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. | 1.3B/5.7B/6.7B/33B | 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. | 16k | 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 2 2023 | Nov 6 2025 |
| Access | Open Weight | Open Weight |
| Model details | View model | View model |
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DeepSeek Coder and Kimi K2 Thinking don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. DeepSeek Coder shipped 735 days before Kimi K2 Thinking, so benchmark comparisons should account for the intervening progress.
DeepSeek Coder has 1.3B/5.7B/6.7B/33B parameters, while Kimi K2 Thinking has 1T. Context windows are 16k (DeepSeek Coder) vs 256k (Kimi K2 Thinking).
Direct benchmark comparisons are unavailable — DeepSeek Coder and Kimi K2 Thinking don't publish scores on any of the same benchmarks.