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 | >1T |
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 | 256k |
| 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. | — | $1.20 |
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. | — | $6.00 |
| — | $540Qwen list price | |
| These models have no shared benchmark scores. | ||
HumanEvalFunction synthesis — 164 small Python problems: the AI is given a function's description and has to write the working function. This was the coding benchmark of the GPT-3.5 and GPT-4 era, before the field moved to fixing real bugs in real repositories. Higher is better. | 65.2% | — |
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. | 87.1% | — |
GSM8KGrade-school math — Grade-school maths word problems that take a few steps of arithmetic to work through. It separated the models of 2022 and 2023 sharply, then saturated. One caveat on the historical numbers: OpenAI included part of the GSM8K training set in GPT-4's pre-training mix, so GPT-4's score is not a clean few-shot result. Higher is better. | 89.3% | — |
| Overview | ||
| Company | DeepSeek | Qwen |
| Release date | Dec 25 2024 | Sep 24 2025 |
| Access | Open Weight | Closed |
| Model details | View model | View model |
Other comparisons
DeepSeek V3 BasevsClaude Haiku 5.5Qwen3-MaxvsClaude Haiku 5.5DeepSeek V3 BasevsGPT-6.1 SolQwen3-MaxvsGPT-6.1 SolDeepSeek V3 BasevsGemini 4 ArgonQwen3-MaxvsGemini 4 ArgonDeepSeek V3 BasevsMuse Spark OpenQwen3-MaxvsMuse Spark OpenDeepSeek V3 BasevsGrok 4.7Qwen3-MaxvsGrok 4.7DeepSeek V3 BasevsMistral Large 4Qwen3-MaxvsMistral Large 4Frequently asked questions
DeepSeek V3 Base and Qwen3-Max don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Only Qwen3-Max has a verified first-party API price: $1.20 per million input tokens and $6.00 per million output tokens. No pay-as-you-go API rate is tracked for DeepSeek V3 Base. DeepSeek V3 Base shipped 273 days before Qwen3-Max, so benchmark comparisons should account for the intervening progress.
DeepSeek V3 Base has 671B parameters, while Qwen3-Max has >1T. Context windows are 128k (DeepSeek V3 Base) vs 256k (Qwen3-Max). DeepSeek V3 Base is open weight, while Qwen3-Max is closed.
Direct benchmark comparisons are unavailable — DeepSeek V3 Base and Qwen3-Max don't publish scores on any of the same benchmarks.