Compare AI models
| Specifications | ||
Context windowHow much information the model can work with in one request, measured in tokens — pieces of words or other data. This includes your question, attachments, conversation and reply. Limits can vary by model variant and provider. | 1M | 400k |
| API pricingUSD per 1M tokens · lower wins | ||
Input priceWhat you pay for everything you send the model — your question, plus any documents or earlier conversation you include with it. | — | $1.75 |
Output priceWhat you pay for the text the model writes back. It is normally the dearer half: producing an answer costs more than reading one. | — | $14.00 |
Cached input priceA reduced rate for text you send over and over. If every request starts with the same instructions or the same document, the provider keeps a copy ready and charges less to read it again. | — | $0.175 |
Cheapest inputLowest input rate across every provider on OpenRouter, the lab included. The cheapest endpoint may run a quantised build or a shorter context — see "Available from" on the model page. | — | $1.75Azure |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | — | $14.00Azure |
| — | $1,050OpenAI list price | |
| These models have no shared benchmark scores. | ||
BullshitBench v2Nonsense detection — Given a confidently-worded but nonsensical prompt, does the AI spot that it makes no sense and push back — instead of playing along and inventing an answer? The score is how often it clearly called out the nonsense. Higher is better. | — | 24% |
SWE-Bench VerifiedCoding — Real coding tasks pulled from open-source projects — the AI has to find and fix actual bugs. A human-checked version of the original SWE-Bench. Higher is better. | — | 85% |
Next.js EvalsNext.js coding — Vercel's open eval of how well AI coding agents build and migrate real Next.js apps — measured as the share of tasks the agent completes successfully. Higher is better. | — | 68% |
Terminal-Bench 4.0Agentic terminal coding — Can the AI work in a command-line terminal — running commands and finishing technical setup tasks the way a developer would? Version 4.0 recalibrated how much time, CPU and memory each task gets, removed eight tasks and fixed nineteen, so fewer runs fail for reasons that have nothing to do with the model. Scores are not comparable with earlier versions. Higher is better. | 60.9% | — |
OSWorld-VerifiedAgentic computer use — Can the AI actually operate a computer — clicking, typing, and using real apps — to finish tasks on its own? Higher is better. | — | 64.7% |
threejsevalCommunity preference (Three.js) — Every model gets the same prompt — "the Eiffel Tower", "a glass fishbowl", "a robot arm picking toys into a box" — and builds a 3D scene in Three.js. Real people then see two scenes side by side, names hidden, and vote for the one they prefer. The votes become a chess-style Elo rating on threejseval.com, averaged across all the prompts. It measures whether the scene looks and moves right to a human eye, not whether the code passes a test. Higher is better. | — | 1171 |
| Overview | ||
| Company | Anthropic | OpenAI |
| Release date | Sep 1 2026 | Feb 5 2026 |
| Access | Closed | Closed |
| Model details | View model | View model |
Other comparisons
Claude Mythos 5.1vsGemini 4 ArgonGPT-5.3-CodexvsGemini 4 ArgonClaude Mythos 5.1vsMuse Spark OpenGPT-5.3-CodexvsMuse Spark OpenClaude Mythos 5.1vsGrok 4.7GPT-5.3-CodexvsGrok 4.7Claude Mythos 5.1vsDeepSeek-V4.1-Flash-1017GPT-5.3-CodexvsDeepSeek-V4.1-Flash-1017Claude Mythos 5.1vsMistral Large 4GPT-5.3-CodexvsMistral Large 4Claude Mythos 5.1vsKimi K3GPT-5.3-CodexvsKimi K3Frequently asked questions
Claude Mythos 5.1 and GPT-5.3-Codex don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Only GPT-5.3-Codex has a verified first-party API price: $1.75 per million input tokens and $14.00 per million output tokens. No pay-as-you-go API rate is tracked for Claude Mythos 5.1. GPT-5.3-Codex shipped 208 days before Claude Mythos 5.1, so benchmark comparisons should account for the intervening progress.
Context windows are 1M (Claude Mythos 5.1) vs 400k (GPT-5.3-Codex).
Direct benchmark comparisons are unavailable — Claude Mythos 5.1 and GPT-5.3-Codex don't publish scores on any of the same benchmarks.