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. | — | 744B |
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. | 400k | 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. | — | $1.00 |
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. | — | $3.20 |
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. | — | $0.20 |
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. | $1.25Azure | $0.60GMICloud |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | $10.00Azure | $1.92GMICloud |
| $750Azure | $216StreamLake · fp8 | |
| 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. | — | 28% |
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. | — | 77.8% |
SWE-Bench MultilingualMultilingual coding — Like SWE-Bench, but the coding problems span many programming languages, not just one. Tests how broadly the AI can code. Higher is better. | — | 73.3% |
Terminal-Bench 2.0Agentic terminal coding — Can the AI work in a command-line terminal — running commands and finishing technical setup tasks the way a developer would? (Version 2.0 of the test.) Higher is better. | — | 56.2% |
BrowseCompWeb browsing — Can the AI browse the web and track down hard-to-find answers? Higher is better. | — | 75.9% |
Humanity's Last Exam · with toolsMultidisciplinary reasoning — Humanity's Last Exam — extremely hard expert questions across many subjects. “With tools” means the AI is allowed to search the web or run code while answering. Higher is better. | — | 50.4% |
GPQA DiamondScience — Graduate-level science questions in biology, physics, and chemistry — hard enough that subject-matter PhDs score around 65%. Higher is better. | — | 86% |
| Overview | ||
| Company | OpenAI | Z.ai |
| Release date | Nov 19 2025 | Feb 12 2026 |
| Access | Closed | Open Weight |
| Model details | View model | View model |
Other comparisons
GPT-5.1-Codex-MaxvsClaude Haiku 5.5GLM-5vsClaude Haiku 5.5GPT-5.1-Codex-MaxvsGemini 4 ArgonGLM-5vsGemini 4 ArgonGPT-5.1-Codex-MaxvsMuse Spark OpenGLM-5vsMuse Spark OpenGPT-5.1-Codex-MaxvsGrok 4.7GLM-5vsGrok 4.7GPT-5.1-Codex-MaxvsDeepSeek-V4.1-Flash-1017GLM-5vsDeepSeek-V4.1-Flash-1017GPT-5.1-Codex-MaxvsMistral Large 4GLM-5vsMistral Large 4Frequently asked questions
GPT-5.1-Codex-Max and GLM-5 don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Only GLM-5 has a verified first-party API price: $1.00 per million input tokens and $3.20 per million output tokens. No pay-as-you-go API rate is tracked for GPT-5.1-Codex-Max. GPT-5.1-Codex-Max shipped 85 days before GLM-5, so benchmark comparisons should account for the intervening progress.
Context windows are 400k (GPT-5.1-Codex-Max) vs 200k (GLM-5). GPT-5.1-Codex-Max is closed, while GLM-5 is open weight.
Direct benchmark comparisons are unavailable — GPT-5.1-Codex-Max and GLM-5 don't publish scores on any of the same benchmarks.