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 | 397B |
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 | 1M |
| 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. | — | $0.40 |
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. | — | $2.40 |
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. | $0.50DeepInfra | $0.26Alibaba |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | $2.15DeepInfra | $1.56Alibaba |
| $208DeepInfra · fp4 | $130Alibaba | |
| 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. | 8% | — |
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. | 57.6% | — |
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. | 73.3% | — |
GPQA DiamondScience — Graduate-level science questions in biology, physics, and chemistry — hard enough that subject-matter PhDs score around 65%. Higher is better. | 81% | — |
| Overview | ||
| Company | DeepSeek | Qwen |
| Release date | May 28 2025 | Feb 16 2026 |
| Access | Open Weight | Closed |
| Model details | View model | View model |
Other comparisons
DeepSeek R1-0528vsClaude Haiku 5.5Qwen3.5-PlusvsClaude Haiku 5.5DeepSeek R1-0528vsGPT-6.1 SolQwen3.5-PlusvsGPT-6.1 SolDeepSeek R1-0528vsGemini 4 ArgonQwen3.5-PlusvsGemini 4 ArgonDeepSeek R1-0528vsMuse Spark OpenQwen3.5-PlusvsMuse Spark OpenDeepSeek R1-0528vsGrok 4.7Qwen3.5-PlusvsGrok 4.7DeepSeek R1-0528vsMistral Large 4Qwen3.5-PlusvsMistral Large 4Frequently asked questions
DeepSeek R1-0528 and Qwen3.5-Plus don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Only Qwen3.5-Plus has a verified first-party API price: $0.40 per million input tokens and $2.40 per million output tokens. No pay-as-you-go API rate is tracked for DeepSeek R1-0528. DeepSeek R1-0528 shipped 264 days before Qwen3.5-Plus, so benchmark comparisons should account for the intervening progress.
DeepSeek R1-0528 has 671B parameters, while Qwen3.5-Plus has 397B. Context windows are 128k (DeepSeek R1-0528) vs 1M (Qwen3.5-Plus). DeepSeek R1-0528 is open weight, while Qwen3.5-Plus is closed.
Direct benchmark comparisons are unavailable — DeepSeek R1-0528 and Qwen3.5-Plus don't publish scores on any of the same benchmarks.