Compare AI models
| Specifications | ||
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. | 2M | 1M |
| These models have no shared benchmark scores. | ||
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. | 36% | — |
MATHReasoning — accuracy on the original competition mathematics test set | 91.8% | — |
GPQA DiamondScience — Graduate-level science questions in biology, physics, and chemistry — hard enough that subject-matter PhDs score around 65%. Higher is better. | 64.7% | — |
SimpleQAKnowledge — accuracy on short, fact-seeking questions in the original SimpleQA set | 44.3% | — |
MMLU-ProKnowledge — accuracy on the ten-option MMLU-Pro benchmark | 79.1% | — |
MMMUMultimodal — Tests the AI on understanding images and text together across many college subjects. Higher is better. | 72.7% | — |
| Overview | ||
| Company | Qwen | |
| Release date | Feb 5 2025 | Nov 11 2026 |
| Access | Closed | Closed |
| Model details | View model | View model |
Other comparisons
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Gemini 2.0 Pro and Qwen4-Max Preview don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Gemini 2.0 Pro shipped 644 days before Qwen4-Max Preview, so benchmark comparisons should account for the intervening progress.
Context windows are 2M (Gemini 2.0 Pro) vs 1M (Qwen4-Max Preview).
Direct benchmark comparisons are unavailable — Gemini 2.0 Pro and Qwen4-Max Preview don't publish scores on any of the same benchmarks.