OpenRouter Benchmarks

Use OpenRouter's unified benchmarks endpoint to answer benchmark-backed model ranking and model-selection questions. The endpoint aggregates Artificial Analysis and Design Arena data and returns citation metadata that should be preserved when reporting results.

Prerequisites

Set OPENROUTER_API_KEY to any valid OpenRouter API key. Benchmarks do not require a management key.

export OPENROUTER_API_KEY=sk-or-v1-...

Decision Tree

User wants to...	Action
See benchmark-ranked models across sources	Call `GET /api/v1/benchmarks` and preserve source/citation metadata
Choose a model for an app/use case	Check whether Artificial Analysis or Design Arena contains a relevant signal; say when no direct benchmark exists
Find best coding, intelligence, or agentic models	Use `task_type=coding`, `task_type=intelligence`, or `task_type=agentic`
Query Artificial Analysis only	Use `source=artificial-analysis`
Query Design Arena only	Use `source=design-arena`, plus `arena` and `category` when relevant
Get raw API-shaped data for integration work	Return the raw `data`/`meta` shape from the endpoint
Understand all response fields or direct curl usage	Read `references/benchmarks-api.md`

Use openrouter-models instead when the user needs pricing, context length, supported parameters, modalities, or provider endpoint performance without asking for benchmark rankings.

For creative writing, storytelling, or editorial apps, this endpoint currently has no direct writing-quality benchmark. Treat Artificial Analysis intelligence_index as a weak general-capability signal, and use agentic_index only if the app performs multi-step planning/revision. Do not imply that Design Arena visual/code categories measure prose quality.

Availability Gate

Do not recommend a benchmark-ranked model until it passes an availability check through the models/endpoints API. Benchmark rows can contain dated or benchmark-specific model_permaslug values that are useful for attribution but are not always the exact routable OpenRouter model ID.

Before recommending a benchmark candidate:

Check GET /api/v1/models for an exact id match to the benchmark model_permaslug.
If there is no exact id match but a model has canonical_slug equal to the benchmark model_permaslug, treat the benchmark row as evidence for that model family, not as a directly recommendable ID. Use the model's actual id only after verifying availability.
Check GET /api/v1/models/{author}/{slug}/endpoints or use openrouter-models get-endpoints.ts for provider status.
Prefer candidates with at least one clearly usable endpoint. If all endpoints are degraded, have uptime_last_30m: 0, or the OpenRouter model page/API indicates the model is unavailable, exclude it from primary recommendations and explain that the benchmark result is not currently actionable.
When availability is ambiguous, say so and recommend a verified available alternative instead of presenting the benchmark leader as the default choice.

Do not rely on endpoint status: 0 alone. Model-level availability signals such as routing error messages, warning messages, zero request limits, empty endpoint lists, or provider-specific access restrictions can make a benchmark leader non-actionable even when one endpoint appears operational. If availability signals disagree, explain the ambiguity and avoid making that model the primary recommendation.

API Usage

Query parameters:

Flag	Values	Notes
`source`	`artificial-analysis`, `design-arena`	Omitting it returns all sources.
`task_type`	`coding`, `intelligence`, `agentic`	Maps to source-specific indices/categories.
`arena`	`models`, `builders`, `agents`	Design Arena only; defaults server-side to `models`.
`category`	`codecategories`, `uicomponent`, `gamedev`, `3d`, `dataviz`, `image`, `video`, `svg`, etc.	Design Arena only.
`max_results`	positive integer	Maximum number of rows returned by the API.

Always preserve meta.citation, meta.source_url, and meta.as_of; include attribution when republishing benchmark data.

When results include both sources, do not present them as a single absolute leaderboard: Artificial Analysis indices and Design Arena ELO use different scales. Compare within each source, or rerun with source=artificial-analysis or source=design-arena for a source-specific ranking.

Interpreting Results

Artificial Analysis rows include intelligence_index, coding_index, and agentic_index; higher is better.
Design Arena rows include elo, win_rate, avg_generation_time_ms, arena, category, and tournament_stats; higher elo/win_rate is better, lower generation time is faster.
pricing.prompt and pricing.completion are USD per token as decimal strings. Multiply by 1,000,000 for per-million-token costs.
model_permaslug identifies the benchmarked model entry. Verify it against GET /api/v1/models before using it as a chat/completions model ID.
meta.model_count counts unique models in the response, which can differ from data.length when multiple Design Arena categories are returned.

Direct API Call

curl 'https://openrouter.ai/api/v1/benchmarks?source=artificial-analysis&task_type=coding&max_results=10' \
  -H "Authorization: Bearer $OPENROUTER_API_KEY"

Read references/benchmarks-api.md when implementing against the raw API or handling source-specific response shapes.

openrouter-benchmarks