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 · base tier | ||
Input priceWhat you pay for everything you send the model — your question, plus any documents or earlier conversation you include with it. | $0.10 | $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. | $0.50 | $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. | $0.004 | $2.00 |
| $45.00Meta list price | $9,000OpenAI list price | |
| These models have no shared benchmark scores. | ||
AA Coding Agent IndexAgentic coding — Artificial Analysis' overall score for coding agents, combining three coding benchmarks with what each run costs and how many tokens it burns. It rates a model paired with a particular agent harness rather than the model alone, so the same model scores differently in different tools. Higher is better. | 46 | — |
Terminal-Bench 4.0Agentic terminal coding — Can the AI work in a command-line terminal — running commands and finishing technical setup tasks the way a developer would? Version 4.0 recalibrated how much time, CPU and memory each task gets, removed eight tasks and fixed nineteen, so fewer runs fail for reasons that have nothing to do with the model. Scores are not comparable with earlier versions. Higher is better. | 40.9% | — |
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% |
AutomationBench (Artificial Analysis)Business workflows — The same business-workflow test, run by Artificial Analysis rather than by the lab, and graded strict pass@1: a workflow counts only if the whole of it is right on the first attempt. Read it as a separate board from the lab-run AutomationBench scores. Higher is better. | 37.8% | — |
AA Intelligence Index v5.0Overall intelligence — Artificial Analysis composite intelligence index, version 5.0 with tools; separate from earlier index editions | 36 | — |
GDP.PDF (Artificial Analysis)Document comprehension — The same professional-PDF test, run by Artificial Analysis rather than by the lab. Read it as a separate board from the lab-run GDP.PDF scores. Higher is better. | 22.8% | — |
| Overview | ||
| Company | Meta | OpenAI |
| Release date | Nov 9 2026 | Nov 5 2026 |
| Access | Closed | Closed |
| Model details | View model | View model |
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Muse Ember and GPT-6 Cyber don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Muse Ember is cheaper on both input and output: $0.10 vs $20.00 per million input tokens, and $0.50 vs $100.00 per million output tokens. Figures are base-tier rates. GPT-6 Cyber shipped 4 days before Muse Ember, so benchmark comparisons should account for the intervening progress.
Context windows are 1M (Muse Ember) vs 1.05M (GPT-6 Cyber).
Direct benchmark comparisons are unavailable — Muse Ember and GPT-6 Cyber don't publish scores on any of the same benchmarks.