Noometry

Model comparison

DeepSeek-V3.1-Terminus vs Qwen3 32B

DeepSeek-V3.1-Terminus is the stronger model overall, scoring 42.6 to 38.8 on the Noometry Index.

Last verified . 15 shared benchmarks.

DeepSeek-V3.1-Terminus DeepSeek

42.6

Rank #96 Confirmed

Qwen3 32B Alibaba (Qwen)

38.8

Rank #166 Confirmed

Summary

  • They share 15 benchmarks with published results for both. DeepSeek-V3.1-Terminus scores higher in 5 categories and Qwen3 32B in 2 categories; 6 gaps are clear of the uncertainty.
  • The widest gap is in writing & preference, where DeepSeek-V3.1-Terminus leads 61.3 to 53.1.
  • The biggest single-benchmark swing is DTBench: 81.3% for DeepSeek-V3.1-Terminus and 67.5% for Qwen3 32B.
  • DeepSeek-V3.1-Terminus is cheaper at $0.30 / $1 per million input/output tokens, against $0.70 / $2.80 for Qwen3 32B.
  • DeepSeek-V3.1-Terminus accepts more context: 164K tokens versus 131K.

Side by side

DeepSeek-V3.1-TerminusQwen3 32B
ProviderDeepSeekAlibaba (Qwen)
Noometry Index42.638.8
Released2025-09-222025-04
WeightsOpenOpen
Context window164K131K
Max output66K16K
Input $ / M tokens$0.30$0.70
Output $ / M tokens$1$2.80
Results tracked1626

Sponsored placements are available on pages like this one. Advertise on Noometry

Category by category

Coding DeepSeek-V3.1-Terminus leads

DeepSeek-V3.1-Terminus: 41.5 (#114), Qwen3 32B: 37.3 (#184)

BenchmarkDeepSeek-V3.1-TerminusQwen3 32B
SciCode40.6%35.4%
LMArena Coding14261358
Aider Polyglot—40%
ALE-Bench745.17—

Agentic & Tool Use Not comparable

DeepSeek-V3.1-Terminus: —, Qwen3 32B: 32.3 (#58)

BenchmarkDeepSeek-V3.1-TerminusQwen3 32B
Berkeley Function Calling Leaderboard—48.7%

Reasoning DeepSeek-V3.1-Terminus leads

DeepSeek-V3.1-Terminus: 25.7 (#126), Qwen3 32B: 19.6 (#233)

BenchmarkDeepSeek-V3.1-TerminusQwen3 32B
Kagi LLM Benchmark57.4%54.9%
CritPt1.7%0.3%
LMArena Hard Prompts14271335
DTBench81.3%67.5%
LMCA28.6%17.3%
Chess Puzzles—5%
Epoch Capabilities Index—138.51

Math Qwen3 32B leads

DeepSeek-V3.1-Terminus: 35.7 (#138), Qwen3 32B: 37.9 (#89)

BenchmarkDeepSeek-V3.1-TerminusQwen3 32B
LMArena Math14021399
OTIS Mock AIME 2024-2025—66.9%

Knowledge Not comparable

DeepSeek-V3.1-Terminus: —, Qwen3 32B: 40.0 (#112)

BenchmarkDeepSeek-V3.1-TerminusQwen3 32B
GPQA Diamond—65.7%
Vectara Hallucination Rate—5.9%
LMArena Expert—1362

Multilingual DeepSeek-V3.1-Terminus leads

DeepSeek-V3.1-Terminus: 52.3 (#87), Qwen3 32B: 45.8 (#162)

BenchmarkDeepSeek-V3.1-TerminusQwen3 32B
LMArena Non-English14071317
LMArena Russian14341311
LMArena Chinese—1357
LMArena German—1341

Instruction Following DeepSeek-V3.1-Terminus leads

DeepSeek-V3.1-Terminus: 74.4 (#96), Qwen3 32B: 69.1 (#174)

BenchmarkDeepSeek-V3.1-TerminusQwen3 32B
LMArena Instruction Following14061305

Long Context Too close to call

DeepSeek-V3.1-Terminus: 43.5 (#88), Qwen3 32B: 43.7 (#83)

BenchmarkDeepSeek-V3.1-TerminusQwen3 32B
LMArena Longer Query14261327
Fiction.LiveBench—74.2%

Writing & Preference DeepSeek-V3.1-Terminus leads

DeepSeek-V3.1-Terminus: 61.3 (#87), Qwen3 32B: 53.1 (#158)

BenchmarkDeepSeek-V3.1-TerminusQwen3 32B
LMArena Text14201340
LMArena Creative Writing14041297
LMArena Multi-Turn14111331

Frequently asked questions

Is DeepSeek-V3.1-Terminus better than Qwen3 32B?

DeepSeek-V3.1-Terminus is the stronger model overall, scoring 42.6 to 38.8 on the Noometry Index.

Which is cheaper, DeepSeek-V3.1-Terminus or Qwen3 32B?

DeepSeek-V3.1-Terminus is cheaper. It lists at $0.30 per million input tokens and $1 per million output tokens; Qwen3 32B lists at $0.70 and $2.80.

Is DeepSeek-V3.1-Terminus or Qwen3 32B better for coding?

DeepSeek-V3.1-Terminus scores higher on coding benchmarks: 41.5 versus 37.3 in the Noometry coding category.

Which has the bigger context window?

DeepSeek-V3.1-Terminus does, with 164K tokens against 131K.

How many benchmarks do DeepSeek-V3.1-Terminus and Qwen3 32B share?

15 benchmarks have published results for both models. DeepSeek-V3.1-Terminus has 16 scored results on Noometry and Qwen3 32B has 26.

Related comparisons

Go deeper