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 | 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. | $0.50DeepInfra | $0.60Google |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | $2.15DeepInfra | $2.50Google |
| $208DeepInfra · fp4 | $245Novita · bf16 | |
| Benchmarks | ||
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. | 57.6% | 71.3% |
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. | 8% | — |
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. | 73.3% | — |
GPQA DiamondScience — Graduate-level science questions in biology, physics, and chemistry — hard enough that subject-matter PhDs score around 65%. Higher is better. | 81% | — |
| Overview | ||
| Company | DeepSeek | Moonshot AI |
| Release date | May 28 2025 | Nov 6 2025 |
| Access | Open Weight | Open Weight |
| Model details | View model | View model |
Other comparisons
DeepSeek R1-0528vsClaude Haiku 5.5Kimi K2 ThinkingvsClaude Haiku 5.5DeepSeek R1-0528vsGPT-6.1 SolKimi K2 ThinkingvsGPT-6.1 SolDeepSeek R1-0528vsGemini 4 ArgonKimi K2 ThinkingvsGemini 4 ArgonDeepSeek R1-0528vsMuse Spark OpenKimi K2 ThinkingvsMuse Spark OpenDeepSeek R1-0528vsGrok 4.7Kimi K2 ThinkingvsGrok 4.7DeepSeek R1-0528vsMistral Large 4Kimi K2 ThinkingvsMistral Large 4Frequently asked questions
Kimi K2 Thinking leads DeepSeek R1-0528 on 1 of the 1 benchmark they both report (SWE-Bench Verified). DeepSeek R1-0528 shipped 162 days before Kimi K2 Thinking, so benchmark comparisons should account for the intervening progress.
DeepSeek R1-0528 has 671B parameters, while Kimi K2 Thinking has 1T. Context windows are 128k (DeepSeek R1-0528) vs 256k (Kimi K2 Thinking).
On SWE-Bench Verified, Kimi K2 Thinking leads at 71.3% vs DeepSeek R1-0528 at 57.6%.