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. | 1M | 1M |
| 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. | $0.10Azure | — |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | $0.40Azure | — |
| $40.00Azure | — | |
| These models have no shared benchmark scores. | ||
Aider Polyglot (diff)Coding — editing code in six languages using the diff edit format | 6.2% | — |
HumanEvalFunction synthesis — 164 small Python problems: the AI is given a function's description and has to write the working function. This was the coding benchmark of the GPT-3.5 and GPT-4 era, before the field moved to fixing real bugs in real repositories. Higher is better. | 87% | — |
IFEvalAgentic tasks — following verifiable instructions in generated responses | 74.5% | — |
MATH-500Reasoning — accuracy on the 500-problem MATH subset | 62.3% | — |
DROPReasoning — three-shot token F1 on questions requiring reasoning over paragraphs | 82.2 | — |
AIME 2024Reasoning — single-answer accuracy on the 2024 invitational mathematics exam without tools | 29.4% | — |
GPQA DiamondScience — Graduate-level science questions in biology, physics, and chemistry — hard enough that subject-matter PhDs score around 65%. Higher is better. | 50.3% | — |
SimpleQAKnowledge — accuracy on short, fact-seeking questions in the original SimpleQA set | 7.6% | — |
MMMLUKnowledge — average accuracy on multilingual translations of MMLU | 66.9% | — |
MMLUGeneral knowledge — A 57-subject multiple-choice exam — history, law, medicine, maths — that was the standard measure of how much a model knows from 2020 until roughly 2024, when frontier scores crowded into the high 80s and labs moved on to harder tests. The scores here were published years apart under different testing setups, so read them as a historical record rather than a like-for-like ranking. Higher is better. | 80.1% | — |
MGSMMultilingual math — solving grade-school word problems translated into multiple languages | 73% | — |
CharXiv ReasoningChart reasoning — Can the AI read and reason about complex charts and figures, not just text? Higher is better. | 40.5% | — |
MathVistaMultimodal reasoning — mathematical reasoning about visual information | 56.2% | — |
MMMUMultimodal — Tests the AI on understanding images and text together across many college subjects. Higher is better. | 55.4% | — |
GraphWalks BFS (<128K)Long context — breadth-first traversal accuracy for inputs shorter than 128K tokens | 25% | — |
GraphWalks parents (<128K)Long context — parent-node retrieval accuracy for inputs shorter than 128K tokens | 9.4% | — |
OpenAI MRCR (2 needles) · 128KLong context — retrieval of two matching turns in the original MRCR evaluation | 36.6% | — |
OpenAI MRCR (2 needles) · 1MLong context — retrieval of two matching turns in the original MRCR evaluation | 12% | — |
| Overview | ||
| Company | OpenAI | Qwen |
| Release date | Apr 14 2025 | Nov 11 2026 |
| Access | Closed | Closed |
| Model details | View model | View model |
Other comparisons
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GPT-4.1 nano and Qwen4-Max Preview don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. GPT-4.1 nano shipped 576 days before Qwen4-Max Preview, so benchmark comparisons should account for the intervening progress.
Context windows are 1M (GPT-4.1 nano) vs 1M (Qwen4-Max Preview).
Direct benchmark comparisons are unavailable — GPT-4.1 nano and Qwen4-Max Preview don't publish scores on any of the same benchmarks.