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. | — | 2.4T |
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. | 1.05M | 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. | $20.00 | — |
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. | $100.00 | — |
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. | $2.00 | — |
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. | — | $2.00Alibaba |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | — | $6.00Alibaba |
| $9,000OpenAI list price | $700Alibaba | |
| 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. | — | 95% |
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. | — | 67.7% |
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. | — | 56.6% |
PaperBenchResearch reproduction — reproducing the results of an ML research paper end to end | — | 93% |
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. | — | 55.9% |
QwenSWEBench V2Software engineering — Qwen's in-house coding benchmark, second version, built around complex real-world software-engineering tasks. Scores are not comparable with the first version. Higher is better. | — | 55.1% |
Terminal-Bench 3.0Agentic terminal coding — command-line task completion (v3.0, much harder task set) | — | 11.3% |
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. | — | 86.6% |
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. | — | 53.4% |
CoWorkBenchLong-horizon office work — Tests long-running office tasks across fields including computer science, finance, law, medicine, and other productivity work. Higher is better. | — | 74.8% |
Toolathlon-VerifiedPersonal tool use — Tests how well the AI uses everyday personal tools and apps to get things done — a human-checked version of Toolathlon. Higher is better. | — | 72.5% |
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. | — | 43.6% |
ExploitGym (remote target)Cybersecurity — Can the model exploit a vulnerability it was told about, on a remote target, and show that it used that vulnerability? OpenAI reported this as a percentage at the GPT-6 Cyber launch. It is a different measurement from the timed ExploitGym pass rates and from the count of solved instances. Higher is better. | 48.5% | — |
ExploitGym HardenedCybersecurity — The same kind of intended-exploit test as ExploitGym on a remote target, with stronger defenses turned on, such as address randomization, stack canaries, the V8 sandbox, and stricter kernel settings. Higher is better. | 36.94% | — |
RemoteExploitBenchCybersecurity — Can the model finish an exploit against a known vulnerability in a real application — such as GitLab, PostgreSQL, or Jenkins — running as a remote target in a controlled lab? The score is the share of tasks completed. Higher is better. | 95% | — |
ExploitGym Honeypot · without deployment promptScope adherence — The model is given a hard exploitation task and an easy out-of-scope service it could attack instead. This score is how often it leaves that service alone when no deployment safety prompt is added. Higher is better. | 99.93% | — |
ExploitGym Honeypot · with deployment promptScope adherence — The same honeypot test, with the deployment safety prompt included. The score is how often the model leaves the out-of-scope service alone. Higher is better. | 100% | — |
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. | — | 86.1% |
CharXiv ReasoningChart reasoning — Can the AI read and reason about complex charts and figures, not just text? Higher is better. | — | 88.4% |
BabyVisionVisual reasoning — Tests core visual reasoning — seeing and understanding images the way even young children can, which AIs often find surprisingly hard. Higher is better. | — | 82% |
MMMU-ProMultimodal reasoning — A tougher version of MMMU — college-level questions that mix images, diagrams, and text together. Higher is better. | — | 82.3% |
| Overview | ||
| Company | OpenAI | Qwen |
| Release date | Nov 5 2026 | Aug 3 2026 |
| Access | Closed | Closed |
| Model details | View model | View model |
Other comparisons
GPT-6 CybervsClaude Haiku 5.5Qwen3.8-MaxvsClaude Haiku 5.5GPT-6 CybervsGemini 4 CarbonQwen3.8-MaxvsGemini 4 CarbonGPT-6 CybervsMuse Spark OpenQwen3.8-MaxvsMuse Spark OpenGPT-6 CybervsGrok 4.7Qwen3.8-MaxvsGrok 4.7GPT-6 CybervsDeepSeek-V4.1-Flash-1017Qwen3.8-MaxvsDeepSeek-V4.1-Flash-1017GPT-6 CybervsMistral Large 4Qwen3.8-MaxvsMistral Large 4Frequently asked questions
GPT-6 Cyber and Qwen3.8-Max don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Only GPT-6 Cyber has a verified first-party price for an available API: $20.00 per million input tokens and $100.00 per million output tokens. No currently available pay-as-you-go API rate is tracked for Qwen3.8-Max. Qwen3.8-Max shipped 94 days before GPT-6 Cyber, so benchmark comparisons should account for the intervening progress.
Context windows are 1.05M (GPT-6 Cyber) vs 1M (Qwen3.8-Max).
Direct benchmark comparisons are unavailable — GPT-6 Cyber and Qwen3.8-Max don't publish scores on any of the same benchmarks.