Noometry

Model comparison

DeepSeek-V3.1-Terminus vs Qwen Max

DeepSeek-V3.1-Terminus is the stronger model overall, scoring 43.1 to 34.7 on the Noometry Index.

Last verified . 10 shared benchmarks.

DeepSeek-V3.1-Terminus DeepSeek

43.1

Rank #97 Confirmed

Qwen Max Alibaba (Qwen)

34.7

Rank #230 Confirmed

Summary

  • They share 10 benchmarks with published results for both. DeepSeek-V3.1-Terminus scores higher in 7 categories and Qwen Max in 0 categories; 7 gaps are clear of the uncertainty.
  • The widest gap is in math, where DeepSeek-V3.1-Terminus leads 38.5 to 22.3.
  • DeepSeek-V3.1-Terminus is cheaper at $0.27 / $1 per million input/output tokens, against $1.60 / $6.40 for Qwen Max.
  • DeepSeek-V3.1-Terminus accepts more context: 164K tokens versus 33K.
  • DeepSeek-V3.1-Terminus has downloadable open weights; the other is API-only.

Side by side

DeepSeek-V3.1-TerminusQwen Max
ProviderDeepSeekAlibaba (Qwen)
Noometry Index43.134.7
Released2025-09-222024-04-03
WeightsOpenProprietary
Context window164K33K
Max output147K8K
Input $ / M tokens$0.27$1.60
Output $ / M tokens$1$6.40
Results tracked1623

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Category by category

Coding DeepSeek-V3.1-Terminus leads

DeepSeek-V3.1-Terminus: 42.0 (#113), Qwen Max: 30.7 (#292)

BenchmarkDeepSeek-V3.1-TerminusQwen Max
LMArena Coding14261288
Aider Polyglot—21.8%
SciCode40.6%—
ALE-Bench745.17—

Reasoning DeepSeek-V3.1-Terminus leads

DeepSeek-V3.1-Terminus: 26.4 (#133), Qwen Max: 25.1 (#151)

BenchmarkDeepSeek-V3.1-TerminusQwen Max
LMArena Hard Prompts14261269
Kagi LLM Benchmark57.4%—
CritPt1.7%—
DTBench81.3%—
LMCA28.6%—

Math DeepSeek-V3.1-Terminus leads

DeepSeek-V3.1-Terminus: 38.5 (#137), Qwen Max: 22.3 (#276)

BenchmarkDeepSeek-V3.1-TerminusQwen Max
LMArena Math14021275
OTIS Mock AIME 2024-2025—16.1%
MATH Level 5—67.2%
FrontierMath (Feb 2025 set)—1%

Knowledge Not comparable

DeepSeek-V3.1-Terminus: —, Qwen Max: 30.3 (#228)

BenchmarkDeepSeek-V3.1-TerminusQwen Max
GPQA Diamond—56.1%
LMArena Expert—1248

Multilingual DeepSeek-V3.1-Terminus leads

DeepSeek-V3.1-Terminus: 52.1 (#92), Qwen Max: 41.8 (#202)

BenchmarkDeepSeek-V3.1-TerminusQwen Max
LMArena Non-English14071263
LMArena Russian14361274
LMArena Chinese—1254
LMArena French—1330
LMArena German—1254
LMArena Japanese—1205
LMArena Korean—1142
LMArena Spanish—1290

Instruction Following DeepSeek-V3.1-Terminus leads

DeepSeek-V3.1-Terminus: 74.0 (#106), Qwen Max: 66.5 (#208)

BenchmarkDeepSeek-V3.1-TerminusQwen Max
LMArena Instruction Following14041262

Long Context DeepSeek-V3.1-Terminus leads

DeepSeek-V3.1-Terminus: 43.4 (#97), Qwen Max: 39.4 (#180)

BenchmarkDeepSeek-V3.1-TerminusQwen Max
LMArena Longer Query14211288
Fiction.LiveBench—66.7%

Writing & Preference DeepSeek-V3.1-Terminus leads

DeepSeek-V3.1-Terminus: 61.0 (#92), Qwen Max: 47.8 (#205)

BenchmarkDeepSeek-V3.1-TerminusQwen Max
LMArena Text14191282
LMArena Creative Writing14031248
LMArena Multi-Turn14111277

Frequently asked questions

Is DeepSeek-V3.1-Terminus better than Qwen Max?

DeepSeek-V3.1-Terminus is the stronger model overall, scoring 43.1 to 34.7 on the Noometry Index.

Which is cheaper, DeepSeek-V3.1-Terminus or Qwen Max?

DeepSeek-V3.1-Terminus is cheaper. It lists at $0.27 per million input tokens and $1 per million output tokens; Qwen Max lists at $1.60 and $6.40.

Is DeepSeek-V3.1-Terminus or Qwen Max better for coding?

DeepSeek-V3.1-Terminus scores higher on coding benchmarks: 42.0 versus 30.7 in the Noometry coding category.

Which has the bigger context window?

DeepSeek-V3.1-Terminus does, with 164K tokens against 33K.

How many benchmarks do DeepSeek-V3.1-Terminus and Qwen Max share?

10 benchmarks have published results for both models. DeepSeek-V3.1-Terminus has 16 scored results on Noometry and Qwen Max has 23.

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