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. | 1M | 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. | $8.00Verified organizations only | $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. | $40.00 | $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. | $0.20 | $2.00 |
| $3,600Anthropic list price | $9,000OpenAI list price | |
| These models have no shared benchmark scores. | ||
Terminal-Bench-Science 0.1Agentic scientific computing — The same command-line setup as Terminal-Bench, pointed at scientific work: the AI has to drive research tooling and computational workflows through to a result, rather than administer a machine. Version 0.1 is the first release of the task set, and scores run lower than on the general board. Higher is better. | 75.3% | — |
De novo binder design (24 hr)Protein design — de novo protein binder design within a 24-hour budget, as reported by Anthropic | 85.1% | — |
CyScenarioBenchCybersecurity — multi-step cybersecurity scenarios; each lab runs its own unique task set, so scores from different labs do not compare | 76.3% | — |
CVE RediscoveryCybersecurity — Anthropic internal evaluation: share of 1,829 published CVEs a model finds on its own from a snapshot of the project before the fix | 89.6% | — |
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 | Anthropic | OpenAI |
| Release date | Nov 9 2026 | Nov 5 2026 |
| Access | Closed | Closed |
| Model details | View model | View model |
Other comparisons
Claude Mythos 5.2vsGemini 4 CarbonGPT-6 CybervsGemini 4 CarbonClaude Mythos 5.2vsMuse EmberGPT-6 CybervsMuse EmberClaude Mythos 5.2vsGrok 4.7GPT-6 CybervsGrok 4.7Claude Mythos 5.2vsDeepSeek-V4.1-Flash-1017GPT-6 CybervsDeepSeek-V4.1-Flash-1017Claude Mythos 5.2vsMistral Large 4GPT-6 CybervsMistral Large 4Claude Mythos 5.2vsKimi K3.1 ProGPT-6 CybervsKimi K3.1 ProFrequently asked questions
Claude Mythos 5.2 and GPT-6 Cyber don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Claude Mythos 5.2 is cheaper on both input and output: $8.00 vs $20.00 per million input tokens, and $40.00 vs $100.00 per million output tokens. Claude Mythos 5.2 requires organization verification. GPT-6 Cyber shipped 4 days before Claude Mythos 5.2, so benchmark comparisons should account for the intervening progress.
Context windows are 1M (Claude Mythos 5.2) vs 1.05M (GPT-6 Cyber).
Direct benchmark comparisons are unavailable — Claude Mythos 5.2 and GPT-6 Cyber don't publish scores on any of the same benchmarks.