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. | $0.25DeepInfra | — |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | $0.95DeepInfra | — |
| $97.50DeepInfra · fp4 | — | |
| 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. | 66% | — |
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. | 74.8% | — |
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. | 15.9% | — |
| Overview | ||
| Company | DeepSeek | Moonshot AI |
| Release date | Aug 21 2025 | Oct 30 2025 |
| Access | Open Weight | Open Weight |
| Model details | View model | View model |
Other comparisons
DeepSeek V3.1vsClaude Haiku 5.5Kimi LinearvsClaude Haiku 5.5DeepSeek V3.1vsGPT-6.1 SolKimi LinearvsGPT-6.1 SolDeepSeek V3.1vsGemini 4 ArgonKimi LinearvsGemini 4 ArgonDeepSeek V3.1vsMuse Spark OpenKimi LinearvsMuse Spark OpenDeepSeek V3.1vsGrok 4.7Kimi LinearvsGrok 4.7DeepSeek V3.1vsMistral Large 4Kimi LinearvsMistral Large 4Frequently asked questions
DeepSeek V3.1 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 shipped 70 days before Kimi Linear, so benchmark comparisons should account for the intervening progress.
DeepSeek V3.1 has 671B parameters, while Kimi Linear has 48B. Context windows are 128k (DeepSeek V3.1) vs 1M (Kimi Linear).
Direct benchmark comparisons are unavailable — DeepSeek V3.1 and Kimi Linear don't publish scores on any of the same benchmarks.