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. | 128B | — |
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. | 256k | 1.05M |
| 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. | $1.50 | $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. | $7.50 | $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. | $0.15 | $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. | $1.50Mistral | — |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | $7.50Mistral | — |
| $675Mistral list price | $9,000OpenAI list price | |
| 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. | 77.6% | — |
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% |
| Overview | ||
| Company | Mistral | OpenAI |
| Release date | Apr 29 2026 | Nov 5 2026 |
| Access | Open Weight | Closed |
| Model details | View model | View model |
Other comparisons
Mistral Medium 3.5vsClaude Haiku 5.5GPT-6 CybervsClaude Haiku 5.5Mistral Medium 3.5vsGemini 4 CarbonGPT-6 CybervsGemini 4 CarbonMistral Medium 3.5vsMuse Spark OpenGPT-6 CybervsMuse Spark OpenMistral Medium 3.5vsGrok 4.7GPT-6 CybervsGrok 4.7Mistral Medium 3.5vsDeepSeek-V4.1-Flash-1017GPT-6 CybervsDeepSeek-V4.1-Flash-1017Mistral Medium 3.5vsKimi K3.1 ProGPT-6 CybervsKimi K3.1 ProFrequently asked questions
Mistral Medium 3.5 and GPT-6 Cyber don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Mistral Medium 3.5 is cheaper on both input and output: $1.50 vs $20.00 per million input tokens, and $7.50 vs $100.00 per million output tokens. Mistral Medium 3.5 shipped 190 days before GPT-6 Cyber, so benchmark comparisons should account for the intervening progress.
Context windows are 256k (Mistral Medium 3.5) vs 1.05M (GPT-6 Cyber). Mistral Medium 3.5 is open weight, while GPT-6 Cyber is closed.
Direct benchmark comparisons are unavailable — Mistral Medium 3.5 and GPT-6 Cyber don't publish scores on any of the same benchmarks.