Groq vs DeepInfra

Agent access

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

   weight 2 · round to Groq
   Groq✓fullprobed9/10

   Direct probe evidence confirms llms.txt is live (HTTP 200) at console.groq.com/llms.txt, and Groq also serves per-page .md docs (docs/overview.md) explicitly designed for agent consumption, indicating agent-oriented docs beyond just llms.txt. Missing for 10: no independent/community confirmation of an agent actually using llms.txt in practice.

   • [probe] “PROBE llms.txt: HTTP 200 at https://console.groq.com/llms.txt # https://console.groq.com llms.txt - [JigsawStack 🧩](https://console.groq.c…”
   • [probe] “PROBE docs-md: HTTP 200 at https://console.groq.com/docs/overview.md --- description: Fast LLM inference, OpenAI-compatible. Simple to integ…”

   proof ↗⚑ flag

   DeepInfra—none0/10

   Direct probes show llms.txt, docs.md, and openapi.json all return 404 on deepinfra.com, and no evidence pack item claims an agent-oriented docs format exists; the only machine-readable endpoint found is the models list, not documentation.

   • [probe] “PROBE llms.txt: HTTP 404 at https://deepinfra.com/llms.txt”
   • [probe] “PROBE docs-md: HTTP 404 at https://deepinfra.com/docs.md”
   • [probe] “PROBE openapi: all candidate paths 404 (https://deepinfra.com/openapi.json, https://deepinfra.com/swagger.json, https://deepinfra.com/api/op…”

   proof ↗⚑ flag

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

   weight 2 · round to Groq
   Groq✓fullprobed8/10

   Groq is a pure API/inference service accessed via OpenAI-compatible REST endpoints and SDKs (API key + base_url swap), with batch processing for large-scale asynchronous automation and confirmed live, key-gated JSON endpoints — all of which are inherently scriptable/headless with no GUI dependency, ideal for CI pipelines. Missing for 10: explicit CI/CD examples (e.g., GitHub Actions) or docs specifically framing headless/automation use cases beyond batch API references.

   • [claimed-docs] “pass your Groq API key to the `api_key` parameter and change the `base_url` to `https://api.groq.com/openai/v1`”
   • [claimed-docs] “Batch processing lets you run thousands of API requests at scale by submitting your workload as an asynchronous batch of requests to Groq wi…”
   • [claimed-docs] “Batch processing lets you run thousands of API requests at scale by submitting your workload as an asynchronous batch of requests to Groq wi…”
   • [github] “Request parameters that correspond to file uploads can be passed in many different forms”
   • [probe] “PROBE models-endpoint (2026-09-04): GET https://api.groq.com/openai/v1/models without a key returned HTTP 401 ({"error":{"message":"Invalid …”
   • [claimed-docs] “To enable streaming, set the parameter stream=True. The completion function will then return an iterator of completion deltas”

   proof ↗⚑ flag

   DeepInfra✓fullprobed7/10

   DeepInfra is a REST/OpenAI-compatible API (base_url + api_key model, keyless model enumeration) that is inherently scriptable and stateless, making it straightforward to call from CI/automation pipelines without any UI dependency. missing for 10: no explicit CI/CD examples (GitHub Actions, cron jobs), no official CLI, and no independent hands-on report of someone running it headlessly in a pipeline.

   • [claimed-docs] “The only changes you need to make from your existing OpenAI code: 1. Set `base_url` to `https://api.deepinfra.com/v1/openai` 2. Set `api_…”
   • [claimed-docs] “The only changes you need to make from your existing OpenAI code: Set base_url to https://api.deepinfra.com/v1/openai”
   • [probe] “PROBE models-endpoint (2026-09-04): GET https://api.deepinfra.com/v1/openai/models with NO API key returned HTTP 200 with a machine-readable…”
   • [claimed-docs] “You only pay per token — no idle GPU time, no minimums, no seat fees.”

   proof ↗⚑ flag

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

   weight 3 · round to Groq
   Groq✓fullprobed8/10

   Groq's Responses/Chat API supports remote MCP: you provide an MCP server URL and Groq handles tool orchestration without implementing tool logic yourself, documented explicitly in first-party docs and confirmed via a probe of the dedicated MCP docs page. Missing for 10: independent hands-on user reports specifically validating MCP tool use (community evidence covers speed/reliability but not MCP), and no details on multi-server or auth edge cases.

   • [claimed-docs] “point to an MCP server URL and the Groq API will start using its tools without you having to implement any tool logic yourself”
   • [claimed-docs] “you simply point to an MCP server URL and the Groq API will start using its tools without you having to implement any tool logic yourself”
   • [claimed-docs] “Groq's Responses API supports remote tool use via MCP servers via HTTPS where Groq handles all orchestration... You don't implement anything…”
   • [probe] “official MCP server documented at https://console.groq.com/docs/mcp”

   proof ↗⚑ flag

   DeepInfra—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 Groq
   Groq~partialprobed4/10

   There is a probe hit indicating an official MCP-server doc page exists (console.groq.com/docs/mcp), but the evidence pack contains no actual content, setup instructions, or independent confirmation of this server — the detailed MCP docs quoted (groq-docs-6/17/28) describe Groq's API acting as an MCP *client* consuming remote MCP servers, not Groq serving as an MCP server for other agents to connect to. Missing for 10: quoted documentation of the official MCP server's endpoint/connection instructions, evidence of an agent successfully connecting to Groq via that server, and independent/community confirmation it works as advertised.

   • [probe] “official MCP server documented at https://console.groq.com/docs/mcp”
   • [claimed-docs] “point to an MCP server URL and the Groq API will start using its tools without you having to implement any tool logic yourself”
   • [claimed-docs] “Groq's Responses API supports remote tool use via MCP servers via HTTPS where Groq handles all orchestration... You don't implement anything…”

   proof ↗⚑ flag

   DeepInfra—none0/10

   DeepInfra is an inference API/platform (not an agent), so an official MCP server is a fair axis to expect, but no evidence in the pack mentions MCP support at all—only OpenAI-compatible REST API docs.

   ⚑ flag

5. ai-native userUse an official CLI

   weight 2 · round drawn
   Groq—none0/10

   No evidence of an official Groq CLI tool; documentation covers SDKs (Python/TypeScript), REST API, and MCP integration but no CLI is mentioned anywhere in the evidence pack.

   ⚑ flag

   DeepInfra—none0/10

   No evidence of an official DeepInfra CLI tool; docs focus on REST/OpenAI-SDK compatibility, and probes for docs/openapi/llms.txt turned up nothing about a CLI. DeepInfra is an inference API/cloud platform, so an official CLI is a plausible and fair capability to ask about, but none is evidenced.

   • [claimed-docs] “Point your existing OpenAI SDK to `https://api.deepinfra.com/v1/openai` and your code works without changes. No migration required.”
   • [probe] “PROBE llms.txt: HTTP 404 at https://deepinfra.com/llms.txt”
   • [probe] “PROBE docs-md: HTTP 404 at https://deepinfra.com/docs.md”
   • [probe] “PROBE openapi: all candidate paths 404 (https://deepinfra.com/openapi.json, https://deepinfra.com/swagger.json, https://deepinfra.com/api/op…”

   proof ↗⚑ flag

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

   weight 3 · round to Groq
   Groq✓fullprobed9/10

   Groq ships an extensively documented, OpenAI-compatible REST API (chat completions, streaming, tool use, structured outputs, batch, service tiers, LoRA, Responses API) with a live confirmed endpoint (401 on missing key, not 404) and a public status page. Missing for 10: a discoverable formal OpenAPI/Swagger spec (all candidate paths 404).

   • [claimed-docs] “Fast LLM inference, OpenAI-compatible. Simple to integrate, easy to scale. Start building in minutes.”
   • [claimed-docs] “pass your Groq API key to the `api_key` parameter and change the `base_url` to `https://api.groq.com/openai/v1`”
   • [claimed-docs] “To start using Groq with OpenAI's client libraries, pass your Groq API key to the api_key parameter and change the base_url to https://api.g…”
   • [claimed-docs] “Generating text with Groq's Chat Completions API enables you to have natural, conversational interactions with Groq's large language models.”
   • [claimed-docs] “To use tools, the model must be provided with tool definitions. These tool definitions are in JSON schema format and are passed to the model…”
   • [claimed-docs] “Structured Outputs is a feature that ensures your model responses conform to your provided JSON Schema”
   • [claimed-docs] “Groq's Responses API is fully compatible with OpenAI's Responses API, making it easy to integrate advanced conversational AI capabilities in…”
   • [probe] “PROBE llms.txt: HTTP 200 at https://console.groq.com/llms.txt # https://console.groq.com llms.txt - [JigsawStack 🧩](https://console.groq.c…”
   • [probe] “PROBE docs-md: HTTP 200 at https://console.groq.com/docs/overview.md --- description: Fast LLM inference, OpenAI-compatible. Simple to integ…”
   • [probe] “PROBE openapi: all candidate paths 404 (https://console.groq.com/openapi.json, https://console.groq.com/swagger.json, https://console.groq.c…”
   • [probe] “PROBE models-endpoint (2026-09-04): GET https://api.groq.com/openai/v1/models without a key returned HTTP 401 ({"error":{"message":"Invalid …”
   • [probe] “PROBE status-page (2026-09-04): https://groqstatus.com returns HTTP 200 and renders a public service-status page (page body includes "operat…”

   proof ↗⚑ flag

   DeepInfra✓fullprobed8/10

   DeepInfra publishes extensive documentation for a fully OpenAI-compatible REST API (chat completions, embeddings, vision, image/video, speech, service tiers, reasoning_effort, fail_fast, etc.), confirmed live via a keyless probe that enumerated 189 models through the documented endpoint. Missing for 10: no discoverable OpenAPI/swagger spec or llms.txt for machine-readable agent consumption (all returned 404), so an AI agent must rely on human-oriented docs rather than a formal machine spec.

   • [claimed-docs] “Point your existing OpenAI SDK to `https://api.deepinfra.com/v1/openai` and your code works without changes. No migration required.”
   • [claimed-docs] “OpenAI-compatible chat completions API — just change the base URL and model name.”
   • [claimed-docs] “The only changes you need to make from your existing OpenAI code: 1. Set `base_url` to `https://api.deepinfra.com/v1/openai` 2. Set `api_…”
   • [claimed-docs] “The only changes you need to make from your existing OpenAI code: Set base_url to https://api.deepinfra.com/v1/openai”
   • [claimed-docs] “Set service_tier to "priority" to request priority inference on supported models. Priority requests get faster time-to-first-token and highe…”
   • [claimed-docs] “Set service_tier to "flex" to run Chat Completions requests at a lower cost in exchange for slower response times and occasional resource un…”
   • [claimed-docs] “Set the optional fail_fast parameter to true to get an immediate HTTP 429 instead of waiting.”
   • [probe] “PROBE models-endpoint (2026-09-04): GET https://api.deepinfra.com/v1/openai/models with NO API key returned HTTP 200 with a machine-readable…”
   • [probe] “PROBE llms.txt: HTTP 404 at https://deepinfra.com/llms.txt”
   • [probe] “PROBE openapi: all candidate paths 404 (https://deepinfra.com/openapi.json, https://deepinfra.com/swagger.json, https://deepinfra.com/api/op…”

   proof ↗⚑ flag

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

   weight 2 · round drawn
   Groq—none0/10

   Evidence covers billing spend limits and general API key auth (single api_key parameter) but shows no support for scoped/least-privilege credentials such as role-based keys, granular permission scopes, or per-agent restricted tokens; only one flat API key model is documented.

   • [claimed-docs] “pass your Groq API key to the `api_key` parameter and change the `base_url` to `https://api.groq.com/openai/v1`”
   • [claimed-docs] “To start using Groq with OpenAI's client libraries, pass your Groq API key to the api_key parameter and change the base_url to https://api.g…”
   • [claimed-docs] “Spend Limits: Set automated spending limits and receive budget alerts”
   • [claimed-docs] “Spend Limits:** Set automated spending limits and receive budget alerts”

   proof ↗⚑ flag

   DeepInfra—none0/10

   No evidence of scoped or least-privilege API key/credential management (e.g., per-agent tokens, permission scoping, role-based keys) — only a single generic API token model is implied via base_url/api_key usage. Missing for 10: any docs on creating multiple scoped keys, permission/role restrictions, credential expiration, or per-agent token issuance.

   • [claimed-docs] “The only changes you need to make from your existing OpenAI code: 1. Set `base_url` to `https://api.deepinfra.com/v1/openai` 2. Set `api_…”
   • [claimed-docs] “Drop-in OpenAI replacement. Point your existing OpenAI SDK to https://api.deepinfra.com/v1/openai and your code works without changes.”

   proof ↗⚑ flag

8. ai-native userBuild against official SDKs

   weight 2 · round drawn
   Groq✓fullclaimed8/10

   Groq maintains an official TypeScript SDK (groq-typescript) with concrete usage examples (audio transcription, file uploads) and extensive first-party docs covering chat completions, streaming, tool use, structured outputs, and OpenAI-compatible client usage, all of which are core to building against official SDKs. Missing for 10: explicit evidence of an official Python SDK repo (only OpenAI-compatible base_url swap is shown for Python) and independent/community validation specifically of SDK reliability rather than general API performance.

   • [github] “Request parameters that correspond to file uploads can be passed in many different forms”
   • [github] “await client.audio.transcriptions.create({ model: 'whisper-large-v3-turbo', file: fs.createReadStream('/path/to/file'), });”
   • [github] “If you have access to Node fs we recommend using fs.createReadStream()... Or if you have the web File API you can pass a File instance”
   • [claimed-docs] “pass your Groq API key to the `api_key` parameter and change the `base_url` to `https://api.groq.com/openai/v1`”
   • [claimed-docs] “To start using Groq with OpenAI's client libraries, pass your Groq API key to the api_key parameter and change the base_url to https://api.g…”
   • [claimed-docs] “To enable streaming, set the parameter stream=True. The completion function will then return an iterator of completion deltas”
   • [claimed-docs] “Tool use (or function calling) is what transforms a language model from a conversational interface into an autonomous agent capable of takin…”
   • [claimed-docs] “Structured Outputs is a feature that ensures your model responses conform to your provided JSON Schema”

   proof ↗⚑ flag

   DeepInfra✓fullprobed8/10

   DeepInfra explicitly documents OpenAI SDK compatibility as a drop-in replacement (just change base_url, api_key, model) with no code migration required, and a live probe confirms the OpenAI-compatible endpoint is functional and enumerable. This directly satisfies building against an official/well-known SDK ecosystem rather than a bespoke library. missing for 10: no dedicated first-party DeepInfra-branded SDK packages mentioned (relies on OpenAI SDK compatibility), and no independent third-party developer corroboration beyond docs and endpoint probes.

   • [claimed-docs] “Point your existing OpenAI SDK to `https://api.deepinfra.com/v1/openai` and your code works without changes. No migration required.”
   • [claimed-docs] “Point your existing OpenAI SDK to https://api.deepinfra.com/v1/openai and your code works without changes. No migration required.”
   • [claimed-docs] “The only changes you need to make from your existing OpenAI code: 1. Set `base_url` to `https://api.deepinfra.com/v1/openai` 2. Set `api_…”
   • [claimed-docs] “The only changes you need to make from your existing OpenAI code: Set base_url to https://api.deepinfra.com/v1/openai”
   • [claimed-docs] “Drop-in OpenAI replacement. Point your existing OpenAI SDK to https://api.deepinfra.com/v1/openai and your code works without changes.”
   • [probe] “PROBE models-endpoint (2026-09-04): GET https://api.deepinfra.com/v1/openai/models with NO API key returned HTTP 200 with a machine-readable…”

   proof ↗⚑ flag

9. ai-native userSubscribe to events via webhooks

   weight 2 · round drawn
   Groq—none0/10

   No evidence anywhere in the pack of a webhooks/event-subscription feature for Groq's API—only synchronous/streaming inference, batch, tool-use/MCP, and admin/billing docs are covered.

   ⚑ flag

   DeepInfra—none0/10

   No evidence of any webhook subscription or event-notification mechanism anywhere in the docs; DeepInfra is a request/response inference API with no mention of async event callbacks.

   ⚑ flag

Agentic features

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

   weight 2 · round drawn
   Groq—none0/10

   Groq's evidence covers only inference primitives (chat completions, tool use, MCP tool integration, batch processing, structured outputs) — none of which describe a scheduling/trigger system for autonomous background automations; batch jobs are asynchronous request batches, not persistent background agents. Missing for 10: any documented scheduler, cron/trigger mechanism, persistent background agent runtime, or workflow orchestration feature that lets a user 'set up' an automation to run unattended.

   • [claimed-docs] “Batch processing lets you run thousands of API requests at scale by submitting your workload as an asynchronous batch of requests to Groq wi…”
   • [claimed-docs] “Tool use (or function calling) is what transforms a language model from a conversational interface into an autonomous agent capable of takin…”
   • [claimed-docs] “point to an MCP server URL and the Groq API will start using its tools without you having to implement any tool logic yourself”

   proof ↗⚑ flag

   DeepInfra—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

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

   weight 3 · round drawn
   Groq—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

   DeepInfra—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 to Groq
   Groq~partialclaimed6/10

   Groq's Chat Completions/Responses API is inherently natural-language driven, and its tool-use and remote-MCP features let a model take autonomous actions from NL instructions, which supports agentic operation on top of the API. However, this is evidence of building NL-driven agents *with* Groq's models, not of an NL command interface for operating the Groq platform/product itself (e.g., managing keys, billing, deployments via chat). Missing for 10: an NL/chat interface for controlling the Groq platform itself, independent verification of agentic tool-use reliability in production.

   • [claimed-docs] “Generating text with Groq's Chat Completions API enables you to have natural, conversational interactions with Groq's large language models.”
   • [claimed-docs] “Tool use (or function calling) is what transforms a language model from a conversational interface into an autonomous agent capable of takin…”
   • [claimed-docs] “To use tools, the model must be provided with tool definitions. These tool definitions are in JSON schema format and are passed to the model…”
   • [claimed-docs] “point to an MCP server URL and the Groq API will start using its tools without you having to implement any tool logic yourself”
   • [claimed-docs] “Groq's Responses API supports remote tool use via MCP servers via HTTPS where Groq handles all orchestration... You don't implement anything…”

   proof ↗⚑ flag

   DeepInfra—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 Groq
   Groq~partialprobed4/10

   Groq's docs (console.groq.com/docs) are extensive and code-example-rich (curl/Python/JS snippets for chat, tool-use, structured outputs, batch, MCP, etc.), functioning as a de facto API reference, but there's no evidence of an interactive, in-browser 'runnable' playground or embedded code execution — probes show no discoverable OpenAPI/swagger spec (404s) and no mention of a try-it-now console feature. Missing for 10: an actual interactive/executable API reference (e.g., Swagger UI, embedded runnable code sandbox), confirmation of an OpenAPI spec, and independent user confirmation of using such an interactive reference.

   • [claimed-docs] “Fast LLM inference, OpenAI-compatible. Simple to integrate, easy to scale. Start building in minutes.”
   • [claimed-docs] “pass your Groq API key to the `api_key` parameter and change the `base_url` to `https://api.groq.com/openai/v1`”
   • [claimed-docs] “To enable streaming, set the parameter stream=True. The completion function will then return an iterator of completion deltas”
   • [probe] “PROBE docs-md: HTTP 200 at https://console.groq.com/docs/overview.md --- description: Fast LLM inference, OpenAI-compatible. Simple to integ…”
   • [probe] “PROBE openapi: all candidate paths 404 (https://console.groq.com/openapi.json, https://console.groq.com/swagger.json, https://console.groq.c…”

   proof ↗⚑ flag

   DeepInfra—none0/10

   The docs pack shows static code snippets (e.g., OpenAI SDK base_url examples) but no evidence of an interactive, runnable API console or playground; probes for openapi.json/swagger.json all returned 404, and no llms.txt/docs.md machine-readable spec exists, indicating no interactive reference is exposed.

   • [probe] “PROBE llms.txt: HTTP 404 at https://deepinfra.com/llms.txt”
   • [probe] “PROBE docs-md: HTTP 404 at https://deepinfra.com/docs.md”
   • [probe] “PROBE openapi: all candidate paths 404 (https://deepinfra.com/openapi.json, https://deepinfra.com/swagger.json, https://deepinfra.com/api/op…”
   • [claimed-docs] “The only changes you need to make from your existing OpenAI code: 1. Set `base_url` to `https://api.deepinfra.com/v1/openai` 2. Set `api_…”

   proof ↗⚑ flag

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

   weight 2 · round drawn
   Groq—none0/10

   Groq's API is OpenAI-compatible and well documented, but there is no evidence of a downloadable OpenAPI/Swagger spec; a direct probe of common spec paths (openapi.json, swagger.json, etc.) all returned 404, and no docs page links to a machine-readable spec.

   • [probe] “PROBE openapi: all candidate paths 404 (https://console.groq.com/openapi.json, https://console.groq.com/swagger.json, https://console.groq.c…”

   proof ↗⚑ flag

   DeepInfra—none0/10

   Direct probes for llms.txt, docs.md, and common OpenAPI/swagger spec paths all returned 404, indicating no machine-readable API spec is published; the docs only describe OpenAI SDK compatibility, not a downloadable OpenAPI/JSON schema.

   • [probe] “PROBE llms.txt: HTTP 404 at https://deepinfra.com/llms.txt”
   • [probe] “PROBE docs-md: HTTP 404 at https://deepinfra.com/docs.md”
   • [probe] “PROBE openapi: all candidate paths 404 (https://deepinfra.com/openapi.json, https://deepinfra.com/swagger.json, https://deepinfra.com/api/op…”

   proof ↗⚑ flag

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

   weight 1 · round drawn
   Groq—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

   DeepInfra—none0/10

   The evidence pack describes DeepInfra as a pay-per-token inference API with zero data retention, private dedicated deployments, and GPU clusters, but there is no mention of a distinct sandbox/test environment, test API keys, or any mechanism to separate test traffic from production data — instead it just uses live API keys against production endpoints. missing for 10: sandbox/test environment or mode, staging API keys, documented separation between test and production data flows.

   • [claimed-docs] “Need to run your own fine-tuned weights, or require data isolation? Deploy a dedicated instance on A100/H100/H200/B200/B300 with autoscaling…”
   • [claimed-docs] “With our zero retention policy your inputs, your outputs, and your user data stay private. DeepInfra is SOC 2 and ISO 27001 certified.”
   • [claimed-docs] “You only pay per token — no idle GPU time, no minimums, no seat fees.”

   proof ↗⚑ flag

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

   weight 2 · round drawn
   Groq—none0/10

   No evidence pack item documents API versioning conventions or a formal deprecation policy for Groq's endpoints or models; docs cover OpenAI-compatibility, features, and pricing but not lifecycle/versioning guarantees.

   ⚑ flag

   DeepInfra—none0/10

   The evidence pack shows OpenAI-compatible endpoints and a model catalog but contains no documentation of API versioning scheme, changelog, or deprecation/sunset policy for models or endpoints; probes even show missing openapi/spec files (404s), reinforcing the absence of such formal API-lifecycle documentation.

   • [probe] “PROBE docs-md: HTTP 404 at https://deepinfra.com/docs.md”
   • [probe] “PROBE openapi: all candidate paths 404 (https://deepinfra.com/openapi.json, https://deepinfra.com/swagger.json, https://deepinfra.com/api/op…”
   • [claimed-docs] “The only changes you need to make from your existing OpenAI code: 1. Set `base_url` to `https://api.deepinfra.com/v1/openai` 2. Set `api_…”

   proof ↗⚑ flag

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

   weight 2 · round to Groq
   Groq✓fullclaimed7/10

   Groq's Batch API explicitly supports submitting thousands of API requests asynchronously at 50% lower cost with a 24h-7day processing window, directly satisfying bulk operations across many items, and higher-throughput Flex processing further supports large-scale workloads. Missing for 10: independent hands-on verification of batch job completion/reliability at scale and details on per-batch item limits or error handling.

   • [claimed-docs] “Batch processing lets you run thousands of API requests at scale by submitting your workload as an asynchronous batch of requests to Groq wi…”
   • [claimed-docs] “Batch processing lets you run thousands of API requests at scale by submitting your workload as an asynchronous batch of requests to Groq wi…”
   • [claimed-docs] “Flex processing is available for all models to paid customers only with 10x higher rate limits compared to on-demand processing.”
   • [claimed-docs] “Flex processing is available for all models to paid customers only with 10x higher rate limits compared to on-demand processing. Pricing mat…”

   proof ↗⚑ flag

   DeepInfra—none0/10

   The docs mention a 'flex' pricing tier suited to asynchronous, non-production workloads like data enrichment and model evaluations, but there is no documented batch API, bulk upload endpoint, or mechanism to submit and process many items in a single call — only single-request chat/completions semantics are described.

   • [claimed-docs] “It’s ideal for non-production or lower-priority tasks such as model evaluations, data enrichment, and asynchronous workloads.”
   • [claimed-docs] “Set service_tier to "flex" to run Chat Completions requests at a lower cost in exchange for slower response times and occasional resource un…”

   proof ↗⚑ flag

Batch

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

   weight 2 · round to Groq
   Groq✓fullclaimed8/10

   Groq's official docs explicitly describe an asynchronous batch API delivering 50% lower cost than standard on-demand pricing, with a 24-hour to 7-day processing window and no impact on standard rate limits (groq-docs-7, groq-docs-29). This directly matches the story's requirement of documented discounted async batch inference. Missing for 10: independent/hands-on confirmation of the batch discount actually being applied in practice, and more detail on job submission/monitoring workflow beyond the docs summary.

   • [claimed-docs] “Batch processing lets you run thousands of API requests at scale by submitting your workload as an asynchronous batch of requests to Groq wi…”
   • [claimed-docs] “Batch processing lets you run thousands of API requests at scale by submitting your workload as an asynchronous batch of requests to Groq wi…”

   proof ↗⚑ flag

   DeepInfra~partialclaimed4/10

   DeepInfra documents a 'flex' service_tier that gives a 20% discount versus standard pricing and is explicitly recommended for 'asynchronous workloads' and lower-priority tasks, which partially matches the story's intent of discounted async processing. However, this is a request-level QoS flag on the standard synchronous chat API, not a true batch-job system (no file upload, job submission ID, or polling/webhook completion pattern like OpenAI's Batch API). Missing for 10: a documented batch-job submission endpoint (upload/queue a set of prompts, get a job ID, poll or receive callback), explicit SLA/turnaround time for batch jobs, and independent confirmation of the discount and workflow in practice.

   • [claimed-docs] “It’s ideal for non-production or lower-priority tasks such as model evaluations, data enrichment, and asynchronous workloads.”
   • [claimed-docs] “Flex inference is billed at a 20% discount off the model's standard per-token price.”
   • [claimed-docs] “Set service_tier to "flex" to run Chat Completions requests at a lower cost in exchange for slower response times and occasional resource un…”

   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 DeepInfra
   Groq—none0/10

   Evidence shows Groq's service tiers (on-demand, flex, auto) and batch processing, all within the shared serverless API, but no documentation of dedicated GPU capacity or customer-isolated infrastructure with autoscaling; one community remark about 'call us for quotes' for newer models hints at an enterprise track but gives no detail on dedicated capacity or autoscaling.

   • [claimed-docs] “Groq offers multiple service tiers so you can tune for latency, throughput, and reliability.”
   • [claimed-docs] “Flex processing is available for all models to paid customers only with 10x higher rate limits compared to on-demand processing.”
   • [claimed-docs] “auto: Pass this if you dont want to think about tiers and you want to leverage the best tier available to you at any given moment.”
   • [community] “As soon as i saw they switched to 'call us for quotes' for the new models, i knew they are over.”

   proof ↗⚑ flag

   DeepInfra✓fullclaimed7/10

   DeepInfra docs explicitly offer dedicated instance deployment on A100/H100/H200/B200/B300 GPUs with autoscaling and a private endpoint for data isolation, directly matching the story's request for isolated, dedicated capacity separate from the shared serverless pool. missing for 10: independent/hands-on confirmation of autoscaling behavior under load, SLA/isolation guarantees, and pricing/latency benchmarks for dedicated vs shared tiers.

   • [claimed-docs] “Run your own fine-tuned LLM on A100 / H100 / H200 / B200 / B300 with autoscaling.”
   • [claimed-docs] “Need to run your own fine-tuned weights, or require data isolation? Deploy a dedicated instance on A100/H100/H200/B200/B300 with autoscaling…”
   • [claimed-docs] “Deploy Private Models Run your own fine-tuned LLM on A100 / H100 / H200 / B200 / B300 with autoscaling.”

   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 DeepInfra
   Groq—none0/10

   Groq's docs show it supports serving pre-made LoRA adapters for inference (groq-docs-11, groq-docs-20, groq-docs-32), but there is no evidence of any fine-tuning/training capability on the platform itself — adapters must be trained elsewhere and uploaded. No mention of a fine-tuning API, training jobs, or dataset ingestion for model customization.

   • [claimed-docs] “Upload your existing LoRA adapters to run specialized inference while maintaining the performance and efficiency of Groq's infrastructure.”
   • [claimed-docs] “With LoRA inference on Groq, you can: Run inference with your pre-made LoRA adapters”
   • [claimed-docs] “Groq provides inference services for pre-made Low-Rank Adaptation (LoRA) adapters... Upload your existing LoRA adapters to run specialized i…”

   proof ↗⚑ flag

   DeepInfra~partialclaimed4/10

   DeepInfra documents deploying your own fine-tuned weights for serving (docs-8/9/18) and separately offers GPU clusters with SSH for training (docs-10), so the pieces to fine-tune-then-serve exist, but there is no evidence of an integrated managed fine-tuning job/dataset-upload API on a supported base model — customers must train themselves on rented GPUs and then manually deploy the resulting weights as a private endpoint. Missing for 10: a first-party fine-tuning job service (upload dataset, select base model, launch job) that outputs directly into the serving catalog, and any hands-on/community confirmation of this end-to-end flow.

   • [claimed-docs] “Run your own fine-tuned LLM on A100 / H100 / H200 / B200 / B300 with autoscaling.”
   • [claimed-docs] “Need to run your own fine-tuned weights, or require data isolation? Deploy a dedicated instance on A100/H100/H200/B200/B300 with autoscaling…”
   • [claimed-docs] “GPU Clusters for training and full control. Rent a B200 or B300 cluster with SSH access and run whatever you want.”
   • [claimed-docs] “Deploy Private Models Run your own fine-tuned LLM on A100 / H100 / H200 / B200 / B300 with autoscaling.”

   proof ↗⚑ flag

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

   weight 2 · round drawn
   Groq~partialclaimed6/10

   Groq's docs explicitly describe LoRA adapter upload and serving ('Upload your existing LoRA adapters to run specialized inference' — groq-docs-11/20/32), directly matching part of the story. However, evidence only covers LoRA adapters on top of existing base models, not uploading fully custom model weights, and there's no independent/hands-on confirmation of this LoRA workflow actually working in practice. Missing for 10: evidence of custom full-weight model upload/serving, and independent corroboration that LoRA upload works reliably in production.

   • [claimed-docs] “Upload your existing LoRA adapters to run specialized inference while maintaining the performance and efficiency of Groq's infrastructure.”
   • [claimed-docs] “With LoRA inference on Groq, you can: Run inference with your pre-made LoRA adapters”
   • [claimed-docs] “Groq provides inference services for pre-made Low-Rank Adaptation (LoRA) adapters... Upload your existing LoRA adapters to run specialized i…”

   proof ↗⚑ flag

   DeepInfra~partialclaimed6/10

   DeepInfra documents deploying custom fine-tuned LLM weights on dedicated GPU instances (A100/H100/H200/B200/B300) with autoscaling and private endpoints, directly addressing the 'serve my own custom model weights' part of the story. However, there is no explicit mention of LoRA adapter upload/serving specifically, nor details on the upload workflow, supported formats, or independent/hands-on confirmation that this works as claimed. Missing for 10: LoRA adapter support specifics, upload/serving workflow documentation, independent verification of custom weight deployment.

   • [claimed-docs] “Run your own fine-tuned LLM on A100 / H100 / H200 / B200 / B300 with autoscaling.”
   • [claimed-docs] “Need to run your own fine-tuned weights, or require data isolation? Deploy a dedicated instance on A100/H100/H200/B200/B300 with autoscaling…”
   • [claimed-docs] “Deploy Private Models Run your own fine-tuned LLM on A100 / H100 / H200 / B200 / B300 with autoscaling.”

   proof ↗⚑ flag

Catalog

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

   weight 2 · round to Groq
   Groq~partialcommunity5/10

   Groq's docs list several open-weight models (e.g. GPT-OSS 120B, Llama, Mixtral) and community reports mention using Kimi K2 'fast' shortly after its release, suggesting some open-weight models are added promptly, but there is no explicit documentation of a release-to-availability SLA or a systematic track record of rapid onboarding. Missing for 10: explicit timeline commitments or a changelog showing new open-weight model releases added within days, and independent verification beyond a single anecdote.

   • [claimed-docs] “GPT-OSS 120B is OpenAI's flagship open-weight language model with 120 billion parameters, built in browser search and code execution, and re…”
   • [community] “I wanted to use Kimi K2 fast for coding and Groq was the only fast provider at the time... Definitely recommend cerebras tho now that groq's…”
   • [claimed-docs] “Upload your existing LoRA adapters to run specialized inference while maintaining the performance and efficiency of Groq's infrastructure.”

   proof ↗⚑ flag

   DeepInfra—none0/10

   The evidence pack shows a broad model catalog (189 models) and general inference features, but nothing documents DeepInfra's speed in adding newly released open-weight models after public release — no changelog, launch-day availability claims, or timing data are present.

   • [probe] “PROBE models-endpoint (2026-09-04): GET https://api.deepinfra.com/v1/openai/models with NO API key returned HTTP 200 with a machine-readable…”

   proof ↗⚑ flag

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

   weight 1 · round drawn
   Groq—none0/10

   No evidence pack item documents a deprecation policy, sunset timeline, or advance-notice commitment for hosted models being removed; community comments even reference abrupt shifts (e.g., new models moved to 'call us for quotes') without formal notice process. This is a fair question for a model-hosting API but no supporting documentation exists.

   • [community] “As soon as i saw they switched to 'call us for quotes' for the new models, i knew they are over.”

   proof ↗⚑ flag

   DeepInfra—none0/10

   No evidence pack item mentions a deprecation policy, sunset notice period, or advance notice process for removing hosted models; the docs cover API usage, pricing, and infrastructure but nothing about model lifecycle/removal policy.

   ⚑ flag

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

   weight 2 · round drawn
   Groq✓fullprobed8/10

   Groq exposes an OpenAI-compatible /v1/models endpoint (confirmed live via probe returning JSON error structure, not 404), and docs establish OpenAI SDK/client compatibility with base_url https://api.groq.com/openai/v1, which per OpenAI spec includes the models list endpoint; this is documented and independently verified via a real GET request. missing for 10: no explicit Groq-authored documentation page specifically describing the /v1/models endpoint response schema, and no successful authenticated enumeration example shown (only the 401 unauthenticated probe).

   • [probe] “PROBE models-endpoint (2026-09-04): GET https://api.groq.com/openai/v1/models without a key returned HTTP 401 ({"error":{"message":"Invalid …”
   • [claimed-docs] “pass your Groq API key to the `api_key` parameter and change the `base_url` to `https://api.groq.com/openai/v1`”
   • [claimed-docs] “To start using Groq with OpenAI's client libraries, pass your Groq API key to the api_key parameter and change the base_url to https://api.g…”
   • [claimed-docs] “Fast LLM inference, OpenAI-compatible. Simple to integrate, easy to scale. Start building in minutes.”

   proof ↗⚑ flag

   DeepInfra✓fullprobed8/10

   A live probe confirms GET https://api.deepinfra.com/v1/openai/models returns HTTP 200 with a machine-readable list of 189 models, and DeepInfra's docs establish that its API is a documented OpenAI-compatible drop-in (same base_url, same client libraries), which conventionally includes the /v1/models listing endpoint. Missing for 10: a dedicated docs page explicitly describing the /v1/models endpoint schema/parameters rather than relying on general OpenAI-compatibility claims.

   • [probe] “PROBE models-endpoint (2026-09-04): GET https://api.deepinfra.com/v1/openai/models with NO API key returned HTTP 200 with a machine-readable…”
   • [claimed-docs] “Point your existing OpenAI SDK to https://api.deepinfra.com/v1/openai and your code works without changes. No migration required.”
   • [claimed-docs] “The only changes you need to make from your existing OpenAI code: 1. Set `base_url` to `https://api.deepinfra.com/v1/openai` 2. Set `api_…”
   • [claimed-docs] “OpenAI-compatible chat completions API — just change the base URL and model name.”

   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 DeepInfra
   Groq~partialprobed6/10

   Groq's docs confirm OpenAI-compatible serverless endpoints hosting multiple model families, explicitly naming GPT-OSS 120B and (via community reports) Llama and Mixtral models, but the evidence pack never mentions Qwen or DeepSeek being hosted, so the 'broad catalog' claim is only partially substantiated. missing for 10: explicit documentation of Qwen and DeepSeek model availability, and a public model list/catalog page confirming breadth beyond GPT-OSS/Llama/Mixtral.

   • [claimed-docs] “GPT-OSS 120B is OpenAI's flagship open-weight language model with 120 billion parameters, built in browser search and code execution, and re…”
   • [claimed-docs] “To start using Groq with OpenAI's client libraries, pass your Groq API key to the api_key parameter and change the base_url to https://api.g…”
   • [community] “Incredible tool. The Mixtral 8x7B model running on their hardware did 491.40 T/s for me…”
   • [community] “I'm achieving consistent 450+ tokens/sec for Mixtral 8x7b 32k and ~200 tps for Llama 2 70B-4k.”
   • [probe] “PROBE models-endpoint (2026-09-04): GET https://api.groq.com/openai/v1/models without a key returned HTTP 401 ({"error":{"message":"Invalid …”

   proof ↗⚑ flag

   DeepInfra~partialprobed7/10

   DeepInfra's docs confirm an OpenAI-compatible shared serverless endpoint where you 'set model to a model from our catalog,' and a live probe confirms 189 models are enumerable via the API without cold-start dedicated deployment. However, the evidence pack never explicitly names or confirms coverage of Llama, Qwen, DeepSeek, or GPT-OSS specifically as catalog entries. Missing for 10: explicit documentation or listing naming these specific model families as available, and independent commentary on catalog breadth/freshness beyond the raw count.

   • [claimed-docs] “The only changes you need to make from your existing OpenAI code: 1. Set `base_url` to `https://api.deepinfra.com/v1/openai` 2. Set `api_…”
   • [claimed-docs] “DeepInfra is an AI inference cloud that makes it simple to run the latest machine learning models at scale — LLMs, vision, embeddings, image…”
   • [probe] “PROBE models-endpoint (2026-09-04): GET https://api.deepinfra.com/v1/openai/models with NO API key returned HTTP 200 with a machine-readable…”

   proof ↗⚑ flag

Modalities

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

   weight 1 · round to DeepInfra
   Groq—none0/10

   No evidence of an embeddings endpoint or reranking model/API in Groq's documentation; the evidence pack covers chat completions, tool use, transcription, LoRA, batch, and service tiers but never mentions embeddings or rerank capabilities.

   ⚑ flag

   DeepInfra✓fullclaimed8/10

   DeepInfra explicitly offers embeddings and reranking models via an OpenAI-compatible API alongside LLMs, letting developers build retrieval pipelines on one vendor. Missing for 10: independent hands-on validation of embedding/rerank endpoints, benchmark quality comparisons, and detailed API-parameter docs for these specific endpoints.

   • [claimed-docs] “Embeddings & Reranking State-of-the-art embedding and reranker models for search and RAG.”
   • [claimed-docs] “DeepInfra is an AI inference cloud that makes it simple to run the latest machine learning models at scale — LLMs, vision, embeddings, image…”
   • [claimed-docs] “Point your existing OpenAI SDK to `https://api.deepinfra.com/v1/openai` and your code works without changes. No migration required.”

   proof ↗⚑ flag

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

   weight 1 · round to DeepInfra
   Groq~partialclaimed6/10

   Groq's docs confirm audio transcription (Whisper models via groq-gh-2) and vision input support through the Responses API (text and image inputs, groq-docs-13), extending beyond pure text chat. However, there is no evidence of image-generation models or text-to-speech/audio-generation capability on the platform. missing for 10: image-generation model support, audio-generation/TTS support, independent hands-on confirmation of multimodal calls.

   • [github] “await client.audio.transcriptions.create({ model: 'whisper-large-v3-turbo', file: fs.createReadStream('/path/to/file'), });”
   • [claimed-docs] “The Responses API supports both text and image inputs while producing text outputs, stateful conversations, and function calling to connect …”
   • [claimed-docs] “Groq's Responses API is fully compatible with OpenAI's Responses API, making it easy to integrate advanced conversational AI capabilities in…”

   proof ↗⚑ flag

   DeepInfra✓fullclaimed8/10

   Docs explicitly list Vision & OCR, Speech (Whisper/TTS), and Image & Video generation (FLUX, Stable Diffusion, text-to-video) as first-class model categories alongside chat, all accessible via the same OpenAI-compatible API/platform. Missing for 10: independent hands-on confirmation of calling these non-text endpoints and more detail on request/response formats for each modality.

   • [claimed-docs] “DeepInfra is an AI inference cloud that makes it simple to run the latest machine learning models at scale — LLMs, vision, embeddings, image…”
   • [claimed-docs] “FLUX, Stable Diffusion, text-to-video, and more.”
   • [claimed-docs] “Embeddings & Reranking State-of-the-art embedding and reranker models for search and RAG.”
   • [claimed-docs] “Image & Video Generation FLUX, Stable Diffusion, text-to-video, and more.”
   • [claimed-docs] “Speech recognition (Whisper) and text-to-speech models.”
   • [claimed-docs] “Vision & OCR Multimodal models for visual understanding and document text extraction.”

   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 Groq
   Groq~partialprobed7/10

   Groq's docs clearly document OpenAI-compatible base_url swapping (groq-docs-2/25), tool-use/function-calling (groq-docs-5/27), a Responses API that is 'fully compatible with OpenAI's Responses API' (groq-docs-23), and first-party remote MCP server support where 'Groq handles all orchestration' (groq-docs-6/17/28, groq-probe-4) — all of which make it straightforward to plug into agent frameworks that speak the OpenAI protocol or MCP. However, there are no named first-party guides for specific coding-agent/agent-framework integrations (e.g., LangChain, Cursor, AutoGPT) beyond the generic OpenAI-compat pointer, and community reports flag real-world reliability quirks (groq-comm-12/13/14) that add friction beyond the docs' 'simple to integrate' framing. Missing for 10: named integration guides for specific agent frameworks/coding agents, and independent hands-on confirmation of smooth agent-framework plug-in.

   • [claimed-docs] “pass your Groq API key to the `api_key` parameter and change the `base_url` to `https://api.groq.com/openai/v1`”
   • [claimed-docs] “To start using Groq with OpenAI's client libraries, pass your Groq API key to the api_key parameter and change the base_url to https://api.g…”
   • [claimed-docs] “Tool use (or function calling) is what transforms a language model from a conversational interface into an autonomous agent capable of takin…”
   • [claimed-docs] “To use tools, the model must be provided with tool definitions. These tool definitions are in JSON schema format and are passed to the model…”
   • [claimed-docs] “point to an MCP server URL and the Groq API will start using its tools without you having to implement any tool logic yourself”
   • [claimed-docs] “you simply point to an MCP server URL and the Groq API will start using its tools without you having to implement any tool logic yourself”
   • [claimed-docs] “Groq's Responses API supports remote tool use via MCP servers via HTTPS where Groq handles all orchestration... You don't implement anything…”
   • [claimed-docs] “Groq's Responses API is fully compatible with OpenAI's Responses API, making it easy to integrate advanced conversational AI capabilities in…”
   • [probe] “official MCP server documented at https://console.groq.com/docs/mcp”
   • [community] “My company had a really terrible experience trying to use Groq, and I would NOT recommend anyone use their service if you need reliability. …”
   • [community] “I don't really get the value proposition of groq as a user, the performance is really poor for the token price.”

   proof ↗⚑ flag

   DeepInfra~partialprobed5/10

   DeepInfra documents a drop-in OpenAI-compatible endpoint (base_url swap, same SDK, model catalog) which is the mechanism most coding agents/agent frameworks rely on to add a custom provider, and a live probe confirms the OpenAI-style /models endpoint works keylessly. However, the evidence pack contains no first-party guide naming or walking through integration with any specific coding agent or agent framework (e.g., LangChain, LlamaIndex, Cursor, Continue) — only generic 'point your OpenAI SDK here' instructions. missing for 10: named integration guides for specific coding agents/agent frameworks, third-party/community confirmation of successful agent hookups.

   • [claimed-docs] “Point your existing OpenAI SDK to `https://api.deepinfra.com/v1/openai` and your code works without changes. No migration required.”
   • [claimed-docs] “OpenAI-compatible chat completions API — just change the base URL and model name.”
   • [claimed-docs] “The only changes you need to make from your existing OpenAI code: 1. Set `base_url` to `https://api.deepinfra.com/v1/openai` 2. Set `api_…”
   • [claimed-docs] “Drop-in OpenAI replacement. Point your existing OpenAI SDK to https://api.deepinfra.com/v1/openai and your code works without changes.”
   • [probe] “PROBE models-endpoint (2026-09-04): GET https://api.deepinfra.com/v1/openai/models with NO API key returned HTTP 200 with a machine-readable…”

   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 DeepInfra
   Groq✓fullprobed8/10

   Groq's docs explicitly instruct developers to reuse OpenAI client libraries by just swapping api_key and base_url to https://api.groq.com/openai/v1, and this is corroborated by a live probe showing the OpenAI-style /v1/models endpoint responding in the expected JSON error format. Chat completions, streaming, tool-use, and structured outputs are all documented against this same OpenAI-compatible surface, supporting drop-in provider switching mid-workflow. Missing for 10: independent/hands-on confirmation from a real agent framework that switching providers requires zero code changes, and clarity on any OpenAI parameters/features that are NOT supported (full parity claim not verified end-to-end).

   • [claimed-docs] “pass your Groq API key to the `api_key` parameter and change the `base_url` to `https://api.groq.com/openai/v1`”
   • [claimed-docs] “To start using Groq with OpenAI's client libraries, pass your Groq API key to the api_key parameter and change the base_url to https://api.g…”
   • [claimed-docs] “Fast LLM inference, OpenAI-compatible. Simple to integrate, easy to scale. Start building in minutes.”
   • [probe] “PROBE models-endpoint (2026-09-04): GET https://api.groq.com/openai/v1/models without a key returned HTTP 401 ({"error":{"message":"Invalid …”
   • [claimed-docs] “Generating text with Groq's Chat Completions API enables you to have natural, conversational interactions with Groq's large language models.”

   proof ↗⚑ flag

   DeepInfra✓fullprobed9/10

   DeepInfra explicitly documents a drop-in OpenAI-compatible endpoint requiring only base_url/api_key/model changes, and a live probe confirms the OpenAI-compatible models endpoint is reachable and functional, supporting agents switching providers without provider-specific code. Missing for 10: independent third-party (non-vendor) confirmation of seamless mid-workflow agent switching in practice.

   • [claimed-docs] “Point your existing OpenAI SDK to `https://api.deepinfra.com/v1/openai` and your code works without changes. No migration required.”
   • [claimed-docs] “Point your existing OpenAI SDK to https://api.deepinfra.com/v1/openai and your code works without changes. No migration required.”
   • [claimed-docs] “The only changes you need to make from your existing OpenAI code: 1. Set `base_url` to `https://api.deepinfra.com/v1/openai` 2. Set `api_…”
   • [claimed-docs] “The only changes you need to make from your existing OpenAI code: Set base_url to https://api.deepinfra.com/v1/openai”
   • [claimed-docs] “Drop-in OpenAI replacement. Point your existing OpenAI SDK to https://api.deepinfra.com/v1/openai and your code works without changes.”
   • [probe] “PROBE models-endpoint (2026-09-04): GET https://api.deepinfra.com/v1/openai/models with NO API key returned HTTP 200 with a machine-readable…”

   proof ↗⚑ flag

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

   weight 3 · round drawn
   Groq✓fullprobed9/10

   Groq's docs explicitly instruct developers to point OpenAI SDK clients at Groq by only changing api_key and base_url to https://api.groq.com/openai/v1, and a live probe confirms the OpenAI-style /v1/models endpoint is functional. This is corroborated by consistent documentation across chat completions, streaming, tool use, and structured outputs all using OpenAI SDK conventions. Missing for 10: independent third-party confirmation of a drop-in swap working flawlessly in production (community evidence focuses on speed/reliability rather than compatibility specifics).

   • [claimed-docs] “pass your Groq API key to the `api_key` parameter and change the `base_url` to `https://api.groq.com/openai/v1`”
   • [claimed-docs] “To start using Groq with OpenAI's client libraries, pass your Groq API key to the api_key parameter and change the base_url to https://api.g…”
   • [probe] “PROBE models-endpoint (2026-09-04): GET https://api.groq.com/openai/v1/models without a key returned HTTP 401 ({"error":{"message":"Invalid …”
   • [claimed-docs] “Fast LLM inference, OpenAI-compatible. Simple to integrate, easy to scale. Start building in minutes.”

   proof ↗⚑ flag

   DeepInfra✓fullprobed9/10

   Documentation explicitly and repeatedly states developers only need to change base_url and api_key to point an existing OpenAI SDK at DeepInfra, with a live probe confirming the OpenAI-compatible endpoint is functional and reachable. Missing for 10: independent third-party hands-on confirmation of a full SDK migration beyond docs and a keyless probe.

   • [claimed-docs] “Point your existing OpenAI SDK to `https://api.deepinfra.com/v1/openai` and your code works without changes. No migration required.”
   • [claimed-docs] “The only changes you need to make from your existing OpenAI code: 1. Set `base_url` to `https://api.deepinfra.com/v1/openai` 2. Set `api_…”
   • [claimed-docs] “Drop-in OpenAI replacement. Point your existing OpenAI SDK to https://api.deepinfra.com/v1/openai and your code works without changes.”
   • [probe] “PROBE models-endpoint (2026-09-04): GET https://api.deepinfra.com/v1/openai/models with NO API key returned HTTP 200 with a machine-readable…”

   proof ↗⚑ flag

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

   weight 2 · round drawn
   Groq~partialprobed6/10

   Groq's API surface covers essentially all the model-facing capabilities shown in its console/docs — chat completions, streaming, tool use, structured outputs, batch processing, service tiers, LoRA inference, and MCP remote tool use are all documented as API features (groq-docs-2,5,6,7,8,11,16,28). However, account/billing controls like spend limits (groq-docs-12/24) appear to be console-only with no documented API equivalent, and the probe found no public OpenAPI spec (all candidate paths 404, groq-probe-3), making it unclear whether the full API surface is discoverable/self-describing the way the UI is. Missing for 10: API-based management of billing/spend limits, a published OpenAPI/schema confirming full UI-API parity, and independent confirmation that every console feature (e.g., playground-only settings) has an API equivalent.

   • [claimed-docs] “pass your Groq API key to the `api_key` parameter and change the `base_url` to `https://api.groq.com/openai/v1`”
   • [claimed-docs] “Tool use (or function calling) is what transforms a language model from a conversational interface into an autonomous agent capable of takin…”
   • [claimed-docs] “point to an MCP server URL and the Groq API will start using its tools without you having to implement any tool logic yourself”
   • [claimed-docs] “Batch processing lets you run thousands of API requests at scale by submitting your workload as an asynchronous batch of requests to Groq wi…”
   • [claimed-docs] “Upload your existing LoRA adapters to run specialized inference while maintaining the performance and efficiency of Groq's infrastructure.”
   • [claimed-docs] “Structured Outputs is a feature that ensures your model responses conform to your provided JSON Schema”
   • [claimed-docs] “Groq's Responses API supports remote tool use via MCP servers via HTTPS where Groq handles all orchestration... You don't implement anything…”
   • [claimed-docs] “Spend Limits: Set automated spending limits and receive budget alerts”
   • [probe] “PROBE openapi: all candidate paths 404 (https://console.groq.com/openapi.json, https://console.groq.com/swagger.json, https://console.groq.c…”

   proof ↗⚑ flag

   DeepInfra~partialprobed6/10

   DeepInfra's docs show broad API coverage matching most UI-facing capabilities: chat/completions, embeddings, vision/OCR, speech, image/video generation, private model deployment, and GPU clusters are all exposed via API endpoints, and the models endpoint is even queryable keylessly. However, there's no explicit statement of full UI/API parity, no OpenAPI/swagger spec was found (probes returned 404s), and no evidence covers account/billing/dashboard-management actions being API-accessible. missing for 10: explicit parity documentation, a discoverable OpenAPI schema, and evidence that account/billing/UI-console actions are also API-controllable.

   • [claimed-docs] “OpenAI-compatible chat completions API — just change the base URL and model name.”
   • [claimed-docs] “Run your own fine-tuned LLM on A100 / H100 / H200 / B200 / B300 with autoscaling.”
   • [claimed-docs] “Need to run your own fine-tuned weights, or require data isolation? Deploy a dedicated instance on A100/H100/H200/B200/B300 with autoscaling…”
   • [claimed-docs] “GPU Clusters for training and full control. Rent a B200 or B300 cluster with SSH access and run whatever you want.”
   • [claimed-docs] “Embeddings & Reranking State-of-the-art embedding and reranker models for search and RAG.”
   • [claimed-docs] “Image & Video Generation FLUX, Stable Diffusion, text-to-video, and more.”
   • [claimed-docs] “Speech recognition (Whisper) and text-to-speech models.”
   • [claimed-docs] “Vision & OCR Multimodal models for visual understanding and document text extraction.”
   • [probe] “PROBE llms.txt: HTTP 404 at https://deepinfra.com/llms.txt”
   • [probe] “PROBE docs-md: HTTP 404 at https://deepinfra.com/docs.md”
   • [probe] “PROBE openapi: all candidate paths 404 (https://deepinfra.com/openapi.json, https://deepinfra.com/swagger.json, https://deepinfra.com/api/op…”
   • [probe] “PROBE models-endpoint (2026-09-04): GET https://api.deepinfra.com/v1/openai/models with NO API key returned HTTP 200 with a machine-readable…”

   proof ↗⚑ flag

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

   weight 3 · round drawn
   Groq—none0/10

   Groq's docs cover API usage, models, tool-use, batch processing and billing, but nothing addresses exporting account data, conversation history, or batch outputs in open/portable formats, nor any account-closure data dump. Even though Groq's Responses API is 'stateful,' there is no documented export or data-portability mechanism.

   ⚑ flag

   DeepInfra—none0/10

   DeepInfra's evidence pack focuses entirely on its OpenAI-compatible inference API, model catalog, and infrastructure offerings; there is no mention of any user data export feature, open-format data portability, or account data download capability. Given the zero-retention policy, DeepInfra does not appear to store persistent user data, but no evidence at all addresses export tooling for whatever account/usage data does exist.

   • [claimed-docs] “With our zero retention policy your inputs, your outputs, and your user data stay private. DeepInfra is SOC 2 and ISO 27001 certified.”

   proof ↗⚑ flag

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

   weight 2 · round drawn
   Groq—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

   DeepInfra—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

Limits

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

   weight 2 · round to Groq
   Groq~partialclaimed5/10

   Groq's docs describe multiple service tiers (on-demand, flex, auto) and note Flex Processing gives '10x higher rate limits' for paid customers, plus batch processing having 'no impact to your standard rate limits' and spend-limit alerts — showing tiered scaling exists and is documented at a high level. However, no evidence pack citation shows a dedicated rate-limits page with concrete numeric request/token-per-minute limits per tier (free vs paid vs flex), so a developer can't get exact production-planning numbers from what's cited. Missing for 10: a documented numeric rate-limit table per usage tier, and independent confirmation that these documented limits match real-world behavior.

   • [claimed-docs] “Groq offers multiple service tiers so you can tune for latency, throughput, and reliability.”
   • [claimed-docs] “Flex processing is available for all models to paid customers only with 10x higher rate limits compared to on-demand processing.”
   • [claimed-docs] “Flex Processing is a service tier optimized for high-throughput workloads that prioritizes fast inference and can handle occasional request …”
   • [claimed-docs] “auto: Pass this if you dont want to think about tiers and you want to leverage the best tier available to you at any given moment.”
   • [claimed-docs] “Batch processing lets you run thousands of API requests at scale by submitting your workload as an asynchronous batch of requests to Groq wi…”
   • [claimed-docs] “Flex processing is available for all models to paid customers only with 10x higher rate limits compared to on-demand processing. Pricing mat…”
   • [claimed-docs] “Groq offers multiple service tiers so you can tune for latency, throughput, and reliability. You can distinguish these by providing the serv…”
   • [claimed-docs] “Spend Limits:** Set automated spending limits and receive budget alerts”

   proof ↗⚑ flag

   DeepInfra—none0/10

   Evidence shows DeepInfra documents a fail_fast parameter to get immediate 429s and priority/flex service tiers for speed vs cost tradeoffs, but there is no documentation of actual rate limit numbers, quotas, or how limits scale across usage/spend tiers. Missing for 10: explicit rate-limit tables, per-tier request/token quotas, and any usage-tier scaling documentation.

   • [claimed-docs] “Set the optional `fail_fast` parameter to `true` to get an immediate HTTP 429 instead of waiting.”
   • [claimed-docs] “Set service_tier to "priority" to request priority inference on supported models. Priority requests get faster time-to-first-token and highe…”
   • [claimed-docs] “Set service_tier to "flex" to run Chat Completions requests at a lower cost in exchange for slower response times and occasional resource un…”
   • [claimed-docs] “Set the optional fail_fast parameter to true to get an immediate HTTP 429 instead of waiting.”

   proof ↗⚑ flag

Pricing

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

   weight 1 · round to Groq
   Groq✓fullclaimed8/10

   Groq's billing docs explicitly state users can set automated spending limits and receive budget alerts, directly matching the story. Missing for 10: no independent/hands-on corroboration of the spend-limit UI actually working, and no detail on granularity (per-project vs account-wide) or alert channels.

   • [claimed-docs] “Spend Limits: Set automated spending limits and receive budget alerts”
   • [claimed-docs] “Spend Limits:** Set automated spending limits and receive budget alerts”

   proof ↗⚑ flag

   DeepInfra—none0/10

   The evidence pack contains no mention of spending caps, budget alerts, usage limits, or billing controls anywhere in DeepInfra's docs or pages; only pay-per-token pricing and service-tier options (priority/flex) are described. This is a fair question for a usage-based API platform, so absence of evidence means 'none' rather than 'na'.

   • [claimed-docs] “You only pay per token — no idle GPU time, no minimums, no seat fees.”
   • [claimed-docs] “Low pay-as-you-go pricing - no long-term contracts, no hidden fees, no surprises.”
   • [claimed-docs] “Low pay-as-you-go pricing - no long-term contracts, no hidden fees, no surprises. Startup? Enterprise? We can scale.”

   proof ↗⚑ flag

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

   weight 3 · round to DeepInfra
   Groq!disputedcontradicted4/10

   Groq's docs discuss billing mechanics (spend limits, flex-processing pricing parity, batch discount) implying a public pricing model, but no evidence pack item shows an actual public per-token price sheet for every hosted model, and a community report explicitly states Groq switched to 'call us for quotes' for newer models, contradicting the claim of fully public pricing without sales contact. Missing for 10: an actual public pricing page listing per-token rates for each model, and confirmation that newer/premium models are not gated behind sales quotes.

   • [claimed-docs] “Spend Limits: Set automated spending limits and receive budget alerts”
   • [claimed-docs] “Spend Limits:** Set automated spending limits and receive budget alerts”
   • [claimed-docs] “Flex processing is available for all models to paid customers only with 10x higher rate limits compared to on-demand processing. Pricing mat…”
   • [community] “As soon as i saw they switched to 'call us for quotes' for the new models, i knew they are over.”
   • [community] “Very impressive! I am even more impressed by the API pricing though - 0.27/1M tokens seems like an order of magnitude cheaper than the GPT-3…”

   proof ↗⚑ flag

   DeepInfra~partialprobed5/10

   Docs repeatedly state pay-as-you-go, per-token pricing with no contracts or sales required (docs-12, docs-16, docs-24, docs-31), strongly implying self-serve access to prices, and the model catalog is enumerable via a keyless API (deepinfra-probe-rt-1). However, no evidence pack item shows an actual public price list or per-model $/token table a founder could browse. missing for 10: explicit citation of a pricing page or API field listing per-token prices for each hosted model, independent confirmation that prices are visible without account/sales contact.

   • [claimed-docs] “You only pay per token — no idle GPU time, no minimums, no seat fees.”
   • [claimed-docs] “Flex inference is billed at a 20% discount off the model's standard per-token price.”
   • [claimed-docs] “Low pay-as-you-go pricing - no long-term contracts, no hidden fees, no surprises.”
   • [claimed-docs] “Low pay-as-you-go pricing - no long-term contracts, no hidden fees, no surprises. Startup? Enterprise? We can scale.”
   • [probe] “PROBE models-endpoint (2026-09-04): GET https://api.deepinfra.com/v1/openai/models with NO API key returned HTTP 200 with a machine-readable…”

   proof ↗⚑ flag

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

   weight 2 · round drawn
   Groq—none0/10

   No evidence in the pack mentions data residency, region selection, or geographic storage controls anywhere in Groq's docs, GitHub, or community discussion.

   ⚑ flag

   DeepInfra—none0/10

   No evidence of region selection or data residency controls; only zero-retention/SOC2/ISO27001 privacy claims are mentioned, with no mention of choosing storage region or geographic data residency options.

   ⚑ flag

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

   weight 3 · round to DeepInfra
   Groq—none0/10

   No evidence pack item addresses data usage/training policies, opt-out controls, or privacy commitments regarding whether user data is used to train Groq's models.

   ⚑ flag

   DeepInfra✓fullclaimed8/10

   DeepInfra explicitly states a zero data retention policy covering inputs, outputs, and user data, backed by SOC 2 and ISO 27001 certifications, directly addressing the no-training-data-use concern. Missing for 10: independent/third-party audit confirmation or explicit contractual opt-out language beyond the marketing statement.

   • [claimed-docs] “With our zero retention policy your inputs, your outputs, and your user data stay private. DeepInfra is SOC 2 and ISO 27001 certified.”

   proof ↗⚑ flag

3. ai-native userControl data retention and deletion

   weight 2 · round to DeepInfra
   Groq—none0/10

   No evidence pack items address data retention policies, deletion controls, or user-facing privacy/data management settings for Groq's API; the docs focus on inference features, tooling, pricing, and rate limits with nothing on retention/deletion.

   ⚑ flag

   DeepInfra~partialclaimed5/10

   DeepInfra states a 'zero retention policy' for inputs/outputs/user data and cites SOC 2 and ISO 27001 certification, implying strong default privacy posture, but there is no documented user-facing mechanism to configure retention periods, request deletion of stored data, or verify enforcement of the zero-retention claim. missing for 10: explicit data-deletion/retention-control API or settings, independent audit/verification of the zero-retention claim, and documentation of what data (if any) is logged/retained by default.

   • [claimed-docs] “With our zero retention policy your inputs, your outputs, and your user data stay private. DeepInfra is SOC 2 and ISO 27001 certified.”

   proof ↗⚑ flag

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

   weight 2 · round drawn
   Groq—none0/10

   No evidence pack item addresses telemetry, usage tracking, opt-out settings, or privacy controls for Groq's API or console; the docs cover inference features, pricing, and infra but nothing about data collection preferences.

   ⚑ flag

   DeepInfra—none0/10

   The evidence pack describes a zero-retention data policy and SOC2/ISO27001 certification, but this addresses data retention rather than a specific telemetry/usage-tracking opt-out control; no docs mention any setting, header, or account option to disable telemetry or usage analytics.

   ⚑ flag

Reliability

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

   weight 1 · round drawn
   Groq—none0/10

   Evidence shows service tiers, flex processing, and a public status page, but nowhere is there a stated uptime/response-time SLA commitment for paid or enterprise customers; enterprise tiers are only referenced via 'call us for quotes' community comments with no SLA terms disclosed.

   • [claimed-docs] “Groq offers multiple service tiers so you can tune for latency, throughput, and reliability.”
   • [claimed-docs] “Groq offers multiple service tiers so you can tune for latency, throughput, and reliability. You can distinguish these by providing the serv…”
   • [probe] “PROBE status-page (2026-09-04): https://groqstatus.com returns HTTP 200 and renders a public service-status page (page body includes "operat…”
   • [community] “As soon as i saw they switched to 'call us for quotes' for the new models, i knew they are over.”

   proof ↗⚑ flag

   DeepInfra—none0/10

   The evidence pack includes pricing, features, and a public status page, but nowhere states an uptime/availability SLA percentage or enterprise contractual guarantee — no mention of SLA terms for paid or enterprise tiers.

   • [probe] “PROBE status-page (2026-09-04): https://status.deepinfra.com returns HTTP 200 and renders a public service-status page (page body includes "…”
   • [claimed-docs] “Low pay-as-you-go pricing - no long-term contracts, no hidden fees, no surprises. Startup? Enterprise? We can scale.”
   • [claimed-docs] “Low pay-as-you-go pricing - no long-term contracts, no hidden fees, no surprises.”

   proof ↗⚑ flag

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

   weight 2 · round to DeepInfra
   Groq~partialprobed5/10

   A probe confirms a live public status page at groqstatus.com that returns HTTP 200 and shows 'operational' status, indicating Groq does operate a status page a founder could check before committing production traffic. However, the evidence doesn't confirm the page includes detailed incident history, uptime history, or granular component-level status, and there's no first-party documentation describing the status page's features. missing for 10: confirmation of incident history/log on the status page, documented SLA or uptime commitments, and independent corroboration beyond a single automated probe.

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

   proof ↗⚑ flag

   DeepInfra~partialprobed6/10

   A probe confirms DeepInfra runs a public status page (status.deepinfra.com) that returns HTTP 200 and displays operational status, which a founder could check before committing production traffic. However, the evidence does not show an incident-history log, uptime SLA, or past-incident detail on that page, so the story is only partially confirmed. Missing for 10: evidence of incident history/timeline, uptime percentage stats, or independent commentary on past outages.

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

   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 Groq
   Groq!disputedcontradicted5/10

   There is no first-party published benchmark or load-testing page in the evidence (docs cover service tiers, flex processing, batch, streaming, but no concrete TPS/latency numbers or methodology). Independent hands-on community reports do supply concrete numbers (491 T/s and 450+ TPS for Mixtral, deterministic low-latency architecture claims) supporting the speed claim, but later community threads directly contradict this, calling performance 'really poor for the token price' and reliability 'worse than traditional hosts' with frequent errors. Missing for 10: an official Groq benchmark/load-testing page with methodology, and resolution of the conflicting recent community performance complaints.

   • [community] “Incredible tool. The Mixtral 8x7B model running on their hardware did 491.40 T/s for me…”
   • [community] “I'm achieving consistent 450+ tokens/sec for Mixtral 8x7b 32k and ~200 tps for Llama 2 70B-4k.”
   • [community] “Groq AMA: 'Unlike with graphics processors, which really need data parallelism to get good throughput, our LPU architecture allows us to del…”
   • [community] “Groq staff: our system is deterministic, no need for waiting or queuing anywhere, and we can have very low latency interconnect between card…”
   • [community] “There's a trail of complaints going back years now, and they rounded out the bottom of Kimi's verification program. Groq hosted models were/…”
   • [community] “I don't really get the value proposition of groq as a user, the performance is really poor for the token price.”
   • [claimed-docs] “Groq offers multiple service tiers so you can tune for latency, throughput, and reliability.”
   • [claimed-docs] “Flex processing is available for all models to paid customers only with 10x higher rate limits compared to on-demand processing.”

   proof ↗⚑ flag

   DeepInfra—none0/10

   The evidence describes qualitative service tiers (priority/flex) that claim to affect speed, and one anecdotal HN comment about cold-start latency, but there are no published tokens-per-second figures, latency benchmarks, or load-testing guides substantiating DeepInfra's speed claims.

   • [claimed-docs] “Priority requests get faster time-to-first-token and higher throughput during peak demand.”
   • [claimed-docs] “Set service_tier to "priority" to request priority inference on supported models. Priority requests get faster time-to-first-token and highe…”
   • [claimed-docs] “Set service_tier to "flex" to run Chat Completions requests at a lower cost in exchange for slower response times and occasional resource un…”
   • [community] “Because this is popular model and many people use it, you will not experience the cold-start latency most likely. But in general it is <10s.”

   proof ↗⚑ flag

Serving

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

   weight 3 · round to DeepInfra
   Groq!disputedcontradicted6/10

   Early hands-on community reports strongly corroborate Groq's speed claims (491 T/s and ~450 t/s on Mixtral, deterministic low-latency architecture, streaming support docs), and Groq documents service tiers/flex-processing explicitly for latency/throughput tuning. However, more recent independent reports directly contradict this, describing Groq's hosted model performance as 'really poor for the token price' and 'always worse than traditional hosts,' plus reliability complaints ('so many random errors'), which concretely undercuts the fast/high-throughput positioning for current models. Missing for 10: recent (not 2024-era) throughput benchmarks, first-party TTFT numbers, and resolution of the conflicting recent user reports on performance-for-price.

   • [community] “Incredible tool. The Mixtral 8x7B model running on their hardware did 491.40 T/s for me…”
   • [community] “I'm achieving consistent 450+ tokens/sec for Mixtral 8x7b 32k and ~200 tps for Llama 2 70B-4k.”
   • [community] “Groq AMA: 'Unlike with graphics processors, which really need data parallelism to get good throughput, our LPU architecture allows us to del…”
   • [community] “Groq staff: our system is deterministic, no need for waiting or queuing anywhere, and we can have very low latency interconnect between card…”
   • [claimed-docs] “Groq offers multiple service tiers so you can tune for latency, throughput, and reliability.”
   • [claimed-docs] “Flex Processing is a service tier optimized for high-throughput workloads that prioritizes fast inference and can handle occasional request …”
   • [claimed-docs] “To enable streaming, set the parameter stream=True. The completion function will then return an iterator of completion deltas”
   • [community] “My company had a really terrible experience trying to use Groq, and I would NOT recommend anyone use their service if you need reliability. …”
   • [community] “There's a trail of complaints going back years now, and they rounded out the bottom of Kimi's verification program. Groq hosted models were/…”
   • [community] “I don't really get the value proposition of groq as a user, the performance is really poor for the token price.”

   proof ↗⚑ flag

   DeepInfra✓fullcommunity8/10

   DeepInfra provides explicit priority service tier for faster time-to-first-token and higher throughput during peak demand (docs-4, docs-26), plus fail_fast for latency-sensitive workflows and a flex tier tradeoff, showing deliberate latency/throughput controls. Community evidence corroborates generally low cold-start latency (<10s) for popular models. Missing for 10: independent benchmark data on actual TTFT/throughput numbers and hands-on comparison to competitors.

   • [claimed-docs] “Priority requests get faster time-to-first-token and higher throughput during peak demand.”
   • [claimed-docs] “Set service_tier to "priority" to request priority inference on supported models. Priority requests get faster time-to-first-token and highe…”
   • [claimed-docs] “Set service_tier to "flex" to run Chat Completions requests at a lower cost in exchange for slower response times and occasional resource un…”
   • [claimed-docs] “Set the optional `fail_fast` parameter to `true` to get an immediate HTTP 429 instead of waiting.”
   • [community] “Because this is popular model and many people use it, you will not experience the cold-start latency most likely. But in general it is <10s.”

   proof ↗⚑ flag

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

   weight 1 · round drawn
   Groq—none0/10

   No evidence pack item mentions prompt/prefix caching, context caching, or any discounted repeated-context pricing on Groq's API. Docs cover streaming, batch processing, service tiers, and flex processing, but none describe caching repeated prompt prefixes to cut latency or cost, so this applicable axis is unaddressed.

   ⚑ flag

   DeepInfra—none0/10

   The evidence pack contains no mention of prompt caching, prefix caching, or any context-reuse feature that reduces latency/cost on repeated prompts; it only covers OpenAI-compatible API basics, priority/flex service tiers, and dedicated deployments, none of which address caching of repeated context.

   ⚑ flag

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

   weight 3 · round to Groq
   Groq✓fullcommunity9/10

   Docs explicitly document setting stream=True for token-by-token delta streaming via the OpenAI-compatible API, aligned with real-time display use cases, and independent community testimonials corroborate very high token throughput consistent with responsive streaming UX. Missing for 10: explicit confirmation of SSE transport format (docs describe iterator/deltas but don't spell out 'SSE' verbatim) and no independent hands-on benchmark specifically of streaming latency/SSE behavior.

   • [claimed-docs] “To enable streaming, set the parameter stream=True. The completion function will then return an iterator of completion deltas”
   • [claimed-docs] “To enable streaming, set the parameter `stream=True`.”
   • [claimed-docs] “you can stream the model's response in real-time. This allows your application to display the response as it's being generated”
   • [claimed-docs] “To enable streaming, set the parameter stream=True. The completion function will then return an iterator of completion deltas rather than a …”
   • [community] “Incredible tool. The Mixtral 8x7B model running on their hardware did 491.40 T/s for me…”
   • [community] “I'm achieving consistent 450+ tokens/sec for Mixtral 8x7b 32k and ~200 tps for Llama 2 70B-4k.”
   • [community] “Very impressed with the speed. This is one of the most impressive tech demos I've ever seen in my life... surreal to see the thing spitting …”

   proof ↗⚑ flag

   DeepInfra~partialclaimed4/10

   DeepInfra positions itself as a drop-in OpenAI-compatible API (same SDK, same code), which implies standard OpenAI SDK features like `stream=True` token-by-token SSE would work unchanged, but no evidence pack item explicitly documents or demonstrates streaming responses, SSE format, or token-by-token behavior. Missing for 10: explicit streaming/SSE documentation or examples, benchmarks on streaming latency, and independent confirmation that stream=True works as expected.

   • [claimed-docs] “Point your existing OpenAI SDK to `https://api.deepinfra.com/v1/openai` and your code works without changes. No migration required.”
   • [claimed-docs] “The only changes you need to make from your existing OpenAI code: 1. Set `base_url` to `https://api.deepinfra.com/v1/openai` 2. Set `api_…”
   • [claimed-docs] “Drop-in OpenAI replacement. Point your existing OpenAI SDK to https://api.deepinfra.com/v1/openai and your code works without changes.”

   proof ↗⚑ flag

Structured

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

   weight 3 · round to Groq
   Groq✓fullclaimed8/10

   Groq's docs explicitly describe Structured Outputs with JSON Schema and a strict:true mode that uses constrained decoding to guarantee schema-conforming output, plus tool-use docs confirming JSON schema-based function calling. This is first-party documented and consistent with OpenAI-compatible API design. Missing for 10: independent/hands-on verification that strict mode reliably parses in production and no community corroboration of structured-output reliability specifically.

   • [claimed-docs] “With strict: true, the model uses constrained decoding to guarantee that the output will always match your schema exactly.”
   • [claimed-docs] “Structured Outputs is a feature that ensures your model responses conform to your provided JSON Schema”
   • [claimed-docs] “With `strict: true`, the model uses constrained decoding to guarantee that the output will always match your schema exactly”
   • [claimed-docs] “To use tools, the model must be provided with tool definitions. These tool definitions are in JSON schema format and are passed to the model…”

   proof ↗⚑ flag

   DeepInfra—none0/10

   The evidence pack details DeepInfra's OpenAI-compatible API, pricing tiers, and model catalog, but contains no mention of JSON schema enforcement, structured output modes, or grammar-constrained decoding. Missing for 10: any documentation of `response_format`/json_schema support, grammar-based constrained generation, or examples showing reliable JSON parsing guarantees.

   ⚑ 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 Groq
   Groq~partialcommunity5/10

   Groq's docs clearly document OpenAI-compatible function/tool calling (JSON-schema tool definitions, strict-mode structured outputs, and now Remote MCP where 'Groq handles all orchestration') which supports agentic tool use, but none of the evidence explicitly confirms parallel tool calls or multi-step tool-use loops working reliably. Community reports also raise reliability concerns ('so many random errors, so many silly quirks') that create some doubt about robustness for agent loops, though not a concrete tool-calling failure. missing for 10: explicit documentation/examples of parallel tool calling, multi-step agentic loop reliability testing, and independent hands-on verification that tool calls remain faithful across steps.

   • [claimed-docs] “Tool use (or function calling) is what transforms a language model from a conversational interface into an autonomous agent capable of takin…”
   • [claimed-docs] “To use tools, the model must be provided with tool definitions. These tool definitions are in JSON schema format and are passed to the model…”
   • [claimed-docs] “With strict: true, the model uses constrained decoding to guarantee that the output will always match your schema exactly.”
   • [claimed-docs] “point to an MCP server URL and the Groq API will start using its tools without you having to implement any tool logic yourself”
   • [claimed-docs] “Groq's Responses API supports remote tool use via MCP servers via HTTPS where Groq handles all orchestration... You don't implement anything…”
   • [community] “My company had a really terrible experience trying to use Groq, and I would NOT recommend anyone use their service if you need reliability. …”
   • [community] “There's a trail of complaints going back years now, and they rounded out the bottom of Kimi's verification program. Groq hosted models were/…”

   proof ↗⚑ flag

   DeepInfra—none0/10

   The evidence pack only shows generic OpenAI-compatible API claims (base URL swap, chat completions) and unrelated features (priority tiers, GPU deployment, image/video models) but never mentions function/tool calling, parallel tool calls, or multi-step agentic tool use, nor any benchmark/community report on tool-calling fidelity. Missing for 10: explicit documentation of a `tools`/`function_call` parameter, evidence of parallel tool call support, and any hands-on or benchmark confirmation that tool calling works reliably in agent loops.

   ⚑ flag