Noometry

Model comparison

GLM-5.3-Flash vs Llama2 70b Steerlm Chat

GLM-5.3-Flash is the stronger model overall, scoring 51.8 to 31.8 on the Noometry Index.

Last verified . 9 shared benchmarks.

GLM-5.3-Flash Z.ai (Zhipu)

51.8

Rank #41 Confirmed

Llama2 70b Steerlm Chat NVIDIA

31.8

Rank #268 Confirmed

Summary

  • They share 9 benchmarks with published results for both. GLM-5.3-Flash scores higher in 7 categories and Llama2 70b Steerlm Chat in 0 categories; 7 gaps are clear of the uncertainty.
  • The widest gap is in writing & preference, where GLM-5.3-Flash leads 65.3 to 31.6.

Side by side

GLM-5.3-FlashLlama2 70b Steerlm Chat
ProviderZ.ai (Zhipu)NVIDIA
Noometry Index51.831.8
Released2026-08-20—
WeightsOpenOpen
Context window1M—
Max output131K—
Input $ / M tokens$0.15—
Output $ / M tokens$0.50—
Results tracked409

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Category by category

Coding GLM-5.3-Flash leads

GLM-5.3-Flash: 53.1 (#31), Llama2 70b Steerlm Chat: 29.9 (#300)

BenchmarkGLM-5.3-FlashLlama2 70b Steerlm Chat
LMArena Coding15081025
DeepSWE63.4%—
FrontierCode31.8%—
CursorBench36.8%—
LMArena WebDev1609—
FrontierSWE18.1%—
SciCode51.6%—
ALE-Bench303.55—

Agentic & Tool Use Not comparable

GLM-5.3-Flash: 34.2 (#47), Llama2 70b Steerlm Chat: —

BenchmarkGLM-5.3-FlashLlama2 70b Steerlm Chat
APEX-Agents52.8%—
GDP.pdf14%—

Reasoning GLM-5.3-Flash leads

GLM-5.3-Flash: 48.0 (#42), Llama2 70b Steerlm Chat: 20.0 (#246)

BenchmarkGLM-5.3-FlashLlama2 70b Steerlm Chat
LMArena Hard Prompts14911047
ARC-AGI-265.8%—
ARC-AGI-191%—
CritPt15.4%—
Chess Puzzles14%—
Mystery Game Puzzles8%—
Surface Evolver Bench52.5%—
Bench to the Future 30.15—
Epoch Capabilities Index151.88—

Math GLM-5.3-Flash leads

GLM-5.3-Flash: 53.3 (#47), Llama2 70b Steerlm Chat: 31.3 (#226)

BenchmarkGLM-5.3-FlashLlama2 70b Steerlm Chat
LMArena Math15001072
FrontierMath (Tiers 1-3)55.8%—
FrontierMath Tier 417.1%—
OTIS Mock AIME 2024-202593.9%—
ProofBench21%—

Knowledge Not comparable

GLM-5.3-Flash: 58.4 (#36), Llama2 70b Steerlm Chat: —

BenchmarkGLM-5.3-FlashLlama2 70b Steerlm Chat
GPQA Diamond90.2%—
LMArena Expert1513—

Multimodal Not comparable

GLM-5.3-Flash: 42.8 (#27), Llama2 70b Steerlm Chat: —

BenchmarkGLM-5.3-FlashLlama2 70b Steerlm Chat
LMArena Vision1296—

Multilingual GLM-5.3-Flash leads

GLM-5.3-Flash: 56.0 (#25), Llama2 70b Steerlm Chat: 28.8 (#270)

BenchmarkGLM-5.3-FlashLlama2 70b Steerlm Chat
LMArena Non-English14621063
LMArena Chinese1527—
LMArena French1496—
LMArena German1470—
LMArena Japanese1429—
LMArena Korean1446—
LMArena Russian1469—
LMArena Spanish1471—

Instruction Following GLM-5.3-Flash leads

GLM-5.3-Flash: 77.5 (#20), Llama2 70b Steerlm Chat: 54.2 (#279)

BenchmarkGLM-5.3-FlashLlama2 70b Steerlm Chat
LMArena Instruction Following14781060

Long Context GLM-5.3-Flash leads

GLM-5.3-Flash: 45.4 (#39), Llama2 70b Steerlm Chat: 30.4 (#288)

BenchmarkGLM-5.3-FlashLlama2 70b Steerlm Chat
LMArena Longer Query1482998

Writing & Preference GLM-5.3-Flash leads

GLM-5.3-Flash: 65.3 (#50), Llama2 70b Steerlm Chat: 31.6 (#283)

BenchmarkGLM-5.3-FlashLlama2 70b Steerlm Chat
LMArena Text14711098
LMArena Creative Writing14421091
LMArena Multi-Turn14671058

Frequently asked questions

Is GLM-5.3-Flash better than Llama2 70b Steerlm Chat?

GLM-5.3-Flash is the stronger model overall, scoring 51.8 to 31.8 on the Noometry Index.

Is GLM-5.3-Flash or Llama2 70b Steerlm Chat better for coding?

GLM-5.3-Flash scores higher on coding benchmarks: 53.1 versus 29.9 in the Noometry coding category.

How many benchmarks do GLM-5.3-Flash and Llama2 70b Steerlm Chat share?

9 benchmarks have published results for both models. GLM-5.3-Flash has 40 scored results on Noometry and Llama2 70b Steerlm Chat has 9.

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