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 | 48B |
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. | 128k | 1M |
| 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% | — |
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 | DeepSeek | Moonshot AI |
| Release date | Sep 22 2025 | Oct 30 2025 |
| Access | Open Weight | Open Weight |
| Model details | View model | View model |
Other comparisons
DeepSeek V3.1 TerminusvsClaude Haiku 5.5Kimi LinearvsClaude Haiku 5.5DeepSeek V3.1 TerminusvsGPT-6.1 SolKimi LinearvsGPT-6.1 SolDeepSeek V3.1 TerminusvsGemini 4 ArgonKimi LinearvsGemini 4 ArgonDeepSeek V3.1 TerminusvsMuse Spark OpenKimi LinearvsMuse Spark OpenDeepSeek V3.1 TerminusvsGrok 4.7Kimi LinearvsGrok 4.7DeepSeek V3.1 TerminusvsMistral Large 4Kimi LinearvsMistral Large 4Frequently asked questions
DeepSeek V3.1 Terminus and Kimi Linear don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. DeepSeek V3.1 Terminus shipped 38 days before Kimi Linear, so benchmark comparisons should account for the intervening progress.
DeepSeek V3.1 Terminus has 671B parameters, while Kimi Linear has 48B. Context windows are 128k (DeepSeek V3.1 Terminus) vs 1M (Kimi Linear).
Direct benchmark comparisons are unavailable — DeepSeek V3.1 Terminus and Kimi Linear don't publish scores on any of the same benchmarks.