Compare AI models
| Specifications | ||
ParametersA rough measure of how big the model is. More parameters usually means more capable and more expensive to run, though it is a poor guide on its own — a smaller, newer model often beats a larger, older one. | — | 355B |
Context windowHow much text the model can hold in mind at once — your question, any documents you attach, the conversation so far, and its own reply. Go past it and the earliest part falls out of view. | 1.05M | 200k |
| 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. | $2.00 | $0.60 |
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. | $10.00 | $2.20 |
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. | $0.10 | $0.11 |
Cheapest inputLowest input rate across third-party providers, excluding the lab itself. The cheapest endpoint may run a quantised build or a shorter context — see "Available from" on the model page. | $1.00OpenAI | $0.43Venice |
Cheapest outputLowest output rate across third-party providers, excluding the lab itself. May come from a different provider than the cheapest input. | $5.00OpenAI | $1.75Venice |
These models have no shared benchmark scores. | ||
BullshitBench v2Nonsense detection — Given a confidently-worded but nonsensical prompt, does the AI spot that it makes no sense and push back — instead of playing along and inventing an answer? The score is how often it clearly called out the nonsense. Higher is better. | 65% | — |
Auto-review circumvention (Internal)Safety-review circumvention — OpenAI's internal safety check on how often a model finds ways around its own automated review — the guardrail that inspects what it is about to do. This one counts failures, so lower is better and zero is the goal. | 0% | — |
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. | — | 68% |
DeepSWE 1.1Agentic coding — Artificial Analysis' independent test of deep, agentic software-engineering work — the AI has to plan and carry out substantial coding tasks end to end. (Version 1.1 of the test.) Higher is better. | 75.2% | — |
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. | 57.1% | — |
OSWorld 2.0Agentic computer use — Can the AI actually operate a computer — clicking, typing, and using real apps — to finish tasks on its own? Version 2.0 is a harder, refreshed task set. Higher is better. | 71.4% | — |
AutomationBenchBusiness workflows — Tests whether the AI can run real multi-step business workflows — the kind of end-to-end office processes companies want to automate — from start to finish. Higher is better. | 36.2% | — |
GDP.PDFDocument comprehension — Real professional PDFs — filings, reports, technical documents — with questions an expert in that field would ask. Tests whether the AI reads the page as a document, layout and figures included, rather than as loose text. Higher is better. | 32% | — |
| Overview | ||
| Company | OpenAI | Z.ai |
| Release date | Sep 29 2026 | Sep 30 2025 |
| Access | Closed | Open Weight |
| Model details | View model | View model |
Other comparisons
GPT-6.1 SolvsClaude Sonnet 5.5GLM-4.6vsClaude Sonnet 5.5GPT-6.1 SolvsGemini 4 ArgonGLM-4.6vsGemini 4 ArgonGPT-6.1 SolvsMuse Spark 1.3GLM-4.6vsMuse Spark 1.3GPT-6.1 SolvsGrok 4.7GLM-4.6vsGrok 4.7GPT-6.1 SolvsDeepSeek-V4.1-FlashGLM-4.6vsDeepSeek-V4.1-FlashGPT-6.1 SolvsMistral Medium 3.5GLM-4.6vsMistral Medium 3.5Frequently asked questions
GPT-6.1 Sol and GLM-4.6 don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. GLM-4.6 is cheaper on both input and output: $0.60 vs $2.00 per million input tokens, and $2.20 vs $10.00 per million output tokens. GLM-4.6 shipped 364 days before GPT-6.1 Sol, so benchmark comparisons should account for the intervening progress.
Context windows are 1.05M (GPT-6.1 Sol) vs 200k (GLM-4.6). GPT-6.1 Sol is closed, while GLM-4.6 is open weight.
Direct benchmark comparisons are unavailable — GPT-6.1 Sol and GLM-4.6 don't publish scores on any of the same benchmarks.