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 | 0.6B/1.7B/4B/8B/14B/30B/32B/235B |
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. | 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. | $0.49AtlasCloud | $0.08DeepInfra |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | $2.50AtlasCloud | $0.22NextBit |
| $223AtlasCloud · int4 | $30.00DeepInfra · fp8 | |
| Benchmarks | ||
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. | 52% | 6% |
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. | 76.8% | — |
Next.js EvalsNext.js coding — Vercel's open eval of how well AI coding agents build and migrate real Next.js apps — measured as the share of tasks the agent completes successfully. Higher is better. | 16% | — |
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. | 85% | — |
BrowseCompWeb browsing — Can the AI browse the web and track down hard-to-find answers? Higher is better. | 60.6% | — |
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. | 30.1% | — |
GPQA DiamondScience — Graduate-level science questions in biology, physics, and chemistry — hard enough that subject-matter PhDs score around 65%. Higher is better. | 87.6% | — |
OSWorld-VerifiedAgentic computer use — Can the AI actually operate a computer — clicking, typing, and using real apps — to finish tasks on its own? Higher is better. | 63.3% | — |
| Overview | ||
| Company | Moonshot AI | Qwen |
| Release date | Jan 27 2026 | Apr 29 2025 |
| Access | Open Weight | Open Weight |
| Model details | View model | View model |
Other comparisons
Kimi K2.5vsClaude Haiku 5.5Qwen3vsClaude Haiku 5.5Kimi K2.5vsGPT-6.1 TerraQwen3vsGPT-6.1 TerraKimi K2.5vsGemini 4 CarbonQwen3vsGemini 4 CarbonKimi K2.5vsMuse Spark OpenQwen3vsMuse Spark OpenKimi K2.5vsGrok 4.7Qwen3vsGrok 4.7Kimi K2.5vsDeepSeek-V4.1-Flash-1017Qwen3vsDeepSeek-V4.1-Flash-1017Frequently asked questions
Kimi K2.5 leads Qwen3 on 1 of the 1 benchmark they both report (BullshitBench v2). Qwen3 shipped 273 days before Kimi K2.5, so benchmark comparisons should account for the intervening progress.
Kimi K2.5 has 1T parameters, while Qwen3 has 0.6B/1.7B/4B/8B/14B/30B/32B/235B.
On BullshitBench v2, Kimi K2.5 leads at 52% vs Qwen3 at 6%.