Compare AI models
| Specifications | ||
ParametersThe learned values that shape how a model responds. B means billion and T means trillion. For mixture-of-experts models, the total count is larger than the number active for each token. Parameter count alone does not measure quality. | — | 284B |
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 | — |
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 | — |
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. | — | $0.0434Wafer |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | — | $0.14Relace |
| $3,600Anthropic list price | $21.00Relace · fp4 | |
| 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. | — | 39% |
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. | — | 79% |
Terminal-Bench 2.1Agentic terminal coding — Can the AI work in a command-line terminal — running commands and finishing technical setup tasks the way a developer would? Higher is better. | — | 82.7% |
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% | — |
Toolathlon-VerifiedPersonal tool use — Tests how well the AI uses everyday personal tools and apps to get things done — a human-checked version of Toolathlon. Higher is better. | — | 70.3% |
BrowseCompWeb browsing — Can the AI browse the web and track down hard-to-find answers? Higher is better. | — | 73.2% |
CyberGymCybersecurity — Tests the AI on cybersecurity challenges — finding and exploiting software weaknesses inside a safe sandbox. Higher is better. | — | 76.7% |
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 · with toolsMultidisciplinary reasoning — Humanity's Last Exam — extremely hard expert questions across many subjects. “With tools” means the AI is allowed to search the web or run code while answering. Higher is better. | — | 34.8% |
ARC-AGI-2Abstract reasoning — Puzzle-style tests of abstract reasoning and pattern-finding — the kind of thing people find easy but AIs often struggle with. Higher is better. | — | 61.4% |
AutomationBenchBusiness workflows — Tests whether the AI can run real multi-step business workflows — the kind of end-to-end office processes companies want to automate — from start to finish. Higher is better. | — | 25.1% |
| Overview | ||
| Company | Anthropic | DeepSeek |
| Release date | Nov 9 2026 | Jul 31 2026 |
| Access | Closed | Open Weight |
| Model details | View model | View model |
Other comparisons
Claude Mythos 5.2vsGPT-6.1 TerraDeepSeek-V4-Flash-0731vsGPT-6.1 TerraClaude Mythos 5.2vsGemini 4 CarbonDeepSeek-V4-Flash-0731vsGemini 4 CarbonClaude Mythos 5.2vsMuse EmberDeepSeek-V4-Flash-0731vsMuse EmberClaude Mythos 5.2vsGrok 4.7DeepSeek-V4-Flash-0731vsGrok 4.7Claude Mythos 5.2vsMistral Large 4DeepSeek-V4-Flash-0731vsMistral Large 4Claude Mythos 5.2vsKimi K3.1 ProDeepSeek-V4-Flash-0731vsKimi K3.1 ProFrequently asked questions
Claude Mythos 5.2 and DeepSeek-V4-Flash-0731 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 DeepSeek-V4-Flash-0731. Claude Mythos 5.2 requires organization verification. DeepSeek-V4-Flash-0731 shipped 101 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 (DeepSeek-V4-Flash-0731). Claude Mythos 5.2 is closed, while DeepSeek-V4-Flash-0731 is open weight.
Direct benchmark comparisons are unavailable — Claude Mythos 5.2 and DeepSeek-V4-Flash-0731 don't publish scores on any of the same benchmarks.