Claude Opus 5.5 takes the frontier lead
Anthropic edges OpenAI by 2 points on the Artificial Analysis intelligence index.
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Practical starting points from real OpenRouter usage, production performance, and independent benchmarks.
Practical starting points from real OpenRouter usage, production performance, and independent benchmarks.
Anthropic edges OpenAI by 2 points on the Artificial Analysis intelligence index.
The simplest way to get free inference. openrouter/free is a router that selects free models at random from the models available on OpenRouter. The router smartly filters for models that support features needed for your request such as image understanding, tool calling, structured outputs and more.
The Pareto Router maintains a tiered shortlist of strong coding models, ranked by [Artificial Analysis](https://artificialanalysis.ai/) coding percentiles. Set min_coding_score between 0 and 1 on the [pareto-router plugin](https://openrouter.ai/docs/guides/routing/routers/pareto-router#the-min_coding_score-parameter) to control how strong a coder you need; higher scores select stronger (and typically more expensive) models. If you omit min_coding_score, the router defaults to the High tier. Selecting Nitro from the variant dropdown ranks the models in your tier by measured throughput and routes each request to the fastest one, so you trade some model variety for lower latency. Read the [Pareto Router docs](https://openrouter.ai/docs/guides/routing/routers/pareto-router) for the full selection logic, fallback behavior, and how to customize routing. For another way to route, see the [Auto Router](/openrouter/auto).
Fusion turns your prompt into a small multi-model deliberation. A panel of expert models (see below) analyzes your prompt in parallel with web search and web fetch enabled, then a judge model synthesizes their responses into a structured analysis — consensus, contradictions, partial coverage, unique insights, and blind spots — and writes the final answer from it. By default the panel is the `Quality` preset; switch to `Budget` for cheaper members, or override the panel and judge entirely with the fusion plugin's `analysis_models` and `model` fields. Reach for Fusion when a single model isn't enough — research, expert critique, or anywhere the cost of being wrong outweighs a few extra completions. Because Fusion runs every panel member plus a judge call, your request is priced as the sum of those underlying completions rather than a single model. To see which models ran, visit [Activity](/activity). Note: Context limits are dependent on which models you select. Learn more in our [docs](/docs/guides/features/plugins/fusion). For another way to route, see the [Auto Router](/openrouter/auto).
The experimental version of our Auto Router where we test new improvements. Use it to get the latest and greatest version of our general purpose auto router, but expect beta quality. When new improvements are proven in auto-beta, we graduate them to the auto router. We've love to hear your feedback on whether auto-beta is working for you, or requests for routing improvements. To see which model was used, visit [Activity](/activity), or read the `model` attribute of the response. Your response will be priced at the same rate as the routed model. Learn more, including how to customize the models for routing, in our [docs](/docs/guides/routing/routers/auto-router).
Auto Code is OpenRouter’s router built specifically for coding agents and harnesses. It uses the same routing engine as the [Auto Router](https://openrouter.ai/openrouter/auto), with every request treated as part of an agent session and model choices guided by coding-task pass rate, completion time, and cost per solved task. It follows edits, test results, and recurring errors across turns, keeping the model while work is going well and trying more reasoning effort before switching when trouble persists. Its default coding tier starts from stronger models and weighs failed code changes more heavily. Your model and provider restrictions still apply.
The most capability per dollar of list price.
| Rank | Model | Intel pctl | Coding pctl | Agentic pctl | Tokens |
|---|---|---|---|---|---|
| 1 | 40 | 79 | 91 | 15.9T | |
| 2 | – | 75 | 73 | 3.7T | |
| 3 | 65 | – | – | 25.4B | |
| 4 | – | 59 | 54 | 2.5T | |
| 5 | 34 | 78 | 77 | 25.8B |
Highest median output speed across multiple providers.
| Rank | Model | Throughput | TTFT p50 | Providers |
|---|---|---|---|---|
| 1 | 820 tok/s | 167ms | 27 | |
| 2 | 708 tok/s | 282ms | 24 | |
| 3 | 376 tok/s | 285ms | 5 | |
| 4 | 353 tok/s | 201ms | 11 | |
| 5 | 350 tok/s | 227ms | 6 |
Pin one model ID and always get the newest release in that family.
| Rank | Model | Family tokens | Context |
|---|---|---|---|
| 1 | 4.8T | 1M | |
| 2 | 56B | 33K | |
| 3 | 1.1T | 1.1M | |
| 4 | 5.7T | 1M | |
| 5 | 73.8T | 1M |
The most-used models with a free endpoint.
| Rank | Model | Tokens/wk | Context |
|---|---|---|---|
| 1 | 2.9T | 1M | |
| 2 | 2.8T | 1M | |
| 3 | 1.1T | 1M | |
| 4 | 747.6B | 512K | |
| 5 | 594.7B | 1M |
Generate and edit images across the leading model providers.
Generate videos from text and image prompts across the leading model providers.
Start with a focused set of models for the job you need to do.