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 | 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 | 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. | — | $0.57Novita |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | — | $2.30Novita |
| — | $229Novita · fp8 | |
| These models have no shared benchmark scores. | ||
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. | — | 10% |
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. | — | 65.8% |
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. | — | 4.7% |
GPQA DiamondScience — Graduate-level science questions in biology, physics, and chemistry — hard enough that subject-matter PhDs score around 65%. Higher is better. | — | 75.1% |
| Overview | ||
| Company | DeepSeek | Moonshot AI |
| Release date | Feb 5 2024 | Jul 11 2025 |
| Access | Open Weight | Open Weight |
| Model details | View model | View model |
Other comparisons
DeepSeek-MathvsClaude Haiku 5.5Kimi K2vsClaude Haiku 5.5DeepSeek-MathvsGPT-6.1 SolKimi K2vsGPT-6.1 SolDeepSeek-MathvsGemini 4 ArgonKimi K2vsGemini 4 ArgonDeepSeek-MathvsMuse Spark OpenKimi K2vsMuse Spark OpenDeepSeek-MathvsGrok 4.7Kimi K2vsGrok 4.7DeepSeek-MathvsMistral Large 4Kimi K2vsMistral Large 4Frequently asked questions
DeepSeek-Math and Kimi K2 don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. DeepSeek-Math shipped 522 days before Kimi K2, so benchmark comparisons should account for the intervening progress.
DeepSeek-Math has 7B parameters, while Kimi K2 has 1T. Context windows are 4k (DeepSeek-Math) vs 128k (Kimi K2).
Direct benchmark comparisons are unavailable — DeepSeek-Math and Kimi K2 don't publish scores on any of the same benchmarks.