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 | 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 | 128k |
| 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. | — | $0.70 |
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. | — | $2.80 |
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. | $0.25DeepInfra | $0.08DeepInfra |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | $0.95DeepInfra | $0.22NextBit |
| $97.50DeepInfra · fp4 | $30.00DeepInfra · fp8 | |
| These models have no shared benchmark scores. | ||
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. | — | 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. | 66% | — |
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. | 74.8% | — |
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. | 15.9% | — |
| Overview | ||
| Company | DeepSeek | Qwen |
| Release date | Aug 21 2025 | Apr 29 2025 |
| Access | Open Weight | Open Weight |
| Model details | View model | View model |
Other comparisons
DeepSeek V3.1vsClaude Haiku 5.5Qwen3vsClaude Haiku 5.5DeepSeek V3.1vsGPT-6.1 SolQwen3vsGPT-6.1 SolDeepSeek V3.1vsGemini 4 ArgonQwen3vsGemini 4 ArgonDeepSeek V3.1vsMuse Spark OpenQwen3vsMuse Spark OpenDeepSeek V3.1vsGrok 4.7Qwen3vsGrok 4.7DeepSeek V3.1vsMistral Large 4Qwen3vsMistral Large 4Frequently asked questions
DeepSeek V3.1 and Qwen3 don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Only Qwen3 has a verified first-party API price: $0.70 per million input tokens and $2.80 per million output tokens. No pay-as-you-go API rate is tracked for DeepSeek V3.1. Qwen3 shipped 114 days before DeepSeek V3.1, so benchmark comparisons should account for the intervening progress.
DeepSeek V3.1 has 671B parameters, while Qwen3 has 0.6B/1.7B/4B/8B/14B/30B/32B/235B. Context windows are 128k (DeepSeek V3.1) vs 128k (Qwen3).
Direct benchmark comparisons are unavailable — DeepSeek V3.1 and Qwen3 don't publish scores on any of the same benchmarks.