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. | 117B | 0.6B/1.7B/4B/8B/14B/30B/32B/235B |
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. | 128k | — |
| 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. | $0.03AkashML | $0.08DeepInfra |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | $0.119PrimeIntellect | $0.22NextBit |
| $13.50Venice · int4 | $30.00DeepInfra · fp8 | |
| 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. | 12% | 6% |
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. | 62.4% | — |
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. | 14.9% | — |
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. | 19% | — |
AIME 2024Reasoning — single-answer accuracy on the 2024 invitational mathematics exam without tools | 95.8% | — |
AIME 2025Reasoning — single-answer accuracy on the 2025 invitational mathematics exam without tools | 92.5% | — |
GPQA DiamondScience — Graduate-level science questions in biology, physics, and chemistry — hard enough that subject-matter PhDs score around 65%. Higher is better. | 80.1% | — |
MMMLUKnowledge — average accuracy on multilingual translations of MMLU | 81.3% | — |
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% | — |
HealthBench · full setHealthcare — physician rubric score on the full set of the original HealthBench evaluation | 57.6% | — |
HealthBench · hardHealthcare — physician rubric score on the hard subset of the original HealthBench evaluation | 30% | — |
HealthBench · consensusHealthcare — physician rubric score on the consensus subset of the original HealthBench evaluation | 89.9% | — |
| Overview | ||
| Company | OpenAI | Qwen |
| Release date | Aug 5 2025 | Apr 29 2025 |
| Access | Open Weight | Open Weight |
| Model details | View model | View model |
Other comparisons
gpt-oss-120bvsClaude Fable 5.2Qwen3vsClaude Fable 5.2gpt-oss-120bvsGemini 4 CarbonQwen3vsGemini 4 Carbongpt-oss-120bvsMuse EmberQwen3vsMuse Embergpt-oss-120bvsGrok 4.7Qwen3vsGrok 4.7gpt-oss-120bvsDeepSeek-V4.1-Flash-1017Qwen3vsDeepSeek-V4.1-Flash-1017gpt-oss-120bvsMistral Large 4Qwen3vsMistral Large 4Frequently asked questions
gpt-oss-120b leads Qwen3 on 1 of the 1 benchmark they both report (BullshitBench v2). Qwen3 shipped 98 days before gpt-oss-120b, so benchmark comparisons should account for the intervening progress.
gpt-oss-120b has 117B parameters, while Qwen3 has 0.6B/1.7B/4B/8B/14B/30B/32B/235B.
On BullshitBench v2, gpt-oss-120b leads at 12% vs Qwen3 at 6%.