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. | 5.1B/8B/26B/31B | — |
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 |
| API pricingUSD per 1M tokens · lower wins | ||
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. | $0.012PrimeIntellect | — |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | $0.037PrimeIntellect | — |
| $4.25PrimeIntellect · bf16 | — | |
| Benchmarks | ||
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. | 25% | 32% |
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. | 80% | — |
Humanity's Last Exam · no toolsMultidisciplinary reasoning — Humanity's Last Exam — extremely hard expert questions across many subjects, written so you can't just look up the answer. “No tools” means the AI answers on its own. Higher is better. | 19.5% | — |
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. | 26.5% | — |
AIME 2026Reasoning — single-answer accuracy on the 2026 invitational mathematics exam without tools | 89.2% | — |
GPQA DiamondScience — Graduate-level science questions in biology, physics, and chemistry — hard enough that subject-matter PhDs score around 65%. Higher is better. | 84.3% | — |
MMMLUKnowledge — average accuracy on multilingual translations of MMLU | 88.4% | — |
MMLU-ProKnowledge — accuracy on the ten-option MMLU-Pro benchmark | 85.2% | — |
MMMU-ProMultimodal reasoning — A tougher version of MMMU — college-level questions that mix images, diagrams, and text together. Higher is better. | 76.9% | — |
MRCR v2 (8-needle) · 128k averageLong context — Tests whether the AI can find specific details buried inside a very long document (around 128k tokens — roughly a long book). Higher is better. | 66.4% | — |
| Overview | ||
| Company | OpenAI | |
| Release date | Apr 2 2026 | Nov 13 2025 |
| Access | Open Weight | Closed |
| Model details | View model | View model |
Other comparisons
Gemma 4vsClaude Fable 5.2GPT-5.1-CodexvsClaude Fable 5.2Gemma 4vsMuse EmberGPT-5.1-CodexvsMuse EmberGemma 4vsGrok 4.7GPT-5.1-CodexvsGrok 4.7Gemma 4vsDeepSeek-V4.1-Flash-1017GPT-5.1-CodexvsDeepSeek-V4.1-Flash-1017Gemma 4vsMistral Large 4GPT-5.1-CodexvsMistral Large 4Gemma 4vsKimi K3.1 ProGPT-5.1-CodexvsKimi K3.1 ProFrequently asked questions
GPT-5.1-Codex leads Gemma 4 on 1 of the 1 benchmark they both report (BullshitBench v2). GPT-5.1-Codex shipped 140 days before Gemma 4, so benchmark comparisons should account for the intervening progress.
Gemma 4 is open weight, while GPT-5.1-Codex is closed.
On BullshitBench v2, GPT-5.1-Codex leads at 32% vs Gemma 4 at 25%.