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. | 1T | >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. | $0.57Novita | — |
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 | Moonshot AI | Qwen |
| Release date | Jul 11 2025 | Sep 24 2025 |
| Access | Open Weight | Closed |
| Model details | View model | View model |
Other comparisons
Kimi K2vsClaude Haiku 5.5Qwen3-MaxvsClaude Haiku 5.5Kimi K2vsGPT-6.1 TerraQwen3-MaxvsGPT-6.1 TerraKimi K2vsGemini 4 CarbonQwen3-MaxvsGemini 4 CarbonKimi K2vsMuse Spark OpenQwen3-MaxvsMuse Spark OpenKimi K2vsGrok 4.7Qwen3-MaxvsGrok 4.7Kimi K2vsDeepSeek-V4.1-Flash-1017Qwen3-MaxvsDeepSeek-V4.1-Flash-1017Frequently asked questions
Kimi K2 and Qwen3-Max don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Kimi K2 shipped 75 days before Qwen3-Max, so benchmark comparisons should account for the intervening progress.
Kimi K2 has 1T parameters, while Qwen3-Max has >1T. Context windows are 128k (Kimi K2) vs 256k (Qwen3-Max). Kimi K2 is open weight, while Qwen3-Max is closed.
Direct benchmark comparisons are unavailable — Kimi K2 and Qwen3-Max don't publish scores on any of the same benchmarks.