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. | — | 106B |
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 | 128k |
| 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. | — | $0.20 |
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. | — | $1.10 |
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. | — | $0.03 |
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. | — | $0.13Novita |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | — | $0.85Novita |
| — | $68.50Novita · bf16 | |
| 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. | 45% | — |
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. | — | 59.8% |
Next.js EvalsNext.js coding — Vercel's open eval of how well AI coding agents build and migrate real Next.js apps — measured as the share of tasks the agent completes successfully. Higher is better. | 46% | — |
| Overview | ||
| Company | OpenAI | Z.ai |
| Release date | Dec 18 2025 | Jul 28 2025 |
| Access | Closed | Open Weight |
| Model details | View model | View model |
Other comparisons
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GPT-5.2-Codex and GLM-4.5-Air don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Only GLM-4.5-Air has a verified first-party price for an available API: $0.20 per million input tokens and $1.10 per million output tokens. No currently available pay-as-you-go API rate is tracked for GPT-5.2-Codex. GLM-4.5-Air shipped 143 days before GPT-5.2-Codex, so benchmark comparisons should account for the intervening progress.
Context windows are 400k (GPT-5.2-Codex) vs 128k (GLM-4.5-Air). GPT-5.2-Codex is closed, while GLM-4.5-Air is open weight.
Direct benchmark comparisons are unavailable — GPT-5.2-Codex and GLM-4.5-Air don't publish scores on any of the same benchmarks.