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. | 40.6% | 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. | 41.6% | 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. | 50% | — |
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 | Oct 22 2024 | Sep 22 2025 |
| Access | Closed | Open Weight |
| Model details | View model | View model |
Other comparisons
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DeepSeek V3.1 Terminus leads Claude 3.5 Haiku on 2 of the 2 benchmarks they both report (SWE-Bench Verified, GPQA Diamond). Claude 3.5 Haiku shipped 335 days before DeepSeek V3.1 Terminus, so benchmark comparisons should account for the intervening progress.
Context windows are 200k (Claude 3.5 Haiku) vs 128k (DeepSeek V3.1 Terminus). Claude 3.5 Haiku is closed, while DeepSeek V3.1 Terminus is open weight.
On SWE-Bench Verified, DeepSeek V3.1 Terminus leads at 68.4% vs Claude 3.5 Haiku at 40.6%. On GPQA Diamond, DeepSeek V3.1 Terminus leads at 80.7% vs Claude 3.5 Haiku at 41.6%.