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. | 236B | 1T |
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 | 256k |
| 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. | — | $0.60Google |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | — | $2.50Google |
| — | $245Novita · bf16 | |
| 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. | — | 71.3% |
| Overview | ||
| Company | DeepSeek | Moonshot AI |
| Release date | May 6 2024 | Nov 6 2025 |
| Access | Open Weight | Open Weight |
| Model details | View model | View model |
Other comparisons
DeepSeek V2vsClaude Haiku 5.5Kimi K2 ThinkingvsClaude Haiku 5.5DeepSeek V2vsGPT-6.1 SolKimi K2 ThinkingvsGPT-6.1 SolDeepSeek V2vsGemini 4 ArgonKimi K2 ThinkingvsGemini 4 ArgonDeepSeek V2vsMuse Spark OpenKimi K2 ThinkingvsMuse Spark OpenDeepSeek V2vsGrok 4.7Kimi K2 ThinkingvsGrok 4.7DeepSeek V2vsMistral Large 4Kimi K2 ThinkingvsMistral Large 4Frequently asked questions
DeepSeek V2 and Kimi K2 Thinking don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. DeepSeek V2 shipped 549 days before Kimi K2 Thinking, so benchmark comparisons should account for the intervening progress.
DeepSeek V2 has 236B parameters, while Kimi K2 Thinking has 1T. Context windows are 128k (DeepSeek V2) vs 256k (Kimi K2 Thinking).
Direct benchmark comparisons are unavailable — DeepSeek V2 and Kimi K2 Thinking don't publish scores on any of the same benchmarks.