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. | 671B | 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. | 128k | 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. | $0.256DeepInfra | $0.018Darkbloom |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | $0.712DeepInfra | $0.09Darkbloom |
| — | $8.10Darkbloom · fp8 | |
| 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. | 42% | 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. | 59.1% | 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. | 88.5% | 85.3% |
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. | 40.5% | — |
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% |
| Overview | ||
| Company | DeepSeek | OpenAI |
| Release date | Dec 26 2024 | Aug 5 2025 |
| Access | Open Weight | Open Weight |
| Model details | View model | View model |
Other comparisons
DeepSeek V3 ChatvsClaude Haiku 5.5gpt-oss-20bvsClaude Haiku 5.5DeepSeek V3 ChatvsGemini 4 Argongpt-oss-20bvsGemini 4 ArgonDeepSeek V3 ChatvsMuse Spark Opengpt-oss-20bvsMuse Spark OpenDeepSeek V3 ChatvsGrok 4.7gpt-oss-20bvsGrok 4.7DeepSeek V3 ChatvsMistral Large 4gpt-oss-20bvsMistral Large 4DeepSeek V3 ChatvsKimi K3gpt-oss-20bvsKimi K3Frequently asked questions
gpt-oss-20b leads DeepSeek V3 Chat on 2 of the 3 benchmarks they both report (SWE-Bench Verified, GPQA Diamond, MMLU). DeepSeek V3 Chat shipped 222 days before gpt-oss-20b, so benchmark comparisons should account for the intervening progress.
DeepSeek V3 Chat has 671B parameters, while gpt-oss-20b has 21B. Context windows are 128k (DeepSeek V3 Chat) vs 128k (gpt-oss-20b).
On SWE-Bench Verified, gpt-oss-20b leads at 60.7% vs DeepSeek V3 Chat at 42%. On GPQA Diamond, gpt-oss-20b leads at 71.5% vs DeepSeek V3 Chat at 59.1%. On MMLU, DeepSeek V3 Chat leads at 88.5% vs gpt-oss-20b at 85.3%.