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. | 16B | 480B |
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. | — | $0.30DeepInfra |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | — | $1.00DeepInfra |
| — | $110DeepInfra · turbo · fp4 | |
| 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. | — | 20% |
| Overview | ||
| Company | Moonshot AI | Qwen |
| Release date | Apr 10 2025 | Jul 22 2025 |
| Access | Open Weight | Open Weight |
| Model details | View model | View model |
Other comparisons
Kimi-VLvsClaude Haiku 5.5Qwen3-CodervsClaude Haiku 5.5Kimi-VLvsGPT-6.1 TerraQwen3-CodervsGPT-6.1 TerraKimi-VLvsGemini 4 CarbonQwen3-CodervsGemini 4 CarbonKimi-VLvsMuse Spark OpenQwen3-CodervsMuse Spark OpenKimi-VLvsGrok 4.7Qwen3-CodervsGrok 4.7Kimi-VLvsDeepSeek-V4.1-Flash-1017Qwen3-CodervsDeepSeek-V4.1-Flash-1017Frequently asked questions
Kimi-VL and Qwen3-Coder don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Kimi-VL shipped 103 days before Qwen3-Coder, so benchmark comparisons should account for the intervening progress.
Kimi-VL has 16B parameters, while Qwen3-Coder has 480B. Context windows are 128k (Kimi-VL) vs 256k (Qwen3-Coder).
Direct benchmark comparisons are unavailable — Kimi-VL and Qwen3-Coder don't publish scores on any of the same benchmarks.