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 | |
| 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. | — | 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 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% | — |
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% |
| Overview | ||
| Company | Anthropic | DeepSeek |
| Release date | Sep 1 2026 | Sep 22 2025 |
| Access | Closed | Open Weight |
| Model details | View model | View model |
Other comparisons
Claude Mythos 5.1vsGPT-6.1 SolDeepSeek V3.1 TerminusvsGPT-6.1 SolClaude Mythos 5.1vsGemini 4 ArgonDeepSeek V3.1 TerminusvsGemini 4 ArgonClaude Mythos 5.1vsMuse Spark OpenDeepSeek V3.1 TerminusvsMuse Spark OpenClaude Mythos 5.1vsGrok 4.7DeepSeek V3.1 TerminusvsGrok 4.7Claude Mythos 5.1vsMistral Large 4DeepSeek V3.1 TerminusvsMistral Large 4Claude Mythos 5.1vsKimi K3DeepSeek V3.1 TerminusvsKimi K3Frequently asked questions
Claude Mythos 5.1 and DeepSeek V3.1 Terminus don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. DeepSeek V3.1 Terminus shipped 344 days before Claude Mythos 5.1, so benchmark comparisons should account for the intervening progress.
Context windows are 1M (Claude Mythos 5.1) vs 128k (DeepSeek V3.1 Terminus). Claude Mythos 5.1 is closed, while DeepSeek V3.1 Terminus is open weight.
Direct benchmark comparisons are unavailable — Claude Mythos 5.1 and DeepSeek V3.1 Terminus don't publish scores on any of the same benchmarks.