, etc.) all returned 404, and no docs page in the pack references a downloadable OpenAPI/machine-readable spec for the Together API.

   • [probe] “PROBE openapi: all candidate paths 404 (https://docs.together.ai/openapi.json, https://docs.together.ai/swagger.json, https://docs.together.…”

   proof ↗⚑ flag

   Cerebras Inference—none0/10

   Cerebras is OpenAI-API-compatible and documents endpoints extensively, but a direct probe for machine-readable spec files (openapi.json, swagger.json, etc.) returned 404 on all candidate paths, and no evidence pack item links to a downloadable OpenAPI/Swagger spec.

   • [probe] “PROBE openapi: all candidate paths 404 (https://inference-docs.cerebras.ai/openapi.json, https://inference-docs.cerebras.ai/swagger.json, ht…”

   proof ↗⚑ flag

3. ai-native userTest against a sandbox environment without touching production data

   weight 1 · round to Cerebras Inference
   Together AI—none0/10

   No evidence of a dedicated sandbox/staging environment; the only related mention is a test account with trial credit on the same production API, not an isolated sandbox for testing without touching production data.

   • [community] “the price is pretty low... 4B MODEL, PRICE 1K TOKENS: $0.0001. register with an email, test account has $25 credit, python API as well, good…”

   proof ↗⚑ flag

   Cerebras Inference~partialclaimed4/10

   Cerebras offers a no-key Cloud Console playground and $5 free credits to 'prototype prompts, agents, and real-time apps before you spend a dollar,' which lets a user experiment without hitting a paid/production billing tier, but there is no dedicated 'sandbox' API mode, test keys, or explicit separation from production data/environment documented. missing for 10: an explicit sandbox/test-mode endpoint or key type, documentation guaranteeing isolation from production data, and independent confirmation that free-tier usage never touches the same infra as production workloads.

   • [claimed-docs] “Use the playground in the Cloud Console — no key or install needed.”
   • [claimed-docs] “Get started with $5 in free credits after making an account”
   • [claimed-docs] “Get started with $5 in free credit after creating an account. Prototype prompts, agents, and real-time apps before you spend a dollar.”

   proof ↗⚑ flag

4. ai-native userRely on versioned APIs with a documented deprecation policy

   weight 2 · round drawn
   Together AI—none0/10

   The evidence shows a documented, OpenAI-compatible REST API (with a /v1/ endpoint) but contains no mention of a versioning scheme or a documented deprecation policy for API changes. Absence of evidence for this applicable capability means it cannot be credited as delivered.

   ⚑ flag

   Cerebras Inference—none0/10

   No evidence of API versioning scheme or a documented deprecation policy; docs cover OpenAI-compatible endpoints, streaming, tool use, etc., but nothing on version lifecycle or deprecation timelines. The openapi.json probe even returned 404s, and no changelog or deprecation notice pages are cited.

   • [probe] “PROBE openapi: all candidate paths 404 (https://inference-docs.cerebras.ai/openapi.json, https://inference-docs.cerebras.ai/swagger.json, ht…”
   • [claimed-docs] “Existing applications can use Cerebras by changing the API key, base URL, and model ID.”
   • [claimed-docs] “OpenAI API compatibility lets developers build on Cerebras with just two code changes.”

   proof ↗⚑ flag

1. ai-native userPerform bulk operations across many items at once

   weight 2 · round drawn
   Together AI✓fullclaimed7/10

   Together AI offers an asynchronous Batch API explicitly designed for bulk workloads across many items at once, at up to 50% lower cost than synchronous calls, alongside dedicated endpoints and fine-tuning pipelines that can process large datasets end-to-end. missing for 10: no documented batch size/throughput limits, no independent/hands-on report validating batch job behavior at scale, and no detail on input format flexibility for large item counts.

   • [claimed-docs] “Run asynchronous batch workloads at up to 50% lower cost.”
   • [claimed-docs] “Together AI handles the full lifecycle: data upload, training, hosting, and inference on a dedicated endpoint.”
   • [claimed-docs] “Fine-tuning tailors a pretrained model to a smaller, targeted dataset so it performs better on a specific task or domain. Together AI handle…”

   proof ↗⚑ flag

   Cerebras Inference✓fullclaimed7/10

   Cerebras offers a documented Batch API for processing groups of requests asynchronously, directly supporting bulk operations across many items, and rate-limit/caching docs describe handling high-volume token throughput. missing for 10: no hands-on/community evidence validating batch API at scale, and no details on batch size limits or job management UX.

   • [claimed-docs] “The Batch API lets you process groups of requests asynchronously, making it perfect for workloads where you don't need immediate results”
   • [claimed-docs] “Cached tokens don't count toward your uncached TPM limit, so a higher cache hit rate lets you process far more total tokens within the same …”
   • [claimed-docs] “a higher cache hit rate lets you process far more total tokens within the same uncached limit”
   • [claimed-docs] “Improving your cache hit rate lets the same uncached limit serve significantly more total tokens”

   proof ↗⚑ flag

Batch

1. ml-engineerSubmit asynchronous batch inference jobs at a documented discount versus real-time pricing

   weight 2 · round to Together AI
   Together AI✓fullclaimed8/10

   Together AI has documented batch inference at up to 50% lower cost than real-time serverless pricing, directly matching the story's requirement for asynchronous jobs at a documented discount [together-ai-docs-3]. missing for 10: independent/hands-on corroboration of actual batch job submission and discount realization beyond the docs page, and more detail on submission workflow/API specifics.

   • [claimed-docs] “Run asynchronous batch workloads at up to 50% lower cost.”

   proof ↗⚑ flag

   Cerebras Inference~partialclaimed4/10

   Cerebras documents a Batch API for asynchronous, non-immediate processing of grouped requests (cerebras-docs-8), satisfying the async batch-submission part of the story. However, no evidence pack item documents a specific discounted price for batch jobs versus real-time pricing — the pricing pages only mention free credits, $10 self-serve tier, and rate-limit multipliers (cerebras-docs-13, cerebras-docs-14, cerebras-docs-16), not a batch discount. missing for 10: documented batch discount percentage or rate card, independent confirmation of batch pricing savings.

   • [claimed-docs] “The Batch API lets you process groups of requests asynchronously, making it perfect for workloads where you don't need immediate results”
   • [claimed-docs] “Get started with $5 in free credits after making an account”
   • [claimed-docs] “Self-serve payment starting at just $10 * 10x higher rate limits than free tier * Higher priority processing”
   • [claimed-docs] “Get access to Cerebras Inference through our partner APIs”

   proof ↗⚑ flag

Dedicated

1. ml-engineerDeploy a model on dedicated GPU capacity with autoscaling so my traffic is isolated from the shared serverless pool

   weight 2 · round to Together AI
   Together AI✓fullclaimed8/10

   Docs explicitly describe Dedicated Model Inference (DMI) for serving models on reserved/isolated hardware distinct from the shared serverless pool, with no hard rate limits, and a dedicated scaling doc showing configurable autoscaling via replica limits, plus deployment of fine-tuned or custom models via CLI. This directly matches the story's requirements of dedicated GPU capacity, autoscaling, and isolation from shared serverless traffic. Missing for 10: independent/hands-on verification of autoscaling behavior in production and concrete GPU-type/SLA details beyond docs.

   • [claimed-docs] “Dedicated model inference (DMI) lets you serve a model on reserved hardware, providing several advantages over serverless models”
   • [claimed-docs] “Configure your deployment to scale automatically by setting limits on how many replicas it can run.”
   • [claimed-docs] “Fine-tuned models: Deploy a model you fine-tuned from a supported base model.”
   • [claimed-docs] “tg beta endpoints deploy google/gemma-4-E4B-it --endpoint my-endpoint”
   • [claimed-docs] “Dedicated model inference (DMI) lets you serve a model on reserved hardware... Better performance... No hard rate limits”

   proof ↗⚑ flag

   Cerebras Inference~partialclaimed5/10

   Cerebras documents dedicated endpoints as private, reserved capacity isolated from the shared serverless pool, including support for custom fine-tuned models — directly matching the isolation requirement. However, there is no evidence of autoscaling on dedicated capacity; docs describe reserved/provisioned instances without any scaling mechanism mentioned. Missing for 10: explicit autoscaling capability, capacity/quota controls, and independent confirmation of dedicated-endpoint behavior in production.

   • [claimed-docs] “A dedicated endpoint is a private, provisioned instance of the Cerebras Inference service reserved exclusively for your organization.”
   • [claimed-docs] “Your endpoint runs on reserved capacity that is not shared with other customers, so your performance is never impacted by other workloads.”
   • [claimed-docs] “Deploy your custom fine-tuned models alongside standard model variants.”

   proof ↗⚑ flag

Fine tune

1. ml-engineerFine-tune a supported base model on my own data and serve the result on the same platform

   weight 2 · round to Together AI
   Together AI✓fullclaimed9/10

   Docs clearly describe fine-tuning (LoRA or full) on supported base models via console/API/CLI, with Together AI handling the full lifecycle including data upload, training, hosting, and inference on a dedicated endpoint, and dedicated endpoints explicitly support deploying fine-tuned models. This is a first-party documented end-to-end workflow with concrete CLI examples. Missing for 10: independent/hands-on confirmation from a third party that a fine-tune-then-serve workflow works as described.

   • [claimed-docs] “Fine-tune models on your own data and deploy them for inference.”
   • [claimed-docs] “LoRA: Trains a small set of adapter weights on top of the frozen base model... Full fine-tuning: Updates every weight in the base model.”
   • [claimed-docs] “You can launch a fine-tuning job from the console, through the API/SDK, or with the CLI”
   • [claimed-docs] “Fine-tuned models: Deploy a model you fine-tuned from a supported base model.”
   • [claimed-docs] “Together AI handles the full lifecycle: data upload, training, hosting, and inference on a dedicated endpoint.”
   • [claimed-docs] “Fine-tuning tailors a pretrained model to a smaller, targeted dataset so it performs better on a specific task or domain. Together AI handle…”

   proof ↗⚑ flag

   Cerebras Inference~partialclaimed4/10

   Cerebras dedicated endpoints explicitly support deploying custom fine-tuned models alongside standard variants on reserved capacity (cerebras-docs-9, cerebras-docs-10, cerebras-docs-24), covering the 'serve' half of the story. However, there is no evidence that Cerebras itself provides a fine-tuning service/API — the docs imply fine-tuning happens elsewhere and the resulting model is uploaded/deployed to a dedicated endpoint, not that the platform trains it. missing for 10: an actual fine-tuning API/pipeline on Cerebras, documentation of supported base models for tuning, and any hands-on confirmation of the full fine-tune-then-serve workflow.

   • [claimed-docs] “A dedicated endpoint is a private, provisioned instance of the Cerebras Inference service reserved exclusively for your organization.”
   • [claimed-docs] “Deploy your custom fine-tuned models alongside standard model variants.”
   • [claimed-docs] “Your endpoint runs on reserved capacity that is not shared with other customers, so your performance is never impacted by other workloads.”

   proof ↗⚑ flag

2. ml-engineerUpload and serve my own custom model weights or LoRA adapters

   weight 2 · round to Together AI
   Together AI~partialclaimed7/10

   Together AI's docs clearly support fine-tuning (LoRA or full) and deploying the resulting fine-tuned models to dedicated inference endpoints, covering the LoRA-adapter serving use case end-to-end (data upload, training, hosting, inference). However, there's no evidence of uploading externally-trained/custom weights not produced via Together's own fine-tuning pipeline (e.g., bringing your own checkpoint from another framework). missing for 10: evidence of uploading externally-trained custom weights/checkpoints (not produced by Together's fine-tuning), and independent/hands-on confirmation of this workflow.

   • [claimed-docs] “LoRA: Trains a small set of adapter weights on top of the frozen base model... Full fine-tuning: Updates every weight in the base model.”
   • [claimed-docs] “Fine-tuned models: Deploy a model you fine-tuned from a supported base model.”
   • [claimed-docs] “Together AI handles the full lifecycle: data upload, training, hosting, and inference on a dedicated endpoint.”
   • [claimed-docs] “Fine-tuning tailors a pretrained model to a smaller, targeted dataset so it performs better on a specific task or domain. Together AI handle…”
   • [claimed-docs] “You can launch a fine-tuning job from the console, through the API/SDK, or with the CLI”

   proof ↗⚑ flag

   Cerebras Inference~partialclaimed4/10

   Cerebras' dedicated endpoint docs state customers can 'deploy your custom fine-tuned models alongside standard model variants' on reserved capacity, which implies some path to serve custom fine-tuned weights — but this is only mentioned for the enterprise 'dedicated endpoint' tier, not the standard self-serve API, and there is no mention of LoRA adapter support, upload workflow, or self-serve model registration. Missing for 10: LoRA adapter upload/serving, self-serve (non-dedicated) custom weight upload process, independent/hands-on confirmation that custom fine-tuned models can actually be deployed.

   • [claimed-docs] “A dedicated endpoint is a private, provisioned instance of the Cerebras Inference service reserved exclusively for your organization.”
   • [claimed-docs] “Deploy your custom fine-tuned models alongside standard model variants.”
   • [claimed-docs] “Your endpoint runs on reserved capacity that is not shared with other customers, so your performance is never impacted by other workloads.”

   proof ↗⚑ flag

Catalog

1. ml-engineerGet newly released open-weight models on the platform quickly after their public release

   weight 2 · round to Cerebras Inference
   Together AI—none0/10

   The evidence pack shows Together AI hosts 100+ open-source models and supports fine-tuning/dedicated deployment, but there is no evidence—first-party or independent—about the speed or process of adding newly released open-weight models to the catalog. missing for 10: any documentation or community report on time-to-availability for new model releases, changelog/release-notes evidence, or comparison of catalog freshness versus competitors.

   ⚑ flag

   Cerebras Inference~partialcommunity3/10

   Community comments suggest Cerebras adds new open-weight models fairly often (e.g. hosting Qwen 3.8 27B, DFlash2 draft model) and docs show a model catalog exists, but there's no concrete evidence of turnaround time from a model's public release to availability on Cerebras, and one comment notes a newly released model (Qwen 3.8) wasn't yet available via a partner (OpenRouter), implying some lag. missing for 10: documented release-to-availability timelines, first-party announcements tying model launches to Cerebras availability, and independent confirmation of consistent fast onboarding of new open-weight models.

   • [community] “I used their Coding Plan for a few months. It is genuinely difficult to keep up with the models. The output is so fast. Qwen 3.8 27B is like…”
   • [community] “Noticed they are present in OpenRouter, but Qwen 3.8 is not there yet... the context size they allow for Qwen is just 128k. Still interestin…”
   • [community] “The DFlash2 draft model we're using was trained on a lot of code, so if you use it in a coding agent you'll probably notice it run a lot fas…”
   • [claimed-docs] “Find the right open-source model for your workload on Cerebras, including alternatives for Claude, GPT, and Gemini.”
   • [claimed-docs] “Browse all models available on Cerebras public endpoints.”

   proof ↗⚑ flag

2. developerRely on a documented deprecation policy with advance notice before a hosted model is removed

   weight 1 · round drawn
   Together AI—none0/10

   No evidence in the pack mentions a deprecation policy, sunset schedule, or advance-notice commitment for retiring hosted models; docs cover serverless/dedicated inference, fine-tuning, and agent tooling but nothing about model lifecycle/deprecation notices.

   ⚑ flag

   Cerebras Inference—none0/10

   No evidence pack item describes a deprecation policy, model retirement timeline, or advance-notice process for hosted models; documentation covers model selection but not lifecycle/deprecation.

   ⚑ flag

3. ai-native userHave an agent enumerate the live model catalog programmatically via a documented GET /v1/models-style endpoint

   weight 2 · round to Together AI
   Together AI✓fullprobed8/10

   Together AI exposes an OpenAI-compatible /v1/models endpoint confirmed live via probe (returns 401 without key, meaning it exists and speaks JSON, per together-ai-probe-rt-1), and docs confirm OpenAI-compatibility for base URL/API key swaps (together-ai-docs-1) enabling programmatic catalog enumeration. missing for 10: a dedicated docs page explicitly documenting the /v1/models endpoint schema/response format, and independent hands-on confirmation of a successful authenticated call listing the full catalog.

   • [probe] “PROBE models-endpoint (2026-09-04): GET https://api.together.xyz/v1/models without a key returned HTTP 401 (Missing API key) — the OpenAI-st…”
   • [claimed-docs] “you can point it at models hosted on Together with two changes: the API key and base URL”

   proof ↗⚑ flag

   Cerebras Inference~partialprobed7/10

   A live probe confirms `GET https://api.cerebras.ai/v1/models` is a real, JSON-speaking, OpenAI-style endpoint (403 unauthenticated, not 404), and docs repeatedly assert OpenAI API compatibility and a public models catalog page (cerebras-docs-3, -4, -21). However, no first-party doc page explicitly documents the /v1/models endpoint schema/response, nor is there an OpenAPI spec (all openapi.json paths 404). missing for 10: explicit documented endpoint reference/response schema for /v1/models, no OpenAPI spec confirmation, no independent hands-on report of enumerating the catalog via this endpoint.

   • [probe] “PROBE models-endpoint (2026-09-04): GET https://api.cerebras.ai/v1/models without a key returned HTTP 403 ({"detail":"Not authenticated"}) —…”
   • [claimed-docs] “Existing applications can use Cerebras by changing the API key, base URL, and model ID.”
   • [claimed-docs] “OpenAI API compatibility lets developers build on Cerebras with just two code changes.”
   • [claimed-docs] “Browse all models available on Cerebras public endpoints.”
   • [probe] “PROBE openapi: all candidate paths 404 (https://inference-docs.cerebras.ai/openapi.json, https://inference-docs.cerebras.ai/swagger.json, ht…”

   proof ↗⚑ flag

4. developerChoose among a broad catalog of current open-weight model families (Llama, Qwen, DeepSeek, GPT-OSS and peers) on shared serverless endpoints

   weight 3 · round to Together AI
   Together AI✓fullprobed7/10

   Docs confirm serverless endpoints serving '100+ open-source models' with per-token pricing, no provisioning latency, and OpenAI-compatible API for easy model switching, and community evidence corroborates real-world use of Llama-family models on the platform. However, the evidence pack never explicitly enumerates Qwen, DeepSeek, or GPT-OSS by name, so full breadth across all cited model families isn't directly confirmed. Missing for 10: explicit documentation naming Qwen/DeepSeek/GPT-OSS as catalog entries, and independent verification of catalog breadth beyond the generic '100+ models' claim.

   • [claimed-docs] “Call 100+ open-source models with per-token pricing and no provisioning latency.”
   • [claimed-docs] “you can point it at models hosted on Together with two changes: the API key and base URL”
   • [claimed-docs] “you can prototype on serverless and move to reserved hardware later without changing your application code”
   • [community] “Colleague and I were discussing need for a service like this. Their pricing is much cheaper than self-hosting Llama-2-70B on AWS ($20.36/hr)…”
   • [probe] “PROBE models-endpoint (2026-09-04): GET https://api.together.xyz/v1/models without a key returned HTTP 401 (Missing API key) — the OpenAI-st…”

   proof ↗⚑ flag

   Cerebras Inference~partialcommunity7/10

   Cerebras docs confirm a public model catalog with guidance to pick the right model and a models/overview page for browsing all available public endpoints, and community reports confirm live usage of Llama 3.1 70B and Qwen models on shared endpoints. However, no evidence explicitly names DeepSeek or GPT-OSS in the catalog, and one community note flags a context-size limitation (128k) for at least one hosted model, suggesting the catalog's breadth/parity with 'peers' isn't fully documented. missing for 10: explicit confirmation of DeepSeek and GPT-OSS availability, and a full enumerated model list showing parity across all cited families.

   • [claimed-docs] “Use this guide to find the right model for your use case on Cerebras.”
   • [claimed-docs] “Find the right open-source model for your workload on Cerebras, including alternatives for Claude, GPT, and Gemini.”
   • [claimed-docs] “Browse all models available on Cerebras public endpoints.”
   • [community] “I used their Coding Plan for a few months. It is genuinely difficult to keep up with the models. The output is so fast. Qwen 3.8 27B is like…”
   • [community] “Noticed they are present in OpenRouter, but Qwen 3.8 is not there yet... the context size they allow for Qwen is just 128k. Still interestin…”
   • [community] “This is astonishingly fast. I'm struggling to get over 100 tok/s on my own Llama 3.1 70b implementation on an 8x H100 cluster.”

   proof ↗⚑ flag

Modalities

1. developerGenerate embeddings (and rerank results) for retrieval pipelines without a second vendor

   weight 1 · round to Together AI
   Together AI~partialclaimed5/10

   Docs confirm a unified API that includes embeddings alongside chat/image/audio (together-ai-docs-21), supporting retrieval pipelines without a separate embeddings vendor. However, no evidence in the pack documents a dedicated rerank endpoint or reranking models, so the reranking half of the story is unsupported. Missing for 10: dedicated rerank API/model documentation, embeddings model list/dimensions, and independent confirmation of embeddings quality/usage in RAG pipelines.

   • [claimed-docs] “Call chat, image, audio, embedding, and more through one API.”

   proof ↗⚑ flag

   Cerebras Inference—none0/10

   No evidence of embeddings or reranking models/endpoints anywhere in the docs, SDKs, or model catalog — Cerebras Inference documentation focuses solely on chat/completions, tool use, structured outputs, and streaming for LLMs. No mention of an embeddings API or reranking capability.

   ⚑ flag

2. developerCall vision, audio, or image-generation models beyond text chat on the same platform

   weight 1 · round to Together AI
   Together AI✓fullclaimed8/10

   Together AI's docs explicitly state you can 'Call chat, image, audio, embedding, and more through one API' on the serverless platform, indicating multimodal model support beyond text chat on the same platform [together-ai-docs-21]. This is corroborated by the broader serverless/dedicated inference docs describing a unified API/base URL approach [together-ai-docs-1, together-ai-docs-2]. Missing for 10: no independent hands-on report or example code specifically calling vision/audio/image-generation endpoints, and no detailed model list confirming breadth of non-text modalities.

   • [claimed-docs] “Call chat, image, audio, embedding, and more through one API.”
   • [claimed-docs] “Call 100+ open-source models with per-token pricing and no provisioning latency.”
   • [claimed-docs] “you can point it at models hosted on Together with two changes: the API key and base URL”

   proof ↗⚑ flag

   Cerebras Inference~partialclaimed3/10

   Docs show only a narrow vision capability (base64 image_url input support for chat completions) but no evidence of dedicated vision, audio, or image-generation models being served on the platform, nor documentation of separate multimodal model endpoints. missing for 10: audio input/output model support, image-generation model support, explicit vision-model catalog entries, and any hands-on/community confirmation of using multimodal (non-text) capabilities.

   • [claimed-docs] “The standard OpenAI `image_url` content shape is supported. Supply the image as a base64 data URI”
   • [claimed-docs] “Browse all models available on Cerebras public endpoints.”
   • [claimed-docs] “Find the right open-source model for your workload on Cerebras, including alternatives for Claude, GPT, and Gemini.”

   proof ↗⚑ flag

Compat

1. ai-native userPlug the provider into coding agents and agent frameworks through documented, first-party integration guides

   weight 2 · round to Together AI
   Together AI✓fullprobed9/10

   Together AI documents first-party agent-skills and an MCP server specifically for coding agent integration (docs-agent-skills, docs-12/19/20/24/29/30), confirmed live via probe (probe-4, probe-rt-3), plus OpenAI-compatible base URL swap for framework compatibility (docs-1) and function/tool-calling docs for agentic workflows (docs-8/9). Missing for 10: independent hands-on reports specifically validating the agent-skills/MCP integration with popular frameworks like LangChain or Claude Code.

   • [claimed-docs] “you can point it at models hosted on Together with two changes: the API key and base URL”
   • [claimed-docs] “Make your AI coding agent Together-AI-aware with ready-made skills for code generation and an MCP server for live docs lookup.”
   • [claimed-docs] “12 domain-specific skills that load on demand and teach your agent how to write correct Together AI code (right model IDs, SDK patterns, bes…”
   • [claimed-docs] “Gives your agent live access to this documentation site so it can look up current information without leaving your editor.”
   • [claimed-docs] “Docs MCP server: Gives your agent live access to this documentation site so it can look up current information without leaving your editor.”
   • [claimed-docs] “Function calling (also called tool calling) lets LLMs respond with structured function names and arguments that you can execute in your appl…”
   • [claimed-docs] “To build agent loops, chain tool calls inside one response (multi-step), and conversations that thread tools across many turns (multi-turn).”
   • [probe] “official MCP server documented at https://docs.together.ai/docs/agent-skills”
   • [probe] “PROBE mcp-endpoint (2026-09-04): POST initialize to https://docs.together.ai/mcp answered HTTP 200 with a JSON-RPC/MCP response (event: mess…”

   proof ↗⚑ flag

   Cerebras Inference~partialcommunity6/10

   Cerebras publishes first-party OpenAI-compatibility docs and SDKs (cerebras-docs-3, cerebras-docs-4, cerebras-gh-1/2) that let any OpenAI-compatible coding agent or framework plug in by swapping API key/base URL, and community reports confirm 'official support' for tools like Cursor (cerebras-comm-12). However there is no dedicated first-party guide for specific agent frameworks (e.g., LangChain, Cursor, Claude Code) beyond generic OpenAI-compat instructions, and one report shows friction integrating with claude-code-router (422 wrong_api_format, cerebras-comm-11). Missing for 10: explicit named integration guides/tutorials for popular coding agents or agent frameworks, and confirmation that such integrations work smoothly end-to-end.

   • [claimed-docs] “Existing applications can use Cerebras by changing the API key, base URL, and model ID.”
   • [claimed-docs] “OpenAI API compatibility lets developers build on Cerebras with just two code changes.”
   • [github] “This SDK has a mechanism that sends a few requests to `/v1/tcp_warming` upon construction to reduce the TTFT.”
   • [github] “This library provides convenient access to the Cerebras REST API from server-side TypeScript or JavaScript.”
   • [community] “I've been waiting on this for a LONG time. Integration with Cursor when Cerebras released their earlier models was patchy at best, even thro…”
   • [community] “It hits the request per minute limit instantly and then you wait a minute. (API Error: 422 ... wrong_api_format when integrating with claude…”

   proof ↗⚑ flag

2. ai-native userHave an agent switch to or away from this provider mid-workflow because it speaks the standard chat-completions API without provider-specific code changes

   weight 3 · round to Together AI
   Together AI✓fullprobed8/10

   Docs explicitly state OpenAI-compatibility requires only changing API key and base URL, meaning agents/tools written against the standard chat-completions API can point at Together with no provider-specific code changes; the live /v1/models endpoint confirms OpenAI-style REST shape. This directly supports switching to/away from Together mid-workflow with minimal friction. Missing for 10: independent third-party confirmation of drop-in swap behavior (e.g., a framework's changelog showing zero-code-change provider swap) and explicit mention of streaming/tool-call parity with OpenAI's spec beyond docs.

   • [claimed-docs] “you can point it at models hosted on Together with two changes: the API key and base URL”
   • [claimed-docs] “you can prototype on serverless and move to reserved hardware later without changing your application code”
   • [probe] “PROBE models-endpoint (2026-09-04): GET https://api.together.xyz/v1/models without a key returned HTTP 401 (Missing API key) — the OpenAI-st…”

   proof ↗⚑ flag

   Cerebras Inference~partialcommunity7/10

   Cerebras explicitly documents OpenAI-compatible chat completions requiring only base URL/API key/model swap ('two code changes'), and supports streaming, tool calling, image_url content, and structured outputs matching OpenAI's API shape (cerebras-docs-3, cerebras-docs-4, cerebras-docs-15, cerebras-docs-23). However, real-world integration reports show friction: a user hit a 'wrong_api_format' 422 error integrating with claude-code-router (cerebras-comm-11), and Cursor integration was described as 'patchy' before official support was added (cerebras-comm-12), suggesting the compatibility layer isn't always frictionless in practice. Missing for 10: independent verification of drop-in compatibility across multiple agent frameworks without errors, and no OpenAPI spec is publicly served (cerebras-probe-3) to confirm exact schema parity.

   • [claimed-docs] “Existing applications can use Cerebras by changing the API key, base URL, and model ID.”
   • [claimed-docs] “OpenAI API compatibility lets developers build on Cerebras with just two code changes.”
   • [claimed-docs] “The standard OpenAI `image_url` content shape is supported. Supply the image as a base64 data URI”
   • [claimed-docs] “OpenAI API compatibility lets developers build on Cerebras with just two code changes.​”
   • [community] “It hits the request per minute limit instantly and then you wait a minute. (API Error: 422 ... wrong_api_format when integrating with claude…”
   • [community] “I've been waiting on this for a LONG time. Integration with Cursor when Cerebras released their earlier models was patchy at best, even thro…”

   proof ↗⚑ flag

3. developerPoint an existing OpenAI SDK client at the provider by changing only the base URL and API key

   weight 3 · round to Cerebras Inference
   Together AI✓fullprobed8/10

   Docs explicitly state you can point an existing client at Together by changing only the API key and base URL, and the API is OpenAI-compatible with a live /v1/models endpoint confirmed via probe. missing for 10: independent developer testimonial specifically confirming drop-in OpenAI SDK compatibility (community evidence covers pricing/UX but not this specific mechanic), and no explicit code snippet shown in evidence.

   • [claimed-docs] “you can point it at models hosted on Together with two changes: the API key and base URL”
   • [probe] “PROBE models-endpoint (2026-09-04): GET https://api.together.xyz/v1/models without a key returned HTTP 401 (Missing API key) — the OpenAI-st…”
   • [claimed-docs] “you can prototype on serverless and move to reserved hardware later without changing your application code”

   proof ↗⚑ flag

   Cerebras Inference✓fullprobed9/10

   Official docs explicitly state existing OpenAI SDK apps can switch to Cerebras by changing only the API key, base URL, and model ID, and marketing reiterates 'just two code changes'; a live probe confirms the API serves an OpenAI-style /v1/models endpoint. Community reports (e.g. Cursor/OpenRouter integrations) corroborate real-world drop-in usage. Missing for 10: independent hands-on confirmation of a literal SDK base_url swap with zero other code changes.

   • [claimed-docs] “Existing applications can use Cerebras by changing the API key, base URL, and model ID.”
   • [claimed-docs] “OpenAI API compatibility lets developers build on Cerebras with just two code changes.”
   • [claimed-docs] “OpenAI API compatibility lets developers build on Cerebras with just two code changes.​”
   • [probe] “PROBE models-endpoint (2026-09-04): GET https://api.cerebras.ai/v1/models without a key returned HTTP 403 ({"detail":"Not authenticated"}) —…”
   • [community] “I've been waiting on this for a LONG time. Integration with Cursor when Cerebras released their earlier models was patchy at best, even thro…”

   proof ↗⚑ flag

1. ai-native userDo everything through the API that I can do in the UI

   weight 2 · round to Together AI
   Together AI~partialprobed7/10

   Docs show broad API/CLI/SDK coverage mirroring UI actions—fine-tuning can be launched 'from the console, through the API/SDK, or with the CLI', dedicated endpoints can be deployed via CLI (tg beta endpoints deploy), and inference, batch, and cluster provisioning are all API-accessible—suggesting strong UI/API parity for core workflows. However, there's no explicit documentation enumerating full parity (e.g., billing, account/org management, usage dashboards) or confirming every UI-only feature has an API equivalent, and no OpenAPI spec was found (probe shows all openapi.json paths 404), making completeness hard to verify. Missing for 10: an explicit parity statement or API coverage for account/billing/admin functions, and a published OpenAPI spec confirming full API surface.

   • [claimed-docs] “You can launch a fine-tuning job from the console, through the API/SDK, or with the CLI”
   • [claimed-docs] “tg beta endpoints deploy google/gemma-4-E4B-it --endpoint my-endpoint”
   • [claimed-docs] “Together AI handles the full lifecycle: data upload, training, hosting, and inference on a dedicated endpoint.”
   • [claimed-docs] “Spin up H100 and B200 clusters with attached storage for training or large batch jobs.”
   • [claimed-docs] “Run asynchronous batch workloads at up to 50% lower cost.”
   • [probe] “PROBE openapi: all candidate paths 404 (https://docs.together.ai/openapi.json, https://docs.together.ai/swagger.json, https://docs.together.…”

   proof ↗⚑ flag

   Cerebras Inference~partialcommunity6/10

   Cerebras's API surface is extensive and well-documented (chat completions, streaming, tool calling, structured outputs, batch API, dedicated endpoints, model catalog) and the console playground is explicitly described as just a convenience UI on top of the same API, suggesting strong API/UI parity. However there's no explicit statement or independent verification that every console feature (e.g., dedicated-endpoint provisioning, billing/account management, fine-tuned model deployment) is fully API-driven rather than requiring console/sales interaction, and community reports mention waitlists and manual onboarding steps outside the API. Missing for 10: explicit parity documentation, evidence that account/billing/deployment actions are API-accessible, and independent confirmation of full UI-to-API feature parity.

   • [claimed-docs] “Use the playground in the Cloud Console — no key or install needed.”
   • [claimed-docs] “Existing applications can use Cerebras by changing the API key, base URL, and model ID.”
   • [claimed-docs] “OpenAI API compatibility lets developers build on Cerebras with just two code changes.”
   • [claimed-docs] “A dedicated endpoint is a private, provisioned instance of the Cerebras Inference service reserved exclusively for your organization.”
   • [claimed-docs] “Deploy your custom fine-tuned models alongside standard model variants.”
   • [claimed-docs] “Your endpoint runs on reserved capacity that is not shared with other customers, so your performance is never impacted by other workloads.”
   • [community] “They have a waitlist for trying their API. You have to be a bit skeptical when a company makes claims but does not offer their services to b…”
   • [community] “Very interested in playing with their hardware and cloud. Also I wonder if it's possible to try cloud without contacting their sales.”

   proof ↗⚑ flag

2. ai-native userExport all of my data in open formats and leave

   weight 3 · round drawn
   Together AI—none0/10

   The evidence pack covers inference, fine-tuning, dedicated endpoints, and agent tooling, but contains no mention of a data export feature, downloadable account data, or a documented way to retrieve fine-tuning datasets/model weights in open formats and leave the platform. This is a fair question for a hosted AI platform that stores prompts, fine-tuning data, and trained models, so absence of evidence means 'none' rather than 'na'.

   ⚑ flag

   Cerebras Inference—none0/10

   No evidence describes any data-export mechanism (usage logs, fine-tuned model weights, account data) in open formats; the closest analog—OpenAI API compatibility—only covers code portability for switching inference providers, not actual data export/exit tooling. Missing for 10: any documented data export feature, format, or exit/offboarding process.

   ⚑ flag

3. ai-native userRead the product's source under an open license

   weight 2 · round drawn
   Together AI—none0/10

   The evidence pack covers Together AI's hosted inference, fine-tuning, and dedicated endpoint features, and its client SDKs (e.g., together-typescript) are open on GitHub, but there is no evidence that Together AI's core platform/product source is released under an open license—only that it serves open-source models to customers.

   ⚑ flag

   Cerebras Inference—none0/10

   Cerebras Inference is a closed, hosted API service; the evidence pack shows only client SDKs (Node/Python) on GitHub, not the source of the inference service or model weights under an open license. There is no evidence the core product's source code is available for review.

   ⚑ flag

Limits

1. developerRead documented rate limits and how they scale across usage tiers before I hit them in production

   weight 2 · round drawn
   Together AI~partialclaimed6/10

   Together AI documents a rate-limit model explicitly (docs-14): dynamic per-organization, per-model limits based on live model capacity and recent successful usage, and notes dedicated endpoints have no hard rate limits (docs-31). This gives developers some visibility into how limits work before hitting them, but there's no documented tiered structure (e.g., spend-based tiers with specific numeric thresholds) showing how limits scale as usage/spend grows over time. Missing for 10: explicit usage-tier table or thresholds, numeric rate-limit values per tier, guidance on how to request/track tier upgrades.

   • [claimed-docs] “Together uses dynamic rate limits instead of fixed thresholds. Each organization has a dynamic rate per model that adjusts based on: The mod…”
   • [claimed-docs] “Dedicated model inference (DMI) lets you serve a model on reserved hardware... Better performance... No hard rate limits”

   proof ↗⚑ flag

   Cerebras Inference~partialcommunity6/10

   Cerebras has a dedicated rate-limits doc explaining TPM/RPM mechanics and how cache hit rate affects effective throughput (cerebras-docs-12/19/22), and the pricing page states tiers scale limits (e.g., 10x higher on the $10 self-serve tier vs free) (cerebras-docs-14/20/26). Community reports confirm rate limits are real and enforced in practice (cerebras-comm-11), corroborating the docs. However, the evidence never shows concrete numeric RPM/TPM values per tier or a full scaling table across all tiers (free, self-serve, dedicated), so a developer can't fully predict exact limits before hitting them. Missing for 10: explicit numeric rate-limit tables per tier, dedicated-endpoint tier limits, and independent confirmation that documented numbers match real-world enforcement.

   • [claimed-docs] “Cached tokens don't count toward your uncached TPM limit, so a higher cache hit rate lets you process far more total tokens within the same …”
   • [claimed-docs] “a higher cache hit rate lets you process far more total tokens within the same uncached limit”
   • [claimed-docs] “Improving your cache hit rate lets the same uncached limit serve significantly more total tokens”
   • [claimed-docs] “Self-serve payment starting at just $10 * 10x higher rate limits than free tier * Higher priority processing”
   • [claimed-docs] “Self-serve payment starting at just $10 ... 10x higher rate limits than free tier ... Higher priority processing”
   • [claimed-docs] “10x higher rate limits than free tier”
   • [community] “It hits the request per minute limit instantly and then you wait a minute. (API Error: 422 ... wrong_api_format when integrating with claude…”

   proof ↗⚑ flag

Pricing

1. founderSet spending caps or budget alerts so a runaway workload cannot generate an unbounded bill

   weight 1 · round drawn
   Together AI—none0/10

   No evidence of spending caps, budget alerts, or usage-based cutoffs; docs mention rate limits (dynamic, based on capacity/usage) and pricing structures but nothing about setting a spend ceiling or receiving alerts to prevent runaway bills.

   ⚑ flag

   Cerebras Inference—none0/10

   No evidence of spending caps, budget alerts, or usage-limit controls in Cerebras docs; only rate-limit tiers and free credit amounts are mentioned, not billing caps or alerts a founder could set to bound spend.

   • [claimed-docs] “Cached tokens don't count toward your uncached TPM limit, so a higher cache hit rate lets you process far more total tokens within the same …”
   • [claimed-docs] “Get started with $5 in free credits after making an account”
   • [claimed-docs] “Self-serve payment starting at just $10 * 10x higher rate limits than free tier * Higher priority processing”

   proof ↗⚑ flag

2. founderSee public per-token prices for every hosted model without talking to sales

   weight 3 · round to Together AI
   Together AI~partialprobed6/10

   Docs advertise "per-token pricing" for 100+ serverless models and community evidence confirms self-serve signup with visible per-token cost (e.g. "$0.0001/1K tokens", $25 trial credit) with no sales contact required. However, no evidence pack item directly shows a public pricing page enumerating rates for every hosted model, and the live models API requires an API key to enumerate the catalog. Missing for 10: a documented public pricing page/table covering all models, independent confirmation that all model prices (not just examples) are visible pre-signup.

   • [claimed-docs] “Call 100+ open-source models with per-token pricing and no provisioning latency.”
   • [community] “the price is pretty low... 4B MODEL, PRICE 1K TOKENS: $0.0001. register with an email, test account has $25 credit, python API as well, good…”
   • [community] “I've explored most of the inference as a service platforms, theirs is particularly nice. (Clear/simple UI, good pricing, good speed.)”
   • [probe] “PROBE models-endpoint (2026-09-04): GET https://api.together.xyz/v1/models without a key returned HTTP 401 (Missing API key) — the OpenAI-st…”

   proof ↗⚑ flag

   Cerebras Inference~partialcommunity4/10

   Cerebras publishes a public pricing page with self-serve signup, free credits, and tiered self-serve pricing ($10 minimum, 10x rate limits), showing pricing information is not gated behind a sales call for the basic tier (cerebras-docs-13,14,20,25,26). However, none of the evidence shows an actual published per-token $/M-token rate for each hosted model, and one pricing-related doc references access via 'partner APIs' plus a community comment explicitly wonders whether trying the cloud is possible without contacting sales, suggesting the full price list isn't clearly self-evident. Missing for 10: an explicit per-model per-token price table, confirmation that all hosted models (not just self-serve tiers) have listed rates, and independent corroboration that no sales contact is needed to see model-level pricing.

   • [claimed-docs] “Get started with $5 in free credits after making an account”
   • [claimed-docs] “Self-serve payment starting at just $10 * 10x higher rate limits than free tier * Higher priority processing”
   • [claimed-docs] “Self-serve payment starting at just $10 ... 10x higher rate limits than free tier ... Higher priority processing”
   • [claimed-docs] “Get started with $5 in free credit after creating an account. Prototype prompts, agents, and real-time apps before you spend a dollar.”
   • [claimed-docs] “Get access to Cerebras Inference through our partner APIs”
   • [community] “Very interested in playing with their hardware and cloud. Also I wonder if it's possible to try cloud without contacting their sales.”

   proof ↗⚑ flag

1. ai-native userChoose where my data is stored (region/residency)

   weight 2 · round drawn
   Together AI—none0/10

   No evidence in the pack mentions data residency, regional storage options, or geographic control over where data/models are hosted; only privacy policy criticism unrelated to residency is present. Missing for 10: any documentation of region selection, data residency guarantees, or geo-specific hosting options.

   ⚑ flag

   Cerebras Inference—none0/10

   No evidence pack item mentions data residency, regional storage options, or any control over where data is processed/stored; only dedicated endpoint (capacity isolation, not geography) and general API docs are present.

   ⚑ flag

2. ai-native userPrevent my data from being used to train AI models

   weight 3 · round drawn
   Together AI—none0/10

   No first-party documentation describes an opt-out, data-retention control, or 'no training on your data' policy; the only relevant evidence is a community report describing Together AI's privacy policy as broad/vague, allowing data use 'for other purposes,' which points toward the opposite of a training-opt-out guarantee.

   • [community] “Together AI's privacy policy collects broad personal data including prompts, IP addresses, and contact info, with vague language allowing us…”
   • [community] “Colleague and I were discussing need for a service like this. Their pricing is much cheaper than self-hosting Llama-2-70B on AWS ($20.36/hr)…”

   proof ↗⚑ flag

   Cerebras Inference—none0/10

   No evidence pack item addresses a data-privacy/training-opt-out policy, data retention terms, or zero-retention agreement for Cerebras Inference API usage; nothing confirms user data is excluded from model training.

   ⚑ flag

3. ai-native userControl data retention and deletion

   weight 2 · round drawn
   Together AI—none0/10

   No evidence pack item documents user-facing data retention or deletion controls (e.g., API/dashboard settings to delete stored prompts or data). The only relevant community evidence (together-ai-comm-1) actually raises concerns that Together AI's privacy policy allows broad data collection with vague retention/usage language, which is the opposite of demonstrating retention/deletion controls.

   • [community] “Together AI's privacy policy collects broad personal data including prompts, IP addresses, and contact info, with vague language allowing us…”

   proof ↗⚑ flag

   Cerebras Inference—none0/10

   No evidence pack item discusses data retention policies, deletion controls, or privacy/data-handling settings for Cerebras Inference; documentation focuses on API usage, performance, and pricing only.

   ⚑ flag

4. ai-native userOpt out of telemetry and usage tracking

   weight 2 · round drawn
   Together AI—none0/10

   No documentation or feature reference describes a telemetry/usage-tracking opt-out mechanism; the only related evidence is a community critique noting Together AI's privacy policy broadly collects prompts, IPs, and contact info with vague 'other purposes' language and no opt-out mentioned, reinforcing the absence of this capability.

   • [community] “Together AI's privacy policy collects broad personal data including prompts, IP addresses, and contact info, with vague language allowing us…”
   • [community] “Colleague and I were discussing need for a service like this. Their pricing is much cheaper than self-hosting Llama-2-70B on AWS ($20.36/hr)…”

   proof ↗⚑ flag

   Cerebras Inference—none0/10

   No evidence in the pack addresses telemetry, usage tracking, or opt-out privacy controls for Cerebras Inference; the docs focus on API usage, pricing, and performance, not data collection settings.

   ⚑ flag

Reliability

1. founderGet a stated availability SLA on paid or enterprise tiers

   weight 1 · round drawn
   Together AI—none0/10

   Evidence shows a public status page and various product docs, but nothing states an actual uptime/availability SLA percentage or enterprise contractual guarantee. Absence of evidence for this applicable capability yields none.

   • [probe] “PROBE status-page (2026-09-04): https://status.together.ai returns HTTP 200 and renders a public service-status page (page body includes "op…”

   proof ↗⚑ flag

   Cerebras Inference—none0/10

   Evidence shows a public status page and dedicated/reserved-capacity endpoints for enterprise customers, but nowhere in docs or pricing pages is an explicit uptime SLA percentage, credit policy, or contractual availability guarantee stated for paid or enterprise tiers. Community threads even highlight unpredictable rate-limiting and onboarding issues rather than confirming a formal SLA.

   • [claimed-docs] “A dedicated endpoint is a private, provisioned instance of the Cerebras Inference service reserved exclusively for your organization.”
   • [claimed-docs] “Your endpoint runs on reserved capacity that is not shared with other customers, so your performance is never impacted by other workloads.”
   • [probe] “PROBE status-page (2026-09-04): https://status.cerebras.ai returns HTTP 200 and renders a public service-status page (page body includes "op…”
   • [community] “It hits the request per minute limit instantly and then you wait a minute. (API Error: 422 ... wrong_api_format when integrating with claude…”

   proof ↗⚑ flag

2. founderCheck a public status page with incident history before betting production traffic on the platform

   weight 2 · round to Together AI
   Together AI✓fullprobed8/10

   A live public status page at status.together.ai returns HTTP 200 and shows service status ('operational'), confirming a real status dashboard exists that a founder could check before committing production traffic. Missing for 10: no evidence of detailed incident history/postmortems or uptime SLA history visible in the pack, so depth of incident history can't be fully confirmed.

   • [probe] “PROBE status-page (2026-09-04): https://status.together.ai returns HTTP 200 and renders a public service-status page (page body includes "op…”

   proof ↗⚑ flag

   Cerebras Inference~partialprobed6/10

   A live public status page (status.cerebras.ai) was confirmed via probe, returning HTTP 200 and showing an 'operational' status, which supports founders checking service health before committing production traffic. However, there's no evidence in the pack of a visible incident history log or historical uptime records on that page, and community threads note some real-world reliability hiccups (rate-limit bursts, onboarding scaling issues) without connecting them to the status page. Missing for 10: documented incident history/timeline on the status page, uptime SLA data, and independent confirmation that past incidents are publicly logged.

   • [probe] “PROBE status-page (2026-09-04): https://status.cerebras.ai returns HTTP 200 and renders a public service-status page (page body includes "op…”
   • [community] “apologies we just got a sudden burst of new users and traffic, it's scaling up now.”
   • [community] “It hits the request per minute limit instantly and then you wait a minute. (API Error: 422 ... wrong_api_format when integrating with claude…”

   proof ↗⚑ flag

Benchmarks

1. ml-engineerSee published tokens-per-second or latency numbers, benchmarks, or load-testing guides backing the provider's speed claims

   weight 2 · round to Cerebras Inference
   Together AI—none0/10

   The evidence pack contains only vague qualitative claims ('no provisioning latency', 'Better performance' for dedicated endpoints) and subjective community remarks ('good speed') but no published tokens-per-second figures, latency benchmarks, or load-testing methodology/guides that would let an ml-engineer verify speed claims.

   • [claimed-docs] “Call 100+ open-source models with per-token pricing and no provisioning latency.”
   • [claimed-docs] “Dedicated model inference (DMI) lets you serve a model on reserved hardware... Better performance... No hard rate limits”
   • [community] “I've explored most of the inference as a service platforms, theirs is particularly nice. (Clear/simple UI, good pricing, good speed.)”

   proof ↗⚑ flag

   Cerebras Inference~partialcommunity5/10

   There is no first-party benchmark page or load-testing guide in the evidence pack, but community reports repeatedly cite concrete tok/s figures (e.g., 'break 300 tok/s', comparisons showing Cerebras far outpacing 100 tok/s H100 clusters) and the SDK docs mention a TTFT-reduction mechanism, giving some quantitative backing for speed claims. Missing for 10: an official published benchmark/whitepaper with tokens-per-second numbers, a load-testing guide, or independent third-party benchmark reports (e.g., Artificial Analysis) directly cited in the pack.

   • [community] “The DFlash2 draft model we're using was trained on a lot of code, so if you use it in a coding agent you'll probably notice it run a lot fas…”
   • [community] “This is astonishingly fast. I'm struggling to get over 100 tok/s on my own Llama 3.1 70b implementation on an 8x H100 cluster.”
   • [community] “It generates code faster than I can inspect it. In other words, it's needlessly fast.”
   • [community] “It's insanely fast. Here's an AI voice assistant I built that uses it: cerebras.vercel.app”
   • [github] “This SDK has a mechanism that sends a few requests to `/v1/tcp_warming` upon construction to reduce the TTFT.”

   proof ↗⚑ flag

Serving

1. developerServe latency-sensitive workloads with fast time-to-first-token and high-throughput generation

   weight 3 · round to Cerebras Inference
   Together AI!disputedcontradicted5/10

   Together AI's docs claim strong speed characteristics — 'no provisioning latency' for serverless, DMI offering 'better performance' and 'no hard rate limits', dynamic rate limits based on live capacity, streaming SSE support, and automatic prompt caching to speed repeated prefills — all directly supporting fast TTFT and high throughput [together-ai-docs-2][together-ai-docs-31][together-ai-docs-14][together-ai-gh-1][together-ai-docs-13]. However, an independent hands-on comparison explicitly disputes this, stating Together AI was 'at the bottom of the pile' for large-scale deployment speed/pricing versus competitors like Cerebras, DeepInfra, and Hyperbolic [together-ai-comm-5], while another community post gives a positive but vaguer 'good speed' impression [together-ai-comm-4] — a genuine mixed/contradicted signal rather than mere skepticism. Missing for 10: independent benchmarks (TTFT/tokens-per-second numbers) confirming or refuting the vendor's latency claims, and resolution of the conflicting community reports.

   • [claimed-docs] “Call 100+ open-source models with per-token pricing and no provisioning latency.”
   • [claimed-docs] “Dedicated model inference (DMI) lets you serve a model on reserved hardware... Better performance... No hard rate limits”
   • [claimed-docs] “Together uses dynamic rate limits instead of fixed thresholds. Each organization has a dynamic rate per model that adjusts based on: The mod…”
   • [claimed-docs] “Automatic: There is no header, parameter, or account toggle to enable it. Send the same prompt prefix again and any portion that's still war…”
   • [github] “We provide support for streaming responses using Server Sent Events (SSE).”
   • [community] “I've explored most of the inference as a service platforms, theirs is particularly nice. (Clear/simple UI, good pricing, good speed.)”
   • [community] “'Together AI has quickly grown into the leading hosting platform for AI' - Lol. Definitely not. I went through pricing and large scale deplo…”

   proof ↗⚑ flag

   Cerebras Inference✓fullcommunity9/10

   Cerebras' whole value proposition centers on speed: dedicated wafer-scale inference, TTFT-optimized SDK warming (cerebras-gh-1), streaming API (cerebras-docs-5), dedicated non-shared capacity for consistent latency (cerebras-docs-24), and cache-hit optimizations for throughput (cerebras-docs-12/19/22). Independent hands-on community reports strongly corroborate extreme throughput/low-latency (300+ tok/s coding model, beating 8x H100 clusters, 'needlessly fast' code gen) across multiple HN threads (cerebras-comm-1,6,7,10,13,14,15). Missing for 10: no first-party published TTFT/tok-s benchmark numbers in this pack, and some community reports note rate-limit throttling and onboarding friction that slightly tempers the sustained-throughput story (cerebras-comm-11,4).

   • [github] “This SDK has a mechanism that sends a few requests to `/v1/tcp_warming` upon construction to reduce the TTFT.”
   • [claimed-docs] “The Cerebras API supports streaming responses, which send messages back in chunks and display them incrementally as the model generates them…”
   • [claimed-docs] “Your endpoint runs on reserved capacity that is not shared with other customers, so your performance is never impacted by other workloads.”
   • [claimed-docs] “Cached tokens don't count toward your uncached TPM limit, so a higher cache hit rate lets you process far more total tokens within the same …”
   • [claimed-docs] “a higher cache hit rate lets you process far more total tokens within the same uncached limit”
   • [claimed-docs] “Improving your cache hit rate lets the same uncached limit serve significantly more total tokens”
   • [community] “I used their Coding Plan for a few months. It is genuinely difficult to keep up with the models. The output is so fast. Qwen 3.8 27B is like…”
   • [community] “The DFlash2 draft model we're using was trained on a lot of code, so if you use it in a coding agent you'll probably notice it run a lot fas…”
   • [community] “This is astonishingly fast. I'm struggling to get over 100 tok/s on my own Llama 3.1 70b implementation on an 8x H100 cluster.”
   • [community] “It generates code faster than I can inspect it. In other words, it's needlessly fast.”
   • [community] “It's insanely fast. Here's an AI voice assistant I built that uses it: cerebras.vercel.app”
   • [community] “Ok that speed's fucking ridiculous are you kidding me?!?!?! I just tried the Chat trial wtf.”
   • [community] “Damn, that's some impressive speeds. At that rate it doesn't matter if the first try resulted in an unwanted answer, you'll be able to run o…”
   • [community] “It hits the request per minute limit instantly and then you wait a minute. (API Error: 422 ... wrong_api_format when integrating with claude…”

   proof ↗⚑ flag

2. ml-engineerBenefit from prompt/prefix caching that reduces latency or cost on repeated context

   weight 1 · round to Together AI
   Together AI✓fullclaimed8/10

   Together AI documents automatic prompt/prefix caching for serverless chat models, explicitly stating cached input tokens are billed at a steep discount with no toggle needed, directly reducing cost on repeated context. missing for 10: independent/hands-on benchmark confirming latency reduction, and clarity on which models/latency gains apply beyond the cost-discount framing.

   • [claimed-docs] “Automatic: There is no header, parameter, or account toggle to enable it. Send the same prompt prefix again and any portion that's still war…”
   • [claimed-docs] “Send the same prompt prefix again and any portion that's still warm in the shared cache is billed at the cached rate.”
   • [claimed-docs] “Select serverless chat models bill cached input tokens at a steep discount... Automatic: There is no header, parameter, or account toggle to…”

   proof ↗⚑ flag

   Cerebras Inference~partialclaimed6/10

   Docs confirm Cerebras supports prompt/prefix caching for rate-limit purposes—cached tokens don't count toward uncached TPM limits, letting engineers push more total throughput—implying reduced cost/latency impact on repeated context. However, there's no explicit documentation on cache TTL, hit-rate mechanics, pricing discount for cached tokens, or independent benchmarks showing actual latency/cost reduction from caching in practice. missing for 10: documented cache pricing/discount, cache TTL/eviction details, independent latency benchmarks demonstrating caching benefit.

   • [claimed-docs] “Cached tokens don't count toward your uncached TPM limit, so a higher cache hit rate lets you process far more total tokens within the same …”
   • [claimed-docs] “a higher cache hit rate lets you process far more total tokens within the same uncached limit”
   • [claimed-docs] “Improving your cache hit rate lets the same uncached limit serve significantly more total tokens”

   proof ↗⚑ flag

3. developerStream completions token by token over SSE for responsive user experiences

   weight 3 · round drawn
   Together AI✓fullclaimed8/10

   Together AI's SDKs explicitly document SSE-based streaming support ('We provide support for streaming responses using Server Sent Events (SSE)'), and the API is OpenAI-compatible, implying standard streaming chat completion endpoints. Missing for 10: no independent hands-on latency benchmark or explicit token-by-token example snippet in the evidence pack.

   • [github] “We provide support for streaming responses using Server Sent Events (SSE).”
   • [github] “This library provides convenient access to the Together REST API from server-side TypeScript or JavaScript.”
   • [claimed-docs] “you can point it at models hosted on Together with two changes: the API key and base URL”

   proof ↗⚑ flag

   Cerebras Inference✓fullcommunity8/10

   Official docs explicitly describe streaming responses that send messages back in chunks and display them incrementally as the model generates them, and the SDK/API are OpenAI-compatible so standard SSE streaming semantics apply; community reports independently corroborate extremely fast token generation experienced in real-time apps. missing for 10: explicit SSE protocol details/code sample and independent hands-on confirmation specifically of streaming (vs just raw speed).

   • [claimed-docs] “The Cerebras API supports streaming responses, which send messages back in chunks and display them incrementally as the model generates them…”
   • [claimed-docs] “Existing applications can use Cerebras by changing the API key, base URL, and model ID.”
   • [claimed-docs] “OpenAI API compatibility lets developers build on Cerebras with just two code changes.”
   • [community] “It's insanely fast. Here's an AI voice assistant I built that uses it: cerebras.vercel.app”
   • [community] “This is astonishingly fast. I'm struggling to get over 100 tok/s on my own Llama 3.1 70b implementation on an 8x H100 cluster.”

   proof ↗⚑ flag

Structured

1. developerEnforce structured outputs against a JSON schema (or grammar) so model responses parse reliably

   weight 3 · round to Together AI
   Together AI✓fullclaimed8/10

   Together AI's docs explicitly describe structured outputs where supported models return JSON conforming to a supplied schema, avoiding retries/fragile parsing, alongside function/tool calling and multi-step agentic tool call chaining. This directly matches the story's request for JSON-schema-enforced structured outputs; missing for 10: no independent/hands-on validation of schema-conformance reliability or grammar-based constraints beyond JSON schema.

   • [claimed-docs] “Supported models can return JSON that conforms to any schema you supply, so you can read the output directly in code without retries or frag…”
   • [claimed-docs] “Function calling (also called tool calling) lets LLMs respond with structured function names and arguments that you can execute in your appl…”
   • [claimed-docs] “To build agent loops, chain tool calls inside one response (multi-step), and conversations that thread tools across many turns (multi-turn).”
   • [claimed-docs] “Multi-step function calling chains sequential function calls within one conversation turn.”

   proof ↗⚑ flag

   Cerebras Inference✓fullclaimed7/10

   Cerebras docs explicitly describe a Structured Outputs feature that constrains model responses to a JSON schema for reliable parsing, alongside OpenAI-API compatibility that typically carries this through standard SDKs. Missing for 10: independent/hands-on confirmation of schema-enforcement reliability, details on grammar-based constraints beyond JSON schema, and coverage of edge cases (nested schemas, strict mode) in evidence.

   • [claimed-docs] “Structured Outputs constrains model responses to a JSON schema so applications can process generated data reliably.”
   • [claimed-docs] “Existing applications can use Cerebras by changing the API key, base URL, and model ID.”
   • [claimed-docs] “OpenAI API compatibility lets developers build on Cerebras with just two code changes.”

   proof ↗⚑ flag

Tools

1. ai-native userRely on faithful function/tool calling — including parallel and multi-step tool use — so agent loops run on open models without breaking

   weight 3 · round to Together AI
   Together AI~partialclaimed6/10

   Together AI documents native function/tool calling with explicit support for multi-step (chained) and multi-turn agentic tool use, positioned as the foundation for agent loops (together-ai-docs-8,9,15,16). However, there is no independent/hands-on evidence confirming reliability of parallel tool calls or that it avoids breakage across open models in practice — only first-party docs. missing for 10: independent benchmarks or community reports on parallel tool-call accuracy, explicit parallel tool-calling documentation, and hands-on verification that agent loops don't break on open models.

   • [claimed-docs] “Function calling (also called tool calling) lets LLMs respond with structured function names and arguments that you can execute in your appl…”
   • [claimed-docs] “To build agent loops, chain tool calls inside one response (multi-step), and conversations that thread tools across many turns (multi-turn).”
   • [claimed-docs] “Function calling (also called *tool calling*) lets LLMs respond with structured function names and arguments that you can execute in your ap…”
   • [claimed-docs] “Multi-step function calling chains sequential function calls within one conversation turn.”

   proof ↗⚑ flag

   Cerebras Inference~partialcommunity5/10

   Cerebras documents a tool-calling/function-calling capability (cerebras-docs-7) as part of its OpenAI-compatible API, and community reports mention using it for coding agents (cerebras-comm-6, cerebras-comm-12) suggesting real agentic integrations exist. However, the docs pack contains no detail on parallel or multi-step tool-call handling, and one user reports API format errors when integrating with an agent router (cerebras-comm-11), hinting at possible friction in tool-use compatibility. missing for 10: explicit documentation of parallel tool calls, multi-step tool-call chaining, and independent benchmarks confirming reliability of tool-calling in long agent loops.

   • [claimed-docs] “Tool calling, also known as tool use or function calling, lets a model request functions that your application defines.”
   • [community] “The DFlash2 draft model we're using was trained on a lot of code, so if you use it in a coding agent you'll probably notice it run a lot fas…”
   • [community] “I've been waiting on this for a LONG time. Integration with Cursor when Cerebras released their earlier models was patchy at best, even thro…”
   • [community] “It hits the request per minute limit instantly and then you wait a minute. (API Error: 422 ... wrong_api_format when integrating with claude…”

   proof ↗⚑ flag