Gemini 3.0 Provsgpt-oss-20b
Gemini 3.0 Pro | gpt-oss-20b | |
|---|---|---|
| Specifications | ||
ParametersA rough measure of how big the model is. More parameters usually means more capable and more expensive to run, though it is a poor guide on its own — a smaller, newer model often beats a larger, older one. | — | 21B |
Context windowHow much text the model can hold in mind at once — your question, any documents you attach, the conversation so far, and its own reply. Go past it and the earliest part falls out of view. | — | 128k |
| API pricingUSD per 1M tokens · lower wins | ||
Cheapest inputLowest input rate across third-party providers, excluding the lab itself. The cheapest endpoint may run a quantised build or a shorter context — see "Available from" on the model page. | — | $0.03CoreWeave |
Cheapest outputLowest output rate across third-party providers, excluding the lab itself. May come from a different provider than the cheapest input. | — | $0.13CoreWeave |
| Benchmarks | ||
SWE-Bench VerifiedCoding — Real coding tasks pulled from open-source projects — the AI has to find and fix actual bugs. A human-checked version of the original SWE-Bench. Higher is better. | 76.2% | 60.7% |
GPQA DiamondScience — Graduate-level science questions in biology, physics, and chemistry — hard enough that subject-matter PhDs score around 65%. Higher is better. | 91.9% | 71.5% |
| BenchmarksPublished by one model only | ||
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. | 62% | — |
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. | — | 10.9% |
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. | — | 17.3% |
ARC-AGI-2Abstract reasoning — Puzzle-style tests of abstract reasoning and pattern-finding — the kind of thing people find easy but AIs often struggle with. Higher is better. | 31.1% | — |
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. | — | 85.3% |
MMMUMultimodal — Tests the AI on understanding images and text together across many college subjects. Higher is better. | 81% | — |
| Overview | ||
| Company | OpenAI | |
| Release date | Nov 18 2025 | Aug 5 2025 |
| Access | Proprietary | Open Weight |
Other comparisons
Frequently asked questions
Gemini 3.0 Pro leads gpt-oss-20b on 2 of the 2 benchmarks they both report (SWE-Bench Verified, GPQA Diamond). gpt-oss-20b shipped 105 days before Gemini 3.0 Pro, so benchmark comparisons should account for the intervening progress.
Gemini 3.0 Pro is proprietary, while gpt-oss-20b is open weight.
On SWE-Bench Verified, Gemini 3.0 Pro leads at 76.2% vs gpt-oss-20b at 60.7%. On GPQA Diamond, Gemini 3.0 Pro leads at 91.9% vs gpt-oss-20b at 71.5%.
Gemini 3.0 Pro was released by Google on Nov 18 2025.
gpt-oss-20b was released by OpenAI on Aug 5 2025.
Gemini 3.0 Pro leads on SWE-Bench Verified — Gemini 3.0 Pro 76.2% vs gpt-oss-20b 60.7%.
Gemini 3.0 Pro leads on GPQA Diamond — Gemini 3.0 Pro 91.9% vs gpt-oss-20b 71.5%.
Gemini 3.0 Pro is a proprietary model released by Google. gpt-oss-20b is an open weight model released by OpenAI.