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. | — | 1.05M |
| API pricingUSD per 1M tokens · lower wins · base tier | ||
Input priceWhat you pay for everything you send the model — your question, plus any documents or earlier conversation you include with it. | — | $1.00 |
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. | — | $5.00 |
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. | — | $0.05 |
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. | $0.04Darkbloom | $0.50OpenAI |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | $0.10Makora | $2.50OpenAI |
| $14.35PrimeIntellect · bf16 | $225OpenAI | |
| Benchmarks | ||
MMMU-ProMultimodal reasoning — A tougher version of MMMU — college-level questions that mix images, diagrams, and text together. Higher is better. | 76.9% | 85.5% |
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% | — |
OpenAI Factuality (Internal)Factual error rate — OpenAI's internal test uses prompts designed to provoke factual mistakes. The score is the percentage of answers containing at least one factual error, at the model's best published reasoning effort. It is not the percentage of individual claims that are wrong or an error rate for everyday use. Lower is better. | — | 4.3% |
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. | — | 100% |
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% | — |
Terminal-Bench 4.0Agentic terminal coding — Can the AI work in a command-line terminal — running commands and finishing technical setup tasks the way a developer would? Version 4.0 recalibrated how much time, CPU and memory each task gets, removed eight tasks and fixed nineteen, so fewer runs fail for reasons that have nothing to do with the model. Scores are not comparable with earlier versions. Higher is better. | — | 50.3% |
Terminal-Bench-Science 0.1Agentic scientific computing — The same command-line setup as Terminal-Bench, pointed at scientific work: the AI has to drive research tooling and computational workflows through to a result, rather than administer a machine. Version 0.1 is the first release of the task set, and scores run lower than on the general board. Higher is better. | — | 58.3% |
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% | — |
OSWorld 2.1 · offlineAgentic computer use — Can the AI actually operate a computer — clicking, typing, and using real apps — to finish tasks on its own? Version 2.1, in its offline setting. Scores are not comparable with version 2.0. Higher is better. | — | 74.2% |
Agent's Last Exam · pass@1Agentic computer use — A hard set of desktop and operating-system tasks an AI agent has to finish by looking at the screen and working the machine itself. The score is the share it passes outright — partial credit does not count. Higher is better. | — | 60% |
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 | Oct 26 2026 |
| Access | Open Weight | Closed |
| Model details | View model | View model |
Other comparisons
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GPT-6.1 Terra leads Gemma 4 on 1 of the 1 benchmark they both report (MMMU-Pro). Only GPT-6.1 Terra has a verified first-party price for an available API: $1.00 per million input tokens and $5.00 per million output tokens. No currently available pay-as-you-go API rate is tracked for Gemma 4. Gemma 4 shipped 207 days before GPT-6.1 Terra, so benchmark comparisons should account for the intervening progress.
Gemma 4 is open weight, while GPT-6.1 Terra is closed.
On MMMU-Pro, GPT-6.1 Terra leads at 85.5% vs Gemma 4 at 76.9%.