Noometry

Model comparison

GLM-5.3-Flash vs Jamba Large

GLM-5.3-Flash has enough public results to be ranked (#41); Jamba Large does not yet, so treat this comparison as directional.

Last verified . 0 shared benchmarks.

GLM-5.3-Flash Z.ai (Zhipu)

51.8

Rank #41 Confirmed

Jamba Large AI21 Labs

33.1

Unranked Sparse

Summary

  • The widest gap is in reasoning, where GLM-5.3-Flash leads 48.0 to 18.0.
  • GLM-5.3-Flash is cheaper at $0.15 / $0.50 per million input/output tokens, against $2 / $8 for Jamba Large.
  • GLM-5.3-Flash accepts more context: 1M tokens versus 256K.

Side by side

GLM-5.3-FlashJamba Large
ProviderZ.ai (Zhipu)AI21 Labs
Noometry Index51.833.1
Released2026-08-202025-07-01
WeightsOpenOpen
Context window1M256K
Max output131K4K
Input $ / M tokens$0.15$2
Output $ / M tokens$0.50$8
Results tracked402

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Category by category

Coding Not comparable

GLM-5.3-Flash: 53.1 (#31), Jamba Large: —

BenchmarkGLM-5.3-FlashJamba Large
DeepSWE63.4%—
FrontierCode31.8%—
CursorBench36.8%—
LMArena WebDev1609—
FrontierSWE18.1%—
SciCode51.6%—
LMArena Coding1508—
ALE-Bench303.55—

Agentic & Tool Use Not comparable

GLM-5.3-Flash: 34.2 (#47), Jamba Large: —

BenchmarkGLM-5.3-FlashJamba Large
APEX-Agents52.8%—
GDP.pdf14%—

Reasoning GLM-5.3-Flash leads

GLM-5.3-Flash: 48.0 (#42), Jamba Large: 18.0

BenchmarkGLM-5.3-FlashJamba Large
ARC-AGI-265.8%—
Kagi LLM Benchmark—26.1%
ARC-AGI-191%—
CritPt15.4%—
Chess Puzzles14%—
LMArena Hard Prompts1491—
Mystery Game Puzzles8%—
Surface Evolver Bench52.5%—
Bench to the Future 30.15—
Epoch Capabilities Index151.88—

Math Not comparable

GLM-5.3-Flash: 53.3 (#47), Jamba Large: —

BenchmarkGLM-5.3-FlashJamba Large
FrontierMath (Tiers 1-3)55.8%—
FrontierMath Tier 417.1%—
OTIS Mock AIME 2024-202593.9%—
ProofBench21%—
LMArena Math1500—

Knowledge GLM-5.3-Flash leads

GLM-5.3-Flash: 58.4 (#36), Jamba Large: 37.7

BenchmarkGLM-5.3-FlashJamba Large
GPQA Diamond90.2%—
Vectara Hallucination Rate—9.7%
LMArena Expert1513—

Multimodal Not comparable

GLM-5.3-Flash: 42.8 (#27), Jamba Large: —

BenchmarkGLM-5.3-FlashJamba Large
LMArena Vision1296—

Multilingual Not comparable

GLM-5.3-Flash: 56.0 (#25), Jamba Large: —

BenchmarkGLM-5.3-FlashJamba Large
LMArena Non-English1462—
LMArena Chinese1527—
LMArena French1496—
LMArena German1470—
LMArena Japanese1429—
LMArena Korean1446—
LMArena Russian1469—
LMArena Spanish1471—

Instruction Following Not comparable

GLM-5.3-Flash: 77.5 (#20), Jamba Large: —

BenchmarkGLM-5.3-FlashJamba Large
LMArena Instruction Following1478—

Long Context Not comparable

GLM-5.3-Flash: 45.4 (#39), Jamba Large: —

BenchmarkGLM-5.3-FlashJamba Large
LMArena Longer Query1482—

Writing & Preference Not comparable

GLM-5.3-Flash: 65.3 (#50), Jamba Large: —

BenchmarkGLM-5.3-FlashJamba Large
LMArena Text1471—
LMArena Creative Writing1442—
LMArena Multi-Turn1467—

Frequently asked questions

Is GLM-5.3-Flash better than Jamba Large?

GLM-5.3-Flash has enough public results to be ranked (#41); Jamba Large does not yet, so treat this comparison as directional.

Which is cheaper, GLM-5.3-Flash or Jamba Large?

GLM-5.3-Flash is cheaper. It lists at $0.15 per million input tokens and $0.50 per million output tokens; Jamba Large lists at $2 and $8.

Which has the bigger context window?

GLM-5.3-Flash does, with 1M tokens against 256K.

How many benchmarks do GLM-5.3-Flash and Jamba Large share?

0 benchmarks have published results for both models. GLM-5.3-Flash has 40 scored results on Noometry and Jamba Large has 2.

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