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. | 200k | 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. | 72.7% | 68.4% |
GPQA DiamondScience — Graduate-level science questions in biology, physics, and chemistry — hard enough that subject-matter PhDs score around 65%. Higher is better. | 75.4% | 80.7% |
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. | 30% | — |
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% |
| Overview | ||
| Company | Anthropic | DeepSeek |
| Release date | May 22 2025 | Sep 22 2025 |
| Access | Closed | Open Weight |
| Model details | View model | View model |
Other comparisons
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Claude Sonnet 4 and DeepSeek V3.1 Terminus are evenly matched across the 2 benchmarks they both report (SWE-Bench Verified, GPQA Diamond). Claude Sonnet 4 shipped 123 days before DeepSeek V3.1 Terminus, so benchmark comparisons should account for the intervening progress.
Context windows are 200k (Claude Sonnet 4) vs 128k (DeepSeek V3.1 Terminus). Claude Sonnet 4 is closed, while DeepSeek V3.1 Terminus is open weight.
On SWE-Bench Verified, Claude Sonnet 4 leads at 72.7% vs DeepSeek V3.1 Terminus at 68.4%. On GPQA Diamond, DeepSeek V3.1 Terminus leads at 80.7% vs Claude Sonnet 4 at 75.4%.