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. | 7B/13B/33B/65B | 21B |
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. | 2k | 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. | — | $0.018Darkbloom |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | — | $0.09Darkbloom |
| — | $8.10Darkbloom · fp8 | |
| These models have no shared benchmark scores. | ||
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. | — | 60.7% |
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% |
GPQA DiamondScience — Graduate-level science questions in biology, physics, and chemistry — hard enough that subject-matter PhDs score around 65%. Higher is better. | — | 71.5% |
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% |
| Overview | ||
| Company | Meta | OpenAI |
| Release date | Feb 24 2023 | Aug 5 2025 |
| Access | Open Weight | Open Weight |
| Model details | View model | View model |
Other comparisons
LLaMA 1vsClaude Haiku 5.5gpt-oss-20bvsClaude Haiku 5.5LLaMA 1vsGemini 4 Argongpt-oss-20bvsGemini 4 ArgonLLaMA 1vsGrok 4.7gpt-oss-20bvsGrok 4.7LLaMA 1vsDeepSeek-V4.1-Flash-1017gpt-oss-20bvsDeepSeek-V4.1-Flash-1017LLaMA 1vsMistral Large 4gpt-oss-20bvsMistral Large 4LLaMA 1vsKimi K3gpt-oss-20bvsKimi K3Frequently asked questions
LLaMA 1 and gpt-oss-20b don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. LLaMA 1 shipped 893 days before gpt-oss-20b, so benchmark comparisons should account for the intervening progress.
LLaMA 1 has 7B/13B/33B/65B parameters, while gpt-oss-20b has 21B. Context windows are 2k (LLaMA 1) vs 128k (gpt-oss-20b).
Direct benchmark comparisons are unavailable — LLaMA 1 and gpt-oss-20b don't publish scores on any of the same benchmarks.