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. | 1.8B/3.25B | 27B |
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. | 32k | 262k |
| 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. | — | $0.039Chutes |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | — | $1.25Chutes |
| — | $70.30Chutes · 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. | — | 79% |
SWE-Bench ProAgentic coding — Can the AI fix real bugs in real software? It's handed actual problems from open-source projects and has to write code that genuinely solves them. Higher is better. | — | 61.7% |
DeepSWE 1.1Agentic coding — Artificial Analysis' independent test of deep, agentic software-engineering work — the AI has to plan and carry out substantial coding tasks end to end. (Version 1.1 of the test.) Higher is better. | — | 42.2% |
NL2Repo-BenchRepo-level code generation — Tests whether the AI can turn a natural-language requirement into working code across an entire repository, not just produce a single function or patch. Higher is better. | — | 42.3% |
QwenSWEBenchSoftware engineering — Qwen's in-house coding benchmark for evaluating a model's ability to complete software-engineering work. Higher is better. | — | 79% |
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. | — | 90.3% |
Terminal-Bench 2.1Agentic terminal coding — Can the AI work in a command-line terminal — running commands and finishing technical setup tasks the way a developer would? Higher is better. | — | 73% |
JobBenchProfessional tool use — Tests the AI on professional workplace tasks that require using real work tools — the kind of multi-step jobs an office worker handles. Higher is better. | — | 33.4% |
CoWorkBenchLong-horizon office work — Tests long-running office tasks across fields including computer science, finance, law, medicine, and other productivity work. Higher is better. | — | 70.7% |
Humanity's Last Exam · no toolsMultidisciplinary reasoning — Humanity's Last Exam — extremely hard expert questions across many subjects, written so you can't just look up the answer. “No tools” means the AI answers on its own. Higher is better. | — | 30.8% |
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. | — | 30.8% |
GPQA DiamondScience — Graduate-level science questions in biology, physics, and chemistry — hard enough that subject-matter PhDs score around 65%. Higher is better. | — | 89.2% |
IFBenchInstruction following — Tests whether the AI can follow detailed instructions and satisfy multiple constraints at once. Higher is better. | — | 79.5% |
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. | — | 84.3% |
Agent's Last Exam · pass@1Agentic computer use — A hard set of desktop and operating-system tasks an AI agent has to finish by looking at the screen and working the machine itself. The score is the share it passes outright — partial credit does not count. Higher is better. | — | 20.4% |
Agent's Last Exam · scoreAgentic computer use — The graded score on the same desktop and operating-system tasks in Agent's Last Exam, giving partial credit for progress beyond the strict pass-or-fail result. Higher is better. | — | 42.9% |
| Overview | ||
| Company | Qwen | |
| Release date | Dec 6 2023 | Aug 14 2026 |
| Access | Closed | Open Weight |
| Model details | View model | View model |
Other comparisons
Gemini 1.0 NanovsClaude Haiku 5.5Qwen3.8-27BvsClaude Haiku 5.5Gemini 1.0 NanovsGPT-6.1 SolQwen3.8-27BvsGPT-6.1 SolGemini 1.0 NanovsMuse Spark OpenQwen3.8-27BvsMuse Spark OpenGemini 1.0 NanovsGrok 4.7Qwen3.8-27BvsGrok 4.7Gemini 1.0 NanovsDeepSeek-V4.1-Flash-1017Qwen3.8-27BvsDeepSeek-V4.1-Flash-1017Gemini 1.0 NanovsMistral Large 4Qwen3.8-27BvsMistral Large 4Frequently asked questions
Gemini 1.0 Nano and Qwen3.8-27B don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Gemini 1.0 Nano shipped 982 days before Qwen3.8-27B, so benchmark comparisons should account for the intervening progress.
Gemini 1.0 Nano has 1.8B/3.25B parameters, while Qwen3.8-27B has 27B. Context windows are 32k (Gemini 1.0 Nano) vs 262k (Qwen3.8-27B). Gemini 1.0 Nano is closed, while Qwen3.8-27B is open weight.
Direct benchmark comparisons are unavailable — Gemini 1.0 Nano and Qwen3.8-27B don't publish scores on any of the same benchmarks.