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. | — | 32B |
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 | 128k |
| 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 | — |
| $9,000OpenAI list price | — | |
| These models have no shared benchmark scores. | ||
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. | — | 63.4% |
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% | — |
| Overview | ||
| Company | OpenAI | Qwen |
| Release date | Nov 5 2026 | Mar 6 2025 |
| Access | Closed | Open Weight |
| Model details | View model | View model |
Other comparisons
GPT-6 CybervsClaude Haiku 5.5QwQ-32BvsClaude Haiku 5.5GPT-6 CybervsGemini 4 CarbonQwQ-32BvsGemini 4 CarbonGPT-6 CybervsMuse Spark OpenQwQ-32BvsMuse Spark OpenGPT-6 CybervsGrok 4.7QwQ-32BvsGrok 4.7GPT-6 CybervsDeepSeek-V4.1-Flash-1017QwQ-32BvsDeepSeek-V4.1-Flash-1017GPT-6 CybervsMistral Large 4QwQ-32BvsMistral Large 4Frequently asked questions
GPT-6 Cyber and QwQ-32B 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 QwQ-32B. QwQ-32B shipped 609 days before GPT-6 Cyber, so benchmark comparisons should account for the intervening progress.
Context windows are 1.05M (GPT-6 Cyber) vs 128k (QwQ-32B). GPT-6 Cyber is closed, while QwQ-32B is open weight.
Direct benchmark comparisons are unavailable — GPT-6 Cyber and QwQ-32B don't publish scores on any of the same benchmarks.