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. | — | 32B |
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 |
| 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% | — |
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% | — |
LiveCodeBenchCompetitive coding — Coding problems published so recently the AI can't have seen them in training — a contamination-free test of raw programming skill. Higher is better. | — | 63.4% |
| Overview | ||
| Company | OpenAI | Qwen |
| Release date | Dec 18 2025 | Mar 6 2025 |
| Access | Closed | Open Weight |
| Model details | View model | View model |
Other comparisons
GPT-5.2-CodexvsClaude Fable 5.2QwQ-32BvsClaude Fable 5.2GPT-5.2-CodexvsGemini 4 CarbonQwQ-32BvsGemini 4 CarbonGPT-5.2-CodexvsMuse EmberQwQ-32BvsMuse EmberGPT-5.2-CodexvsGrok 4.7QwQ-32BvsGrok 4.7GPT-5.2-CodexvsDeepSeek-V4.1-Flash-1017QwQ-32BvsDeepSeek-V4.1-Flash-1017GPT-5.2-CodexvsMistral Large 4QwQ-32BvsMistral Large 4Frequently asked questions
GPT-5.2-Codex and QwQ-32B don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. QwQ-32B shipped 287 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 (QwQ-32B). GPT-5.2-Codex is closed, while QwQ-32B is open weight.
Direct benchmark comparisons are unavailable — GPT-5.2-Codex and QwQ-32B don't publish scores on any of the same benchmarks.