Plainly

The one page with numbers on it

Model facts

Every hard figure on this site lives here, so there is exactly one page to keep current instead of a hundred. If an explainer quotes a number, it links back to this table.

Last verified 11 August 2026 against each vendor's own documentation, including a re-check of the Anthropic rows that produced one correction, noted below. Anything not verified is flagged in orange.

How to read this

Context window is how much the model can hold in mind at once, input and output together. Max output is the ceiling on a single reply. Price is per million tokens, quoted separately for what you send (input) and what you get back (output) — output almost always costs several times more.

Anthropic (Claude)

Model API ID Context Max output Input / MTok Output / MTok Reliable cutoff Training cutoff
Claude Fable 5 claude-fable-5 1M 128k $10 $50 Jan 2026 Jan 2026
Claude Opus 5 claude-opus-5 1M 128k $5 $25 May 2026 May 2026
Claude Sonnet 5 claude-sonnet-5 1M 128k $2 $10 Jan 2026 Jan 2026
Claude Haiku 4.5 claude-haiku-4-5 200k 64k $1 $5 Feb 2025 Jul 2025

Correction, 11 August 2026. This page previously flagged Sonnet 5's $2 / $10 rate as introductory, said the standard rate was $3 / $15, and told you to check whether the promotion had ended before relying on it. On re-checking, that is no longer true. Anthropic's pricing documentation now states that the $2 / $10 pricing, "announced at launch as introductory pricing through August 31, 2026, is now the standard price," and that "the previously scheduled increase to $3 / $15 per million input/output tokens on September 1, 2026 will not occur." The warning has been removed and the asterisks with it.
Sources: Anthropic models overview and pricing documentation, both fetched 11 August 2026. Batch processing and prompt caching change these numbers substantially and are documented on that pricing page.
Two cutoff columns: the reliable date is how far the model's knowledge is dependable; the training date is the outer edge of its data. The months between are thin, half-known territory — note Haiku's five-month gap. Explained properly in Training cutoff.

OpenAI, Google and Meta

Vendor Model API ID Context Max output Input / MTok Output / MTok
OpenAI GPT-5.6 Sol gpt-5.6-sol 1.05M 128k $5 $30
OpenAI GPT-5.6 Terra gpt-5.6-terra 1.05M 128k $2 $12
Google Gemini 3.6 Flash gemini-3.6-flash 1.05M 64k $1.50 $7.50
Meta Muse Spark 1.2 muse-spark-1.2 not published not published $1.25 $4.25
Meta Llama 4 / Llama 3 n/a n/a no first-party price no first-party price

Verified 11 August 2026 against each vendor's own documentation: OpenAI's pricing and models pages, Google's Gemini API pricing and its Gemini 3.6 Flash model page, and Meta's Model API pricing.
Selection: current general-purpose text models, not the full catalogue. Each vendor sells cheaper small models and more expensive reasoning tiers; this table exists to make the shape of the market legible, not to be exhaustive.
Context figures: Google publishes an exact input limit of 1,048,576 tokens and an output limit of 65,536; OpenAI publishes 1.05M and 128K. The first two are the same number written two ways.
Cutoffs: OpenAI publishes a knowledge cutoff of 16 February 2026 for the GPT-5.6 family. Google's model page for Gemini 3.6 Flash does not publish one, which is why there is no cutoff column here.
Tiering: Google prices some models differently above and below a prompt-size threshold, and offers batch, flex and priority rates that differ substantially from the standard rate quoted. Meta publishes a cheaper "contributor" tier. Anyone budgeting seriously should read the vendor's own page rather than this row.

Two honest gaps in the table above

Meta doesn't fit the shape of this table, and that's the finding rather than an omission. Llama models are downloaded and run by you or by whichever host you choose, so Meta publishes no per-token price for them at all; what you pay depends entirely on where you run them. Meta's paid API sells different models (the Muse family), and its published pricing page gives rates without stating context or output limits, so those two cells say "not published" rather than carrying a number from somewhere else.

A price comparison across vendors is less meaningful than it looks. Identical per-token rates do not imply identical cost for the same job, because tokenisers differ, so the same text is a different number of tokens for different vendors. Treat the columns as order-of-magnitude guidance, not a league table. → Tokens

What changes, and how fast

Rough guide to how much you should trust a figure of a given age:

Concepts behind these numbers → Context windows