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. | 7B/67B | 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. | 4k | 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 | Nov 29 2023 | Nov 6 2025 |
| Access | Open Weight | Open Weight |
| Model details | View model | View model |
Other comparisons
DeepSeek-LLMvsClaude Haiku 5.5Kimi K2 ThinkingvsClaude Haiku 5.5DeepSeek-LLMvsGPT-6.1 SolKimi K2 ThinkingvsGPT-6.1 SolDeepSeek-LLMvsGemini 4 ArgonKimi K2 ThinkingvsGemini 4 ArgonDeepSeek-LLMvsMuse Spark OpenKimi K2 ThinkingvsMuse Spark OpenDeepSeek-LLMvsGrok 4.7Kimi K2 ThinkingvsGrok 4.7DeepSeek-LLMvsMistral Large 4Kimi K2 ThinkingvsMistral Large 4Frequently asked questions
DeepSeek-LLM and Kimi K2 Thinking don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. DeepSeek-LLM shipped 708 days before Kimi K2 Thinking, so benchmark comparisons should account for the intervening progress.
DeepSeek-LLM has 7B/67B parameters, while Kimi K2 Thinking has 1T. Context windows are 4k (DeepSeek-LLM) vs 256k (Kimi K2 Thinking).
Direct benchmark comparisons are unavailable — DeepSeek-LLM and Kimi K2 Thinking don't publish scores on any of the same benchmarks.