Model comparison

DeepSeek-R1 vs Wizardlm 70b

DeepSeek-R1 is the stronger model overall, scoring 42.0 to 32.4 on the Noometry Index.

Last verified . 12 shared benchmarks.

DeepSeek-R1 DeepSeek

42.0

Rank #110 Confirmed

Wizardlm 70b Microsoft

32.4

Rank #245 Confirmed

Summary

  • They share 12 benchmarks with published results for both. DeepSeek-R1 scores higher in 6 categories and Wizardlm 70b in 1 category; 7 gaps are clear of the uncertainty.
  • The widest gap is in writing & preference, where DeepSeek-R1 leads 61.5 to 34.9.
  • Wizardlm 70b has downloadable open weights; the other is API-only.

Side by side

DeepSeek-R1 and Wizardlm 70b specifications
DeepSeek-R1Wizardlm 70b
ProviderDeepSeekMicrosoft
Noometry Index42.032.4
Released2025-01-20—
WeightsProprietaryOpen
Context window164K—
Max output64K—
Input $ / M tokens$0.50—
Output $ / M tokens$2.15—
Results tracked5212

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Category by category

Coding DeepSeek-R1 leads

DeepSeek-R1: 45.9 (#63), Wizardlm 70b: 30.9 (#280)

Coding benchmarks
BenchmarkDeepSeek-R1Wizardlm 70b
LMArena Coding14271081
Aider Polyglot71.4%—
SciCode35.7%—
WeirdML41.6%—
LiveBench Coding66.7%—
ALE-Bench804.12—
AlgoTune1.7—

Agentic & Tool Use Not comparable

DeepSeek-R1: 30.4 (#68), Wizardlm 70b: —

Agentic & Tool Use benchmarks
BenchmarkDeepSeek-R1Wizardlm 70b
DeepResearch Bench35.1%—
BALROG34.9%—
METR Time Horizons53.8%—

Reasoning Wizardlm 70b leads

DeepSeek-R1: 18.7 (#253), Wizardlm 70b: 19.8 (#231)

Reasoning benchmarks
BenchmarkDeepSeek-R1Wizardlm 70b
LMArena Hard Prompts14151079
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 Index141.3—
ForecastBench60—
LiveBench71.6%—

Math DeepSeek-R1 leads

DeepSeek-R1: 42.0 (#70), Wizardlm 70b: 29.7 (#216)

Math benchmarks
BenchmarkDeepSeek-R1Wizardlm 70b
LMArena Math14011116
OTIS Mock AIME 2024-202566.4%—
Omni-MATH42.4%—
LiveBench Math80.7%—
MATH Level 596.6%—

Knowledge Not comparable

DeepSeek-R1: 44.3 (#81), Wizardlm 70b: —

Knowledge benchmarks
BenchmarkDeepSeek-R1Wizardlm 70b
GPQA Diamond76.3%—
MMLU-Pro79.3%—
Confabulations12.7%—
Vectara Hallucination Rate11.3%—
GPQA (HELM)66.6%—
LMArena Expert1394—

Multilingual DeepSeek-R1 leads

DeepSeek-R1: 52.6 (#80), Wizardlm 70b: 29.8 (#260)

Multilingual benchmarks
BenchmarkDeepSeek-R1Wizardlm 70b
LMArena Non-English14121078
LMArena Chinese14431052
LMArena German14051083
LMArena Russian14231155
LMArena French1415—
LMArena Japanese1394—
LMArena Korean1359—
LMArena Spanish1410—

Instruction Following DeepSeek-R1 leads

DeepSeek-R1: 72.2 (#138), Wizardlm 70b: 56.5 (#268)

Instruction Following benchmarks
BenchmarkDeepSeek-R1Wizardlm 70b
LMArena Instruction Following13821093
LiveBench Instruction Following80.5%—
IFEval78.4%—

Long Context DeepSeek-R1 leads

DeepSeek-R1: 45.4 (#36), Wizardlm 70b: 33.3 (#258)

Long Context benchmarks
BenchmarkDeepSeek-R1Wizardlm 70b
LMArena Longer Query13911097
Fiction.LiveBench75%—

Writing & Preference DeepSeek-R1 leads

DeepSeek-R1: 61.5 (#84), Wizardlm 70b: 34.9 (#264)

Writing & Preference benchmarks
BenchmarkDeepSeek-R1Wizardlm 70b
LMArena Text14281120
LMArena Creative Writing14061149
LMArena Multi-Turn14041108
Short-Story Creative Writing83%—
EQ-Bench Creative Writing1500—
WildBench82.8%—
LiveBench Language48.5%—

Frequently asked questions

Is DeepSeek-R1 better than Wizardlm 70b?

DeepSeek-R1 is the stronger model overall, scoring 42.0 to 32.4 on the Noometry Index.

Is DeepSeek-R1 or Wizardlm 70b better for coding?

DeepSeek-R1 scores higher on coding benchmarks: 45.9 versus 30.9 in the Noometry coding category.

How many benchmarks do DeepSeek-R1 and Wizardlm 70b share?

12 benchmarks have published results for both models. DeepSeek-R1 has 52 scored results on Noometry and Wizardlm 70b has 12.

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