DataLearner logo

Qwen3.8-Max-0902vsDeepSeek-V4-Pro

Across 4 shared benchmarks, Qwen3.8-Max-0902 leads overall: Qwen3.8-Max-0902 wins 3, DeepSeek-V4-Pro wins 1, with 0 ties and an average score difference of +7.05.

阿里巴巴
Qwen3.8-Max-0902

阿里巴巴 · 2026-09-02 · Reasoning model

DeepSeek-AI
DeepSeek-V4-Pro

DeepSeek-AI · 2026-08-13 · Reasoning model

Qwen3.8-Max-09023 wins(75%)(25%)1 winDeepSeek-V4-Pro

Benchmark scores

Grouped by capability, sorted by largest gap within each. 4 shared benchmarks.

AI Agent - Tool Usage

Even 2/2
BenchmarkQwen3.8-Max-0902DeepSeek-V4-ProDiff
AutomationBench50.801 / 12极高强度思考(工具)31.805 / 12极高强度思考(工具)+19
Toolathlon-Verified73.307 / 10极高强度思考(工具)74.104 / 10极高强度思考(工具)-0.80

Coding and Software Engineer

Qwen3.8-Max-0902 2/2
BenchmarkQwen3.8-Max-0902DeepSeek-V4-ProDiff
DeepSWE69.304 / 32极高强度思考(工具)62.7014 / 32极高强度思考(工具)+6.60
NL2Repo-Bench64.901 / 12极高强度思考(工具)61.502 / 12极高强度思考(工具)+3.40

Specs

FieldQwen3.8-Max-0902DeepSeek-V4-Pro
Publisher阿里巴巴DeepSeek-AI
Release date2026-09-022026-08-13
Model typeReasoning modelReasoning model
ArchitectureMoEMoE
Parameters2.4T1.6T
Context length1M1M
Max output131K384K

API pricing

Prices use DataLearner records when available; missing fields are not inferred.

ItemQwen3.8-Max-0902DeepSeek-V4-Pro
Text input$2 / 1M tokens$0.435 / 1M tokens
Text output$6 / 1M tokens$0.87 / 1M tokens
Cache read$0.25 / 1M tokens$0.003625 / 1M tokens
Cache write$2.5 / 1M tokensNot public

Summary

  • Qwen3.8-Max-0902leads in:Coding and Software Engineer (2/2)
  • Tied in:AI Agent - Tool Usage

On average across the 4 shared benchmarks, Qwen3.8-Max-0902 scores 7.05 higher.

Largest single-benchmark gap: AutomationBench — Qwen3.8-Max-0902 50.80 vs DeepSeek-V4-Pro 31.80 (+19).

Page generated from structured model, pricing and benchmark records. No real-time LLM is used to write the prose.