Every frontier AI model’s per-token price, current and historical

Headline tier is API list price. Every figure on this page is the public per-million-token rate the provider documents on its own pricing page. Negotiated enterprise pricing, committed-spend discounts, and special programs (OpenAI startup credits, Anthropic enterprise volume, Google Vertex AI region rates) are out of scope — this is a list-price reference.

Cached input pricing is a third axis that has grown more important since OpenAI introduced automatic prompt caching in October 2024 and Anthropic shipped explicit prompt caching in mid-2024. For workloads with a long stable prefix (agents replaying the same system prompt; RAG against a fixed corpus), the cached input rate dominates the effective per-call cost. The page surfaces it as a column rather than the headline number because non-cached workloads still see the headline rate.

Batch endpoints typically halve both input and output rates (OpenAI Batch, Anthropic Message Batches, Google Batch API) in exchange for asynchronous turnaround up to 24 hours. The row note flags batch availability per model; the headline number is the synchronous-API rate.

Open-weights pricing is a hosting-provider proxy. Meta’s Llama, DeepSeek, Mistral’s Apache-2.0-licensed releases, and Alibaba’s open-weights Qwen sizes have no “official” per-token price — the model is free to download. The row records the Together AI or Groq published rate as a reference. Cheaper rates exist on self-hosted inference (vLLM, sglang) or on cost-optimized providers (Fireworks, DeepInfra); more expensive ones exist on the hyperscalers (AWS Bedrock, Azure AI Foundry).

Reasoning tokens count as output. The o-series, Anthropic’s extended-thinking models, and Gemini’s 2.5 Flash thinking budget all bill reasoning tokens at the output rate. A “cheap-looking” reasoning model can be expensive in practice because each call generates more reasoning than visible output. The page records the per-token rate; the effective per-call cost is workload-dependent.

Tokenizer differences cross-provider. $1 / M tokens does not buy the same amount of text from OpenAI’s o200k tokenizer as it does from Anthropic’s claude-3 tokenizer or Google’s SentencePiece. Anthropic’s 4.7-era tokenizer in particular maps 1.0–1.35× the prior token count for the same input, which compresses the effective price improvement. Compare numbers in tokens, not in characters or pages, and treat them as a same-provider apples-to-apples rather than a strict cross-provider one.

Historical pricing is sourced from Wayback Machine and launch posts. Every effective-date entry links to a primary source — provider blog post, archived pricing page, or release announcement. Where the Wayback snapshot is the only viable cite (the provider has rewritten the page since), the URL points there directly.