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. | 284B | 480B |
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. | 1M | 256k |
| 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. | $0.0413Relace | $0.30DeepInfra |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | $0.0871StreamLake | $1.00DeepInfra |
| $13.06StreamLake · fp8 | $110DeepInfra · turbo · fp4 | |
| Benchmarks | ||
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. | 18% | 20% |
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. | 91.6% | — |
GPQA DiamondScience — Graduate-level science questions in biology, physics, and chemistry — hard enough that subject-matter PhDs score around 65%. Higher is better. | 88.1% | — |
| Overview | ||
| Company | DeepSeek | Qwen |
| Release date | Apr 24 2026 | Jul 22 2025 |
| Access | Open Weight | Open Weight |
| Model details | View model | View model |
Other comparisons
DeepSeek-V4-FlashvsClaude Haiku 5.5Qwen3-CodervsClaude Haiku 5.5DeepSeek-V4-FlashvsGPT-6.1 TerraQwen3-CodervsGPT-6.1 TerraDeepSeek-V4-FlashvsGemini 4 CarbonQwen3-CodervsGemini 4 CarbonDeepSeek-V4-FlashvsMuse Spark OpenQwen3-CodervsMuse Spark OpenDeepSeek-V4-FlashvsGrok 4.7Qwen3-CodervsGrok 4.7DeepSeek-V4-FlashvsMistral Large 4Qwen3-CodervsMistral Large 4Frequently asked questions
Qwen3-Coder leads DeepSeek-V4-Flash on 1 of the 1 benchmark they both report (BullshitBench v2). Qwen3-Coder shipped 276 days before DeepSeek-V4-Flash, so benchmark comparisons should account for the intervening progress.
DeepSeek-V4-Flash has 284B parameters, while Qwen3-Coder has 480B. Context windows are 1M (DeepSeek-V4-Flash) vs 256k (Qwen3-Coder).
On BullshitBench v2, Qwen3-Coder leads at 20% vs DeepSeek-V4-Flash at 18%.