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 | 2M |
| 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 | — |
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 | — |
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 | — |
| $3,600Anthropic list price | — | |
| These models have no shared benchmark scores. | ||
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. | — | 34.3% |
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% | — |
MATHReasoning — accuracy on the original competition mathematics test set | — | 86.5% |
GPQA DiamondScience — Graduate-level science questions in biology, physics, and chemistry — hard enough that subject-matter PhDs score around 65%. Higher is better. | — | 59.1% |
MMLU-ProKnowledge — accuracy on the ten-option MMLU-Pro benchmark | — | 75.8% |
MMMUMultimodal — Tests the AI on understanding images and text together across many college subjects. Higher is better. | — | 65.9% |
| Overview | ||
| Company | Anthropic | |
| Release date | Nov 9 2026 | Sep 24 2024 |
| Access | Closed | Closed |
| Model details | View model | View model |
Other comparisons
Claude Mythos 5.2vsGPT-6.1 TerraGemini 1.5 Pro-002vsGPT-6.1 TerraClaude Mythos 5.2vsMuse EmberGemini 1.5 Pro-002vsMuse EmberClaude Mythos 5.2vsGrok 4.7Gemini 1.5 Pro-002vsGrok 4.7Claude Mythos 5.2vsDeepSeek-V4.1-Flash-1017Gemini 1.5 Pro-002vsDeepSeek-V4.1-Flash-1017Claude Mythos 5.2vsMistral Large 4Gemini 1.5 Pro-002vsMistral Large 4Claude Mythos 5.2vsKimi K3.1 ProGemini 1.5 Pro-002vsKimi K3.1 ProFrequently asked questions
Claude Mythos 5.2 and Gemini 1.5 Pro-002 don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Only Claude Mythos 5.2 has a verified first-party price for an available API: $8.00 per million input tokens and $40.00 per million output tokens. No currently available pay-as-you-go API rate is tracked for Gemini 1.5 Pro-002. Claude Mythos 5.2 requires organization verification. Gemini 1.5 Pro-002 shipped 776 days before Claude Mythos 5.2, so benchmark comparisons should account for the intervening progress.
Context windows are 1M (Claude Mythos 5.2) vs 2M (Gemini 1.5 Pro-002).
Direct benchmark comparisons are unavailable — Claude Mythos 5.2 and Gemini 1.5 Pro-002 don't publish scores on any of the same benchmarks.