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. | 1.3B/5.7B/6.7B/33B | — |
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. | 16k | 400k |
| 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% |
| Overview | ||
| Company | DeepSeek | OpenAI |
| Release date | Nov 2 2023 | Dec 18 2025 |
| Access | Open Weight | Closed |
| Model details | View model | View model |
Other comparisons
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DeepSeek Coder and GPT-5.2-Codex don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. DeepSeek Coder shipped 777 days before GPT-5.2-Codex, so benchmark comparisons should account for the intervening progress.
Context windows are 16k (DeepSeek Coder) vs 400k (GPT-5.2-Codex). DeepSeek Coder is open weight, while GPT-5.2-Codex is closed.
Direct benchmark comparisons are unavailable — DeepSeek Coder and GPT-5.2-Codex don't publish scores on any of the same benchmarks.