How Jan’s scores are calculated

The full audit trail, recomputed from the verdict data at build time through the same code that produced the leaderboard: verdict × quality × story weight per cell, cells sum to dimension scores, dimensions blend into the PA Score. Every number on the product page is reproducible from this page alone; for why the formula looks like this, see the methodology.

verdict factors: full ×1.0 · partial ×0.6 · disputed ×0.3 · none ×0.0 · n/a excluded from both sides · cell points = weight × quality × factor · cell max = weight × 10

PA Score16/100

Agent-ready 14.6 × 0.30 = 4.38

API quality 8.0 × 0.20 = 1.60

Openness 34.8 × 0.20 = 6.96

Built-in AI 18.7 × 0.15 = 2.80

Automation 0.0 × 0.15 = 0.00

(4.38 + 1.60 + 6.96 + 2.80 + 0.00) ÷ (0.30 + 0.20 + 0.20 + 0.15 + 0.15) = 15.75 ÷ 1.00 = 15.7

Scores are stored to 1 decimal; the product page’s pills round to whole numbers for display. Each dimension below shows the stories, verdicts, and cited evidence behind its number.

Agent-ready14.6/100×0.30 of the PA blend

Outside-in: can YOUR agent reach and drive this product — API, MCP, CLI, headless runs, agent docs.

Point an agent at llms.txt or agent-oriented docsweight 2

2 (weight) × 0 (quality) × 0.0 (none) = 0.0 of 20 max

[probe] https://jan.ai/llms.txt“PROBE llms.txt: HTTP 404 at https://jan.ai/llms.txt”
[probe] https://jan.ai/openapi.json“PROBE openapi: all candidate paths 404 (https://jan.ai/openapi.json, https://jan.ai/swagger.json, https://jan.ai/api/openapi.json, https://jan.ai/.well-known/openapi.json)”

Run the product headlessly / in CI for automationweight 2

2 (weight) × 0 (quality) × 0.0 (none) = 0.0 of 20 max

[github] https://github.com/janhq/jan“OpenAI-Compatible API: Local server at `localhost:1337` for other applications”
[github] https://github.com/janhq/jan“Model Context Protocol: MCP integration for agentic capabilities”
[github] https://github.com/janhq/jan“This handles everything: installs dependencies, builds core components, and launches the app.”

Plug MCP servers into this product so it can use their toolsweight 3

3 (weight) × 6 (quality) × 0.6 (partial) = 10.8 of 30 max

[github] https://github.com/janhq/jan“Model Context Protocol: MCP integration for agentic capabilities”

Connect an agent via an official MCP serverweight 3

n/a — not applicable to this product: excluded from numerator and denominator

[github] https://github.com/janhq/jan“Model Context Protocol: MCP integration for agentic capabilities”
[github] https://github.com/janhq/jan“OpenAI-Compatible API: Local server at `localhost:1337` for other applications”

Use an official CLIweight 2

2 (weight) × 0 (quality) × 0.0 (none) = 0.0 of 20 max

no evidence cited — the verdict rests on absence of evidence, re-checked on refresh

Drive the product through a documented public APIweight 3

3 (weight) × 5 (quality) × 0.6 (partial) = 9.0 of 30 max

[github] https://github.com/janhq/jan“OpenAI-Compatible API: Local server at `localhost:1337` for other applications”
[probe] https://jan.ai/llms.txt“PROBE llms.txt: HTTP 404 at https://jan.ai/llms.txt”
[probe] https://jan.ai/openapi.json“PROBE openapi: all candidate paths 404 (https://jan.ai/openapi.json, https://jan.ai/swagger.json, https://jan.ai/api/openapi.json, https://jan.ai/.well-known/openapi.json)”

Issue scoped/least-privilege API credentials for an agentweight 2

2 (weight) × 0 (quality) × 0.0 (none) = 0.0 of 20 max

[github] https://github.com/janhq/jan“OpenAI-Compatible API: Local server at `localhost:1337` for other applications”
[github] https://github.com/janhq/jan“Model Context Protocol: MCP integration for agentic capabilities”

Build against official SDKsweight 2

2 (weight) × 0 (quality) × 0.0 (none) = 0.0 of 20 max

[github] https://github.com/janhq/jan“OpenAI-Compatible API: Local server at `localhost:1337` for other applications”
[probe] https://jan.ai/llms.txt“PROBE llms.txt: HTTP 404 at https://jan.ai/llms.txt”
[probe] https://jan.ai/openapi.json“PROBE openapi: all candidate paths 404 (https://jan.ai/openapi.json, https://jan.ai/swagger.json, https://jan.ai/api/openapi.json, https://jan.ai/.well-known/openapi.json)”

Subscribe to events via webhooksweight 2

2 (weight) × 0 (quality) × 0.0 (none) = 0.0 of 20 max

no evidence cited — the verdict rests on absence of evidence, re-checked on refresh

Connect a coding agent to this product as a working backendweight 3

3 (weight) × 6 (quality) × 0.6 (partial) = 10.8 of 30 max

[github] https://github.com/janhq/jan“OpenAI-Compatible API: Local server at `localhost:1337` for other applications”
[github] https://github.com/janhq/jan“Model Context Protocol: MCP integration for agentic capabilities”
[probe] https://jan.ai/openapi.json“PROBE openapi: all candidate paths 404 (https://jan.ai/openapi.json, https://jan.ai/swagger.json, https://jan.ai/api/openapi.json, https://jan.ai/.well-known/openapi.json)”

Agent-ready = 30.6 ÷ 210 × 100 = 14.6

API quality8.0/100×0.20 of the PA blend

The programmable surface once an agent is there — machine-readable spec, interactive docs, sandbox, versioning discipline.

Explore an interactive API reference with runnable examplesweight 2

2 (weight) × 0 (quality) × 0.0 (none) = 0.0 of 20 max

[probe] https://jan.ai/llms.txt“PROBE llms.txt: HTTP 404 at https://jan.ai/llms.txt”
[probe] https://jan.ai/openapi.json“PROBE openapi: all candidate paths 404 (https://jan.ai/openapi.json, https://jan.ai/swagger.json, https://jan.ai/api/openapi.json, https://jan.ai/.well-known/openapi.json)”
[github] https://github.com/janhq/jan“OpenAI-Compatible API: Local server at `localhost:1337` for other applications”

Download a machine-readable API spec (OpenAPI or equivalent)weight 2

2 (weight) × 4 (quality) × 0.6 (partial) = 4.8 of 20 max

[github] https://github.com/janhq/jan“OpenAI-Compatible API: Local server at `localhost:1337` for other applications”
[probe] https://jan.ai/openapi.json“PROBE openapi: all candidate paths 404 (https://jan.ai/openapi.json, https://jan.ai/swagger.json, https://jan.ai/api/openapi.json, https://jan.ai/.well-known/openapi.json)”

Test against a sandbox environment without touching production dataweight 1

n/a — not applicable to this product: excluded from numerator and denominator

no evidence cited — the verdict rests on absence of evidence, re-checked on refresh

Rely on versioned APIs with a documented deprecation policyweight 2

2 (weight) × 0 (quality) × 0.0 (none) = 0.0 of 20 max

[github] https://github.com/janhq/jan“OpenAI-Compatible API: Local server at `localhost:1337` for other applications”
[probe] https://jan.ai/llms.txt“PROBE llms.txt: HTTP 404 at https://jan.ai/llms.txt”
[probe] https://jan.ai/openapi.json“PROBE openapi: all candidate paths 404 (https://jan.ai/openapi.json, https://jan.ai/swagger.json, https://jan.ai/api/openapi.json, https://jan.ai/.well-known/openapi.json)”

API quality = 4.8 ÷ 60 × 100 = 8.0

Openness34.8/100×0.20 of the PA blend

Can you leave, inspect, or self-host — data export, open source, portability.

Do everything through the API that I can do in the UIweight 2

2 (weight) × 4 (quality) × 0.6 (partial) = 4.8 of 20 max

[github] https://github.com/janhq/jan“OpenAI-Compatible API: Local server at `localhost:1337` for other applications”
[github] https://github.com/janhq/jan“Custom Assistants: Create specialized AI assistants for your tasks”
[github] https://github.com/janhq/jan“Model Context Protocol: MCP integration for agentic capabilities”
[probe] https://jan.ai/openapi.json“PROBE openapi: all candidate paths 404 (https://jan.ai/openapi.json, https://jan.ai/swagger.json, https://jan.ai/api/openapi.json, https://jan.ai/.well-known/openapi.json)”
[probe] https://jan.ai/llms.txt“PROBE llms.txt: HTTP 404 at https://jan.ai/llms.txt”

Export all of my data in open formats and leaveweight 3

3 (weight) × 0 (quality) × 0.0 (none) = 0.0 of 30 max

no evidence cited — the verdict rests on absence of evidence, re-checked on refresh

Read the product's source under an open licenseweight 2

2 (weight) × 5 (quality) × 0.6 (partial) = 6.0 of 20 max

[github] https://github.com/janhq/jan“Local AI Models: Download and run LLMs (Llama, Gemma, Qwen, GPT-oss etc.) from HuggingFace”
[github] https://github.com/janhq/jan“This handles everything: installs dependencies, builds core components, and launches the app.”

Self-host the core productweight 3

3 (weight) × 8 (quality) × 1.0 (full) = 24.0 of 30 max

[github] https://github.com/janhq/jan“Download and run LLMs with **full control** and **privacy**.”
[github] https://github.com/janhq/jan“This handles everything: installs dependencies, builds core components, and launches the app.”
[github] https://github.com/janhq/jan“OpenAI-Compatible API: Local server at `localhost:1337` for other applications”
[github] https://github.com/janhq/jan“Local AI Models: Download and run LLMs (Llama, Gemma, Qwen, GPT-oss etc.) from HuggingFace”
[claimed-docs] https://jan.ai“Personal Intelligence that answers only to you”

Openness = 34.8 ÷ 100 × 100 = 34.8

Built-in AI18.7/100×0.15 of the PA blend

Inside-out: how agentic the product itself is for its users — built-in assistants, autonomous features.

Get AI-generated insights and suggestions from my data inside the productweight 2

2 (weight) × 0 (quality) × 0.0 (none) = 0.0 of 20 max

[github] https://github.com/janhq/jan“Local AI Models: Download and run LLMs (Llama, Gemma, Qwen, GPT-oss etc.) from HuggingFace”
[github] https://github.com/janhq/jan“Cloud Integration: Connect to GPT models via OpenAI, Claude models via Anthropic, Mistral, Groq, MiniMax, and others”
[github] https://github.com/janhq/jan“Custom Assistants: Create specialized AI assistants for your tasks”
[github] https://github.com/janhq/jan“Model Context Protocol: MCP integration for agentic capabilities”
[claimed-docs] https://jan.ai“Choose from open models or plug in your favorite online models.”

Set up automations that run autonomously in the backgroundweight 2

2 (weight) × 0 (quality) × 0.0 (none) = 0.0 of 20 max

no evidence cited — the verdict rests on absence of evidence, re-checked on refresh

Delegate tasks to a built-in AI assistant inside the productweight 3

3 (weight) × 6 (quality) × 0.6 (partial) = 10.8 of 30 max

[github] https://github.com/janhq/jan“Custom Assistants: Create specialized AI assistants for your tasks”
[github] https://github.com/janhq/jan“Model Context Protocol: MCP integration for agentic capabilities”
[claimed-docs] https://jan.ai“Personal Intelligence that answers only to you”

Operate the product with natural-language commandsweight 2

2 (weight) × 5 (quality) × 0.6 (partial) = 6.0 of 20 max

[github] https://github.com/janhq/jan“Custom Assistants: Create specialized AI assistants for your tasks”
[github] https://github.com/janhq/jan“Model Context Protocol: MCP integration for agentic capabilities”
[claimed-docs] https://jan.ai“Choose from open models or plug in your favorite online models.”

Built-in AI = 16.8 ÷ 90 × 100 = 18.7

Automation0.0/100×0.15 of the PA blend

Depth of automation primitives — rules, scheduling, bulk operations, webhooks.

Perform bulk operations across many items at onceweight 2

2 (weight) × 0 (quality) × 0.0 (none) = 0.0 of 20 max

no evidence cited — the verdict rests on absence of evidence, re-checked on refresh

Define rules that trigger actions automatically on eventsweight 3

3 (weight) × 0 (quality) × 0.0 (none) = 0.0 of 30 max

no evidence cited — the verdict rests on absence of evidence, re-checked on refresh

Schedule recurring jobs or workflowsweight 2

2 (weight) × 0 (quality) × 0.0 (none) = 0.0 of 20 max

no evidence cited — the verdict rests on absence of evidence, re-checked on refresh

Version, review, and roll back my automationsweight 1

1 (weight) × 0 (quality) × 0.0 (none) = 0.0 of 10 max

no evidence cited — the verdict rests on absence of evidence, re-checked on refresh

Automation = 0.0 ÷ 80 × 100 = 0.0