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. | 200k | 1M |
| API pricingUSD per 1M tokens · lower wins | ||
Input priceWhat you pay for everything you send the model — your question, plus any documents or earlier conversation you include with it. | $2.00 | — |
Output priceWhat you pay for the text the model writes back. It is normally the dearer half: producing an answer costs more than reading one. | $8.00 | — |
Cached input priceA reduced rate for text you send over and over. If every request starts with the same instructions or the same document, the provider keeps a copy ready and charges less to read it again. | $0.50 | — |
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. | $2.00OpenAI | — |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | $8.00OpenAI | — |
| $800OpenAI list price | — | |
| 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. | 26% | — |
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. | 87.4% | — |
MATH-500Reasoning — accuracy on the 500-problem MATH subset | 97.8% | — |
DROPReasoning — three-shot token F1 on questions requiring reasoning over paragraphs | 80.6 | — |
GPQA DiamondScience — Graduate-level science questions in biology, physics, and chemistry — hard enough that subject-matter PhDs score around 65%. Higher is better. | 82.8% | — |
SimpleQAKnowledge — accuracy on short, fact-seeking questions in the original SimpleQA set | 49.4% | — |
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. | 92.9% | — |
MGSMMultilingual math — solving grade-school word problems translated into multiple languages | 92.3% | — |
| Overview | ||
| Company | OpenAI | Qwen |
| Release date | Apr 16 2025 | Nov 11 2026 |
| Access | Closed | Closed |
| Model details | View model | View model |
Other comparisons
o3vsClaude Fable 5.2Qwen4-Max PreviewvsClaude Fable 5.2o3vsGemini 4 CarbonQwen4-Max PreviewvsGemini 4 Carbono3vsMuse EmberQwen4-Max PreviewvsMuse Embero3vsGrok 4.7Qwen4-Max PreviewvsGrok 4.7o3vsDeepSeek-V4.1-Flash-1017Qwen4-Max PreviewvsDeepSeek-V4.1-Flash-1017o3vsMistral Large 4Qwen4-Max PreviewvsMistral Large 4Frequently asked questions
o3 and Qwen4-Max Preview don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Only o3 has a verified first-party price for an available API: $2.00 per million input tokens and $8.00 per million output tokens. No currently available pay-as-you-go API rate is tracked for Qwen4-Max Preview. o3 shipped 574 days before Qwen4-Max Preview, so benchmark comparisons should account for the intervening progress.
Context windows are 200k (o3) vs 1M (Qwen4-Max Preview).
Direct benchmark comparisons are unavailable — o3 and Qwen4-Max Preview don't publish scores on any of the same benchmarks.