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 | 1M |
| 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. | $8.00Verified organizations only | $0.10 |
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. | $40.00 | $0.40 |
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. | $0.20 | $0.01 |
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. | — | $0.05Google AI Studio |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | — | $0.20Google AI Studio |
| $3,600Anthropic list price | $40.00Google list price | |
| These models have no shared benchmark scores. | ||
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. | — | 31.6% |
LiveCodeBenchCompetitive coding — Coding problems published so recently the AI can't have seen them in training — a contamination-free test of raw programming skill. Higher is better. | — | 33.7% |
Terminal-Bench-Science 0.1Agentic scientific computing — The same command-line setup as Terminal-Bench, pointed at scientific work: the AI has to drive research tooling and computational workflows through to a result, rather than administer a machine. Version 0.1 is the first release of the task set, and scores run lower than on the general board. Higher is better. | 75.3% | — |
De novo binder design (24 hr)Protein design — de novo protein binder design within a 24-hour budget, as reported by Anthropic | 85.1% | — |
CyScenarioBenchCybersecurity — multi-step cybersecurity scenarios; each lab runs its own unique task set, so scores from different labs do not compare | 76.3% | — |
CVE RediscoveryCybersecurity — Anthropic internal evaluation: share of 1,829 published CVEs a model finds on its own from a snapshot of the project before the fix | 89.6% | — |
Humanity's Last Exam · no toolsMultidisciplinary reasoning — Humanity's Last Exam — extremely hard expert questions across many subjects, written so you can't just look up the answer. “No tools” means the AI answers on its own. Higher is better. | — | 5.1% |
AIME 2025Reasoning — single-answer accuracy on the 2025 invitational mathematics exam without tools | — | 49.8% |
GPQA DiamondScience — Graduate-level science questions in biology, physics, and chemistry — hard enough that subject-matter PhDs score around 65%. Higher is better. | — | 64.6% |
SimpleQAKnowledge — accuracy on short, fact-seeking questions in the original SimpleQA set | — | 10.7% |
MMMUMultimodal — Tests the AI on understanding images and text together across many college subjects. Higher is better. | — | 72.9% |
MRCR v2 (8-needle) · 128k averageLong context — Tests whether the AI can find specific details buried inside a very long document (around 128k tokens — roughly a long book). Higher is better. | — | 16.6% |
MRCR v2 (8-needle) · 1M pointwiseLong context — Tests whether the AI can find specific details buried inside an enormous document (around 1 million tokens — many books). Higher is better. | — | 4.1% |
| Overview | ||
| Company | Anthropic | |
| Release date | Nov 9 2026 | Jun 17 2025 |
| Access | Closed | Closed |
| Model details | View model | View model |
Other comparisons
Claude Mythos 5.2vsGPT-6.1 TerraGemini 2.5 Flash-LitevsGPT-6.1 TerraClaude Mythos 5.2vsMuse EmberGemini 2.5 Flash-LitevsMuse EmberClaude Mythos 5.2vsGrok 4.7Gemini 2.5 Flash-LitevsGrok 4.7Claude Mythos 5.2vsDeepSeek-V4.1-Flash-1017Gemini 2.5 Flash-LitevsDeepSeek-V4.1-Flash-1017Claude Mythos 5.2vsMistral Large 4Gemini 2.5 Flash-LitevsMistral Large 4Claude Mythos 5.2vsKimi K3.1 ProGemini 2.5 Flash-LitevsKimi K3.1 ProFrequently asked questions
Claude Mythos 5.2 and Gemini 2.5 Flash-Lite don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Gemini 2.5 Flash-Lite is cheaper on both input and output: $0.10 vs $8.00 per million input tokens, and $0.40 vs $40.00 per million output tokens. Claude Mythos 5.2 requires organization verification. Gemini 2.5 Flash-Lite shipped 510 days before Claude Mythos 5.2, so benchmark comparisons should account for the intervening progress.
Context windows are 1M (Claude Mythos 5.2) vs 1M (Gemini 2.5 Flash-Lite).
Direct benchmark comparisons are unavailable — Claude Mythos 5.2 and Gemini 2.5 Flash-Lite don't publish scores on any of the same benchmarks.