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. | — | 671B |
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 | 128k |
| API pricingUSD per 1M tokens · lower wins | ||
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. | — | $0.27SiliconFlow |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | — | $1.00SiliconFlow |
| — | $104SiliconFlow · fp8 | |
| Benchmarks | ||
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. | 95.5% | 68.4% |
SWE-Bench MultilingualMultilingual coding — Like SWE-Bench, but the coding problems span many programming languages, not just one. Tests how broadly the AI can code. Higher is better. | — | 57.8% |
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. | — | 74.9% |
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. | 88% | — |
Terminal-Bench 2.0Agentic terminal coding — Can the AI work in a command-line terminal — running commands and finishing technical setup tasks the way a developer would? (Version 2.0 of the test.) Higher is better. | 88% | — |
BrowseCompWeb browsing — Can the AI browse the web and track down hard-to-find answers? Higher is better. | 88% | — |
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. | 64.5% | — |
GPQA DiamondScience — Graduate-level science questions in biology, physics, and chemistry — hard enough that subject-matter PhDs score around 65%. Higher is better. | — | 80.7% |
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. | 85% | — |
| Overview | ||
| Company | Anthropic | DeepSeek |
| Release date | Jun 9 2026 | Sep 22 2025 |
| Access | Closed | Open Weight |
| Model details | View model | View model |
Other comparisons
Claude Mythos 5vsGPT-6.1 SolDeepSeek V3.1 TerminusvsGPT-6.1 SolClaude Mythos 5vsGemini 4 ArgonDeepSeek V3.1 TerminusvsGemini 4 ArgonClaude Mythos 5vsMuse Spark OpenDeepSeek V3.1 TerminusvsMuse Spark OpenClaude Mythos 5vsGrok 4.7DeepSeek V3.1 TerminusvsGrok 4.7Claude Mythos 5vsMistral Large 4DeepSeek V3.1 TerminusvsMistral Large 4Claude Mythos 5vsKimi K3DeepSeek V3.1 TerminusvsKimi K3Frequently asked questions
Claude Mythos 5 leads DeepSeek V3.1 Terminus on 1 of the 1 benchmark they both report (SWE-Bench Verified). DeepSeek V3.1 Terminus shipped 260 days before Claude Mythos 5, so benchmark comparisons should account for the intervening progress.
Context windows are 1M (Claude Mythos 5) vs 128k (DeepSeek V3.1 Terminus). Claude Mythos 5 is closed, while DeepSeek V3.1 Terminus is open weight.
On SWE-Bench Verified, Claude Mythos 5 leads at 95.5% vs DeepSeek V3.1 Terminus at 68.4%.