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 | 0.5B/1.5B/3B/7B/14B/32B |
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. | $0.2088GMICloud | $0.66Cloudflare |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | $0.3096GMICloud | $1.00Cloudflare |
| $57.24GMICloud · fp8 | $182Cloudflare | |
| 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. | 13% | — |
| Overview | ||
| Company | DeepSeek | Qwen |
| Release date | Dec 1 2025 | Nov 12 2024 |
| Access | Open Weight | Open Weight |
| Model details | View model | View model |
Other comparisons
DeepSeek-V3.2vsClaude Haiku 5.5Qwen2.5-CodervsClaude Haiku 5.5DeepSeek-V3.2vsGPT-6.1 SolQwen2.5-CodervsGPT-6.1 SolDeepSeek-V3.2vsGemini 4 ArgonQwen2.5-CodervsGemini 4 ArgonDeepSeek-V3.2vsMuse Spark OpenQwen2.5-CodervsMuse Spark OpenDeepSeek-V3.2vsGrok 4.7Qwen2.5-CodervsGrok 4.7DeepSeek-V3.2vsMistral Large 4Qwen2.5-CodervsMistral Large 4Frequently asked questions
DeepSeek-V3.2 and Qwen2.5-Coder don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Qwen2.5-Coder shipped 384 days before DeepSeek-V3.2, so benchmark comparisons should account for the intervening progress.
DeepSeek-V3.2 has 671B parameters, while Qwen2.5-Coder has 0.5B/1.5B/3B/7B/14B/32B. Context windows are 128k (DeepSeek-V3.2) vs 128k (Qwen2.5-Coder).
Direct benchmark comparisons are unavailable — DeepSeek-V3.2 and Qwen2.5-Coder don't publish scores on any of the same benchmarks.