Model comparison

GLM-5.3-Flash vs MiniMax-01

GLM-5.3-Flash has enough public results to be ranked (#45); MiniMax-01 does not yet, so treat this comparison as directional.

Last verified . 0 shared benchmarks.

GLM-5.3-Flash Z.ai (Zhipu)

49.6

Rank #45 Confirmed

MiniMax-01 MiniMax

32.8

Unranked Sparse

Summary

  • The widest gap is in knowledge, where GLM-5.3-Flash leads 58.4 to 33.1.
  • GLM-5.3-Flash is cheaper at $0.15 / $0.50 per million input/output tokens, against $0.20 / $1.10 for MiniMax-01.
  • MiniMax-01 accepts more context: 1M tokens versus 1M.

Side by side

GLM-5.3-Flash and MiniMax-01 specifications
GLM-5.3-FlashMiniMax-01
ProviderZ.ai (Zhipu)MiniMax
Noometry Index49.632.8
Released2026-08-20—
WeightsOpenOpen
Context window1M1M
Max output131K900K
Input $ / M tokens$0.15$0.20
Output $ / M tokens$0.50$1.10
Results tracked362

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Category by category

Coding Not comparable

GLM-5.3-Flash: 53.7 (#26), MiniMax-01: —

Coding benchmarks
BenchmarkGLM-5.3-FlashMiniMax-01
DeepSWE63.4%—
FrontierCode31.8%—
LMArena WebDev1614—
SciCode51.6%—
LMArena Coding1506—
ALE-Bench303.55—

Agentic & Tool Use Not comparable

GLM-5.3-Flash: 34.3 (#41), MiniMax-01: —

Agentic & Tool Use benchmarks
BenchmarkGLM-5.3-FlashMiniMax-01
APEX-Agents52.8%—
GDP.pdf14%—

Reasoning GLM-5.3-Flash leads

GLM-5.3-Flash: 31.7 (#69), MiniMax-01: 21.7

Reasoning benchmarks
BenchmarkGLM-5.3-FlashMiniMax-01
Kagi LLM Benchmark—42.5%
CritPt15.4%—
Chess Puzzles14%—
LMArena Hard Prompts1490—
Mystery Game Puzzles8%—
Surface Evolver Bench52.5%—
Bench to the Future 30.15—
Epoch Capabilities Index151.9—

Math Not comparable

GLM-5.3-Flash: 53.1 (#38), MiniMax-01: —

Math benchmarks
BenchmarkGLM-5.3-FlashMiniMax-01
FrontierMath (Tiers 1-3)55.8%—
FrontierMath Tier 417.1%—
OTIS Mock AIME 2024-202593.9%—
ProofBench21%—
LMArena Math1499—

Knowledge GLM-5.3-Flash leads

GLM-5.3-Flash: 58.4 (#33), MiniMax-01: 33.1

Knowledge benchmarks
BenchmarkGLM-5.3-FlashMiniMax-01
GPQA Diamond90.2%—
Confabulations—23.9%
LMArena Expert1512—

Multimodal Not comparable

GLM-5.3-Flash: 42.6 (#25), MiniMax-01: —

Multimodal benchmarks
BenchmarkGLM-5.3-FlashMiniMax-01
LMArena Vision1304—

Multilingual Not comparable

GLM-5.3-Flash: 56.2 (#24), MiniMax-01: —

Multilingual benchmarks
BenchmarkGLM-5.3-FlashMiniMax-01
LMArena Non-English1462—
LMArena Chinese1529—
LMArena French1495—
LMArena German1457—
LMArena Japanese1420—
LMArena Korean1443—
LMArena Russian1472—
LMArena Spanish1471—

Instruction Following Not comparable

GLM-5.3-Flash: 77.8 (#17), MiniMax-01: —

Instruction Following benchmarks
BenchmarkGLM-5.3-FlashMiniMax-01
LMArena Instruction Following1480—

Long Context Not comparable

GLM-5.3-Flash: 45.3 (#41), MiniMax-01: —

Long Context benchmarks
BenchmarkGLM-5.3-FlashMiniMax-01
LMArena Longer Query1479—

Writing & Preference Not comparable

GLM-5.3-Flash: 65.4 (#49), MiniMax-01: —

Writing & Preference benchmarks
BenchmarkGLM-5.3-FlashMiniMax-01
LMArena Text1471—
LMArena Creative Writing1440—
LMArena Multi-Turn1471—

Frequently asked questions

Is GLM-5.3-Flash better than MiniMax-01?

GLM-5.3-Flash has enough public results to be ranked (#45); MiniMax-01 does not yet, so treat this comparison as directional.

Which is cheaper, GLM-5.3-Flash or MiniMax-01?

GLM-5.3-Flash is cheaper. It lists at $0.15 per million input tokens and $0.50 per million output tokens; MiniMax-01 lists at $0.20 and $1.10.

Which has the bigger context window?

MiniMax-01 does, with 1M tokens against 1M.

How many benchmarks do GLM-5.3-Flash and MiniMax-01 share?

0 benchmarks have published results for both models. GLM-5.3-Flash has 36 scored results on Noometry and MiniMax-01 has 2.

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