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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.
7B80B
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.
4k256k
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.
—$0.072DeepInfra
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input.
—$0.88DeepInfra
—$73.00DeepInfra · fp8
Overview
CompanyDeepSeekQwen
Release dateFeb 5 2024Sep 11 2025
AccessOpen WeightOpen Weight
Model detailsView modelView model

Other comparisons

DeepSeek-MathvsClaude Haiku 5.5Qwen3-NextvsClaude Haiku 5.5DeepSeek-MathvsGPT-6.1 SolQwen3-NextvsGPT-6.1 SolDeepSeek-MathvsGemini 4 ArgonQwen3-NextvsGemini 4 ArgonDeepSeek-MathvsMuse Spark OpenQwen3-NextvsMuse Spark OpenDeepSeek-MathvsGrok 4.7Qwen3-NextvsGrok 4.7DeepSeek-MathvsMistral Large 4Qwen3-NextvsMistral Large 4

Frequently asked questions

DeepSeek-Math and Qwen3-Next don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. DeepSeek-Math shipped 584 days before Qwen3-Next, so benchmark comparisons should account for the intervening progress.

DeepSeek-Math has 7B parameters, while Qwen3-Next has 80B. Context windows are 4k (DeepSeek-Math) vs 256k (Qwen3-Next).

Direct benchmark comparisons are unavailable — DeepSeek-Math and Qwen3-Next don't publish scores on any of the same benchmarks.