Model comparison

DeepSeek-R1 vs Qwen2.5 Plus 1127

DeepSeek-R1 is the stronger model overall, scoring 42.3 to 38.8 on the Noometry Index.

Last verified . 14 shared benchmarks.

DeepSeek-R1 DeepSeek

42.3

Rank #115 Confirmed

Qwen2.5 Plus 1127 Alibaba (Qwen)

38.8

Rank #181 Confirmed

Summary

  • They share 14 benchmarks with published results for both. DeepSeek-R1 scores higher in 7 categories and Qwen2.5 Plus 1127 in 1 category; 8 gaps are clear of the uncertainty.
  • The widest gap is in writing & preference, where DeepSeek-R1 leads 61.4 to 49.4.

Side by side

DeepSeek-R1 and Qwen2.5 Plus 1127 specifications
DeepSeek-R1Qwen2.5 Plus 1127
ProviderDeepSeekAlibaba (Qwen)
Noometry Index42.338.8
Released2025-01-20—
WeightsProprietaryProprietary
Context window164K—
Max output64K—
Input $ / M tokens$0.50—
Output $ / M tokens$2.15—
Results tracked5214

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Category by category

Coding DeepSeek-R1 leads

DeepSeek-R1: 46.3 (#68), Qwen2.5 Plus 1127: 38.5 (#175)

Coding benchmarks
BenchmarkDeepSeek-R1Qwen2.5 Plus 1127
LMArena Coding14271314
Aider Polyglot71.4%—
SciCode35.7%—
WeirdML41.6%—
LiveBench Coding66.7%—
ALE-Bench804.12—
AlgoTune1.7—

Agentic & Tool Use Not comparable

DeepSeek-R1: 30.7 (#75), Qwen2.5 Plus 1127: —

Agentic & Tool Use benchmarks
BenchmarkDeepSeek-R1Qwen2.5 Plus 1127
DeepResearch Bench35.1%—
BALROG34.9%—
METR Time Horizons53.8%—

Reasoning Qwen2.5 Plus 1127 leads

DeepSeek-R1: 18.6 (#278), Qwen2.5 Plus 1127: 25.9 (#141)

Reasoning benchmarks
BenchmarkDeepSeek-R1Qwen2.5 Plus 1127
LMArena Hard Prompts14161299
ARC-AGI-21.3%—
SimpleBench40.8%—
Kagi LLM Benchmark69.4%—
ARC-AGI-121.2%—
CritPt1.1%—
LiveBench Reasoning83.2%—
LiveBench Data Analysis69.8%—
Epoch Capabilities Index141.29—
ForecastBench60—
LiveBench71.6%—

Math DeepSeek-R1 leads

DeepSeek-R1: 43.8 (#79), Qwen2.5 Plus 1127: 36.1 (#174)

Math benchmarks
BenchmarkDeepSeek-R1Qwen2.5 Plus 1127
LMArena Math14001298
OTIS Mock AIME 2024-202566.4%—
Omni-MATH42.4%—
LiveBench Math80.7%—
MATH Level 596.6%—

Knowledge DeepSeek-R1 leads

DeepSeek-R1: 44.5 (#87), Qwen2.5 Plus 1127: 35.5 (#183)

Knowledge benchmarks
BenchmarkDeepSeek-R1Qwen2.5 Plus 1127
LMArena Expert13941289
GPQA Diamond76.3%—
MMLU-Pro79.3%—
Confabulations12.7%—
Vectara Hallucination Rate11.3%—
GPQA (HELM)66.6%—

Multilingual DeepSeek-R1 leads

DeepSeek-R1: 52.4 (#85), Qwen2.5 Plus 1127: 41.9 (#201)

Multilingual benchmarks
BenchmarkDeepSeek-R1Qwen2.5 Plus 1127
LMArena Non-English14121265
LMArena Chinese14421314
LMArena German14041231
LMArena Japanese13911207
LMArena Russian14231271
LMArena French1417—
LMArena Korean1360—
LMArena Spanish1411—

Instruction Following DeepSeek-R1 leads

DeepSeek-R1: 72.0 (#143), Qwen2.5 Plus 1127: 67.2 (#199)

Instruction Following benchmarks
BenchmarkDeepSeek-R1Qwen2.5 Plus 1127
LMArena Instruction Following13821275
LiveBench Instruction Following80.5%—
IFEval78.4%—

Long Context DeepSeek-R1 leads

DeepSeek-R1: 45.4 (#36), Qwen2.5 Plus 1127: 39.2 (#184)

Long Context benchmarks
BenchmarkDeepSeek-R1Qwen2.5 Plus 1127
LMArena Longer Query13911292
Fiction.LiveBench75%—

Writing & Preference DeepSeek-R1 leads

DeepSeek-R1: 61.4 (#88), Qwen2.5 Plus 1127: 49.4 (#192)

Writing & Preference benchmarks
BenchmarkDeepSeek-R1Qwen2.5 Plus 1127
LMArena Text14281299
LMArena Creative Writing14051262
LMArena Multi-Turn14051299
Short-Story Creative Writing83%—
EQ-Bench Creative Writing1500—
WildBench82.8%—
LiveBench Language48.5%—

Frequently asked questions

Is DeepSeek-R1 better than Qwen2.5 Plus 1127?

DeepSeek-R1 is the stronger model overall, scoring 42.3 to 38.8 on the Noometry Index.

Is DeepSeek-R1 or Qwen2.5 Plus 1127 better for coding?

DeepSeek-R1 scores higher on coding benchmarks: 46.3 versus 38.5 in the Noometry coding category.

How many benchmarks do DeepSeek-R1 and Qwen2.5 Plus 1127 share?

14 benchmarks have published results for both models. DeepSeek-R1 has 52 scored results on Noometry and Qwen2.5 Plus 1127 has 14.

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