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. | 400k | 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. | $0.25 | — |
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. | $2.00 | — |
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. | $0.025 | — |
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. | $0.125OpenAI | — |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | $1.00OpenAI | — |
| $150OpenAI list price | — | |
| These models have no shared benchmark scores. | ||
ProgramBenchProgram reconstruction — The AI receives a working program and its documentation, then builds a replacement from scratch without the original source code, internet access or decompilation. The score is the percentage of 200 programs that pass every behavioral test. We record each model's best published mini-SWE-agent result, including higher reasoning efforts where available. Partial test-pass rates and almost-solved programs do not count toward this score. Equal scores share a rank here; the official board also uses partial progress to break ties. Higher is better. | 0% | — |
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. | 71% | — |
Aider Polyglot (diff)Coding — editing code in six languages using the diff edit format | 71.6% | — |
τ²-bench · retailAgentic tasks — task success in the retail domain of the second-generation customer-service evaluation | 78.3% | — |
τ²-bench · airlineAgentic tasks — task success in the airline domain of the second-generation customer-service evaluation | 60% | — |
τ²-bench · telecomAgentic tasks — task success in the telecom domain of the second-generation customer-service evaluation | 74.1% | — |
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. | 16.7% | — |
AIME 2025Reasoning — single-answer accuracy on the 2025 invitational mathematics exam without tools | 91.1% | — |
GPQA DiamondScience — Graduate-level science questions in biology, physics, and chemistry — hard enough that subject-matter PhDs score around 65%. Higher is better. | 82.3% | — |
HealthBench · hardHealthcare — physician rubric score on the hard subset of the original HealthBench evaluation | 40.3% | — |
CharXiv Reasoning · with toolsChart reasoning — The same chart-and-figure reasoning test, run with the AI allowed to use tools — writing code to inspect the image, for instance — rather than reading the chart unaided. Scores run higher than the unaided version, so read the two as separate tests. Higher is better. | 75.5% | — |
MMMU-Pro · standard and vision meanMultimodal reasoning — average across the standard and vision sets | 74.1% | — |
MMMUMultimodal — Tests the AI on understanding images and text together across many college subjects. Higher is better. | 81.6% | — |
GraphWalks BFS (<128K)Long context — breadth-first traversal accuracy for inputs shorter than 128K tokens | 73.4% | — |
GraphWalks parents (<128K)Long context — parent-node retrieval accuracy for inputs shorter than 128K tokens | 64.3% | — |
OpenAI MRCR (2 needles) · 128KLong context — retrieval of two matching turns in the original MRCR evaluation | 84.3% | — |
OpenAI MRCR (2 needles) · 256KLong context — retrieval of two matching turns in the original MRCR evaluation | 58.8% | — |
| Overview | ||
| Company | OpenAI | Qwen |
| Release date | Aug 7 2025 | Nov 11 2026 |
| Access | Closed | Closed |
| Model details | View model | View model |
Other comparisons
GPT-5 minivsClaude Fable 5.2Qwen4-Max PreviewvsClaude Fable 5.2GPT-5 minivsGemini 4 CarbonQwen4-Max PreviewvsGemini 4 CarbonGPT-5 minivsMuse EmberQwen4-Max PreviewvsMuse EmberGPT-5 minivsGrok 4.7Qwen4-Max PreviewvsGrok 4.7GPT-5 minivsDeepSeek-V4.1-Flash-1017Qwen4-Max PreviewvsDeepSeek-V4.1-Flash-1017GPT-5 minivsMistral Large 4Qwen4-Max PreviewvsMistral Large 4Frequently asked questions
GPT-5 mini and Qwen4-Max Preview don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Only GPT-5 mini has a verified first-party price for an available API: $0.25 per million input tokens and $2.00 per million output tokens. No currently available pay-as-you-go API rate is tracked for Qwen4-Max Preview. GPT-5 mini shipped 461 days before Qwen4-Max Preview, so benchmark comparisons should account for the intervening progress.
Context windows are 400k (GPT-5 mini) vs 1M (Qwen4-Max Preview).
Direct benchmark comparisons are unavailable — GPT-5 mini and Qwen4-Max Preview don't publish scores on any of the same benchmarks.