The model catalog.

The frontier ceiling — deepest reasoning and vision for the hardest problems.

Prior Opus flagship — frontier reasoning, pinned in place.

Frontier generalist — broad, sharp, and steady on long tool chains.

The prior GPT-5 flagship — most of 5.5's strength for less.

Frontier scale — a 1M-token window and strong multimodal reasoning.

Fast frontier reasoning with a very large window.

Default. Strong general-purpose agent model with fast responses.

The premium ceiling — top reasoning and vision for the hardest work.

The newest Sonnet — near-frontier quality for everyday premium work.

The fast Claude — premium-family quality at a fraction of the latency.

Highest-quality GLM. Flagship tuned for quality over raw speed — higher, variable latency.

Fast, reliable coding flagship for everyday heavy work.

Heavy coding flagship — large MoE built for complex code.

Coding-specialist flagship. Quality-first; higher, variable latency.

Qwen's largest flagship — a heavyweight that codes exceptionally well.

Big reasoning model at a cheap-tier price — standout value.

Strong reasoning. Best on open-ended analysis, not strict tool loops.

Extended reasoning; budget output tokens for its hidden chain-of-thought.

Cheap reasoning with a large context window.

Baidu's reasoning specialist — deep deliberation at a low price.

Tencent's reasoning model — strong long-form thinking, very cheap.

StepFun's multimodal reasoner — thinks, sees, and calls tools.

Strong all-round workhorse with a very large context.

Hybrid MoE, strong on multi-agent, 256K context.

Efficient workhorse — big context at a low price.

Large general-purpose model, fast and capable.

Baidu's flagship generalist — broad knowledge with vision.

Tencent's fast generalist — quick answers at a rock-bottom price.

Google's fast frontier model — 1M context, multimodal, cheap.

Cheap coding offload for routine changes.

Balanced coding model with a large context.

Fast coding-general model.

Coding-specialist tuned for software tasks.

256K context for the price of a small model.

Meituan's efficient MoE — a 1M-token window at a small-model price.

Smallest and fastest tier.

Lowest price per token.

Cheapest tool + reasoning capable model.

OpenAI's strongest general embedding — high quality, 3072-dim.

OpenAI's cheap embedding — most of the quality at a fraction of the price.

Voyage's balanced retrieval embedding — strong quality, cheap.

Voyage's code-specialized embedding — built for code search.

Cohere's multilingual embedding — long inputs, 100+ languages.

Google's embedding — top-ranked general retrieval quality.

Qwen's open-weight embedding — multilingual, high-dimensional.

BAAI's versatile embedding — multilingual, multi-granularity.

Jina's multilingual embedding — task-tuned, long inputs.

Mistral's embedding — solid retrieval quality, simple to adopt.

Nomic's fully-open embedding — long inputs, very cheap.