Compare AI models
| Specifications | ||
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 |
| 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. | 32% | — |
HumanEvalFunction synthesis — 164 small Python problems: the AI is given a function's description and has to write the working function. This was the coding benchmark of the GPT-3.5 and GPT-4 era, before the field moved to fixing real bugs in real repositories. Higher is better. | — | 99.3% |
MATH-500Reasoning — accuracy on the 500-problem MATH subset | — | 98.2% |
DROPReasoning — three-shot token F1 on questions requiring reasoning over paragraphs | — | 78.1 |
GPQA DiamondScience — Graduate-level science questions in biology, physics, and chemistry — hard enough that subject-matter PhDs score around 65%. Higher is better. | — | 81.3% |
SimpleQAKnowledge — accuracy on short, fact-seeking questions in the original SimpleQA set | — | 19.3% |
MMLUGeneral knowledge — A 57-subject multiple-choice exam — history, law, medicine, maths — that was the standard measure of how much a model knows from 2020 until roughly 2024, when frontier scores crowded into the high 80s and labs moved on to harder tests. The scores here were published years apart under different testing setups, so read them as a historical record rather than a like-for-like ranking. Higher is better. | — | 90.3% |
MGSMMultilingual math — solving grade-school word problems translated into multiple languages | — | 93.5% |
| Overview | ||
| Company | OpenAI | OpenAI |
| Release date | Nov 13 2025 | Apr 16 2025 |
| Access | Closed | Closed |
| Model details | View model | View model |
Other comparisons
GPT-5.1-CodexvsClaude Fable 5.2o4-mini-highvsClaude Fable 5.2GPT-5.1-CodexvsGemini 4 Carbono4-mini-highvsGemini 4 CarbonGPT-5.1-CodexvsMuse Embero4-mini-highvsMuse EmberGPT-5.1-CodexvsGrok 4.7o4-mini-highvsGrok 4.7GPT-5.1-CodexvsDeepSeek-V4.1-Flash-1017o4-mini-highvsDeepSeek-V4.1-Flash-1017GPT-5.1-CodexvsMistral Large 4o4-mini-highvsMistral Large 4Frequently asked questions
GPT-5.1-Codex and o4-mini-high don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. o4-mini-high shipped 211 days before GPT-5.1-Codex, so benchmark comparisons should account for the intervening progress.
Context windows are 400k (GPT-5.1-Codex) vs 200k (o4-mini-high).
Direct benchmark comparisons are unavailable — GPT-5.1-Codex and o4-mini-high don't publish scores on any of the same benchmarks.