AI Model Release Tracker

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.
236B0.5B/1.5B/3B/7B/14B/32B/72B
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.
128k128k
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.
—$0.36DeepInfra
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input.
—$0.40DeepInfra
—$92.00DeepInfra · fp8
Overview
CompanyDeepSeekQwen
Release dateMay 6 2024Sep 19 2024
AccessOpen WeightOpen Weight
Model detailsView modelView model

Other comparisons

DeepSeek V2vsClaude Haiku 5.5Qwen2.5vsClaude Haiku 5.5DeepSeek V2vsGPT-6.1 SolQwen2.5vsGPT-6.1 SolDeepSeek V2vsGemini 4 ArgonQwen2.5vsGemini 4 ArgonDeepSeek V2vsMuse Spark OpenQwen2.5vsMuse Spark OpenDeepSeek V2vsGrok 4.7Qwen2.5vsGrok 4.7DeepSeek V2vsMistral Large 4Qwen2.5vsMistral Large 4

Frequently asked questions

DeepSeek V2 and Qwen2.5 don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. DeepSeek V2 shipped 136 days before Qwen2.5, so benchmark comparisons should account for the intervening progress.

DeepSeek V2 has 236B parameters, while Qwen2.5 has 0.5B/1.5B/3B/7B/14B/32B/72B. Context windows are 128k (DeepSeek V2) vs 128k (Qwen2.5).

Direct benchmark comparisons are unavailable — DeepSeek V2 and Qwen2.5 don't publish scores on any of the same benchmarks.