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. | — | 397B |
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 · base tier | ||
Input priceWhat you pay for everything you send the model — your question, plus any documents or earlier conversation you include with it. | $20.00 | $0.40 |
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 | $1.60 |
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. | — | $0.32Alibaba |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | — | $1.28Alibaba |
| $9,000OpenAI list price | $128Alibaba | |
| These models have no shared benchmark scores. | ||
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. | — | 77.7% |
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. | — | 89.6% |
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. | — | 34.7% |
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. | — | 73.3% |
| Overview | ||
| Company | OpenAI | Qwen |
| Release date | Nov 5 2026 | Jun 1 2026 |
| Access | Closed | Closed |
| Model details | View model | View model |
Other comparisons
GPT-6 CybervsClaude Haiku 5.5Qwen3.7-PlusvsClaude Haiku 5.5GPT-6 CybervsGemini 4 CarbonQwen3.7-PlusvsGemini 4 CarbonGPT-6 CybervsMuse Spark OpenQwen3.7-PlusvsMuse Spark OpenGPT-6 CybervsGrok 4.7Qwen3.7-PlusvsGrok 4.7GPT-6 CybervsDeepSeek-V4.1-Flash-1017Qwen3.7-PlusvsDeepSeek-V4.1-Flash-1017GPT-6 CybervsMistral Large 4Qwen3.7-PlusvsMistral Large 4Frequently asked questions
GPT-6 Cyber and Qwen3.7-Plus don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Qwen3.7-Plus is cheaper on both input and output: $0.40 vs $20.00 per million input tokens, and $1.60 vs $100.00 per million output tokens. Figures are base-tier rates. Qwen3.7-Plus shipped 157 days before GPT-6 Cyber, so benchmark comparisons should account for the intervening progress.
Context windows are 1.05M (GPT-6 Cyber) vs 1M (Qwen3.7-Plus).
Direct benchmark comparisons are unavailable — GPT-6 Cyber and Qwen3.7-Plus don't publish scores on any of the same benchmarks.