Noometry

Model comparison

GLM-5.3-Flash vs gpt-oss-20b

GLM-5.3-Flash is the stronger model overall, scoring 51.8 to 32.5 on the Noometry Index. gpt-oss-20b costs 6.6× less per token, which makes it the better buy when GLM-5.3-Flash's lead doesn't matter for your workload.

Last verified . 23 shared benchmarks.

GLM-5.3-Flash Z.ai (Zhipu)

51.8

Rank #41 Confirmed

gpt-oss-20b OpenAI

32.5

Rank #255 Confirmed

Summary

  • They share 23 benchmarks with published results for both. GLM-5.3-Flash scores higher in 9 categories and gpt-oss-20b in 0 categories; 9 gaps are clear of the uncertainty.
  • The widest gap is in writing & preference, where GLM-5.3-Flash leads 65.3 to 35.5.
  • The biggest single-benchmark swing is GPQA Diamond: 90.2% for GLM-5.3-Flash and 60.8% for gpt-oss-20b.
  • gpt-oss-20b is cheaper at $0.018 / $0.09 per million input/output tokens, against $0.15 / $0.50 for GLM-5.3-Flash.
  • GLM-5.3-Flash accepts more context: 1M tokens versus 131K.

Side by side

GLM-5.3-Flashgpt-oss-20b
ProviderZ.ai (Zhipu)OpenAI
Noometry Index51.832.5
Released2026-08-202025-08-05
WeightsOpenOpen
Context window1M131K
Max output131K16K
Input $ / M tokens$0.15$0.018
Output $ / M tokens$0.50$0.09
Results tracked4034

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Category by category

Coding GLM-5.3-Flash leads

GLM-5.3-Flash: 53.1 (#31), gpt-oss-20b: 37.6 (#192)

BenchmarkGLM-5.3-Flashgpt-oss-20b
SciCode51.6%34.4%
LMArena Coding15081306
ALE-Bench303.55566.05
DeepSWE63.4%—
FrontierCode31.8%—
CursorBench36.8%—
LMArena WebDev1609—
FrontierSWE18.1%—
WeirdML—40.9%

Agentic & Tool Use GLM-5.3-Flash leads

GLM-5.3-Flash: 34.2 (#47), gpt-oss-20b: 9.3 (#154)

BenchmarkGLM-5.3-Flashgpt-oss-20b
Terminal-Bench—3.4%
APEX-Agents52.8%—
GDP.pdf14%—

Reasoning GLM-5.3-Flash leads

GLM-5.3-Flash: 48.0 (#42), gpt-oss-20b: 19.3 (#261)

BenchmarkGLM-5.3-Flashgpt-oss-20b
CritPt15.4%1.4%
Chess Puzzles14%4%
LMArena Hard Prompts14911274
Epoch Capabilities Index151.88137.82
ARC-AGI-265.8%—
Kagi LLM Benchmark—53.2%
ARC-AGI-191%—
Mystery Game Puzzles8%—
DTBench—68%
LMCA—14.5%
Surface Evolver Bench52.5%—
Bench to the Future 30.15—

Math GLM-5.3-Flash leads

GLM-5.3-Flash: 53.3 (#47), gpt-oss-20b: 39.4 (#103)

BenchmarkGLM-5.3-Flashgpt-oss-20b
OTIS Mock AIME 2024-202593.9%65.3%
LMArena Math15001317
FrontierMath (Tiers 1-3)55.8%—
FrontierMath Tier 417.1%—
ProofBench21%—
Omni-MATH—56.5%

Knowledge GLM-5.3-Flash leads

GLM-5.3-Flash: 58.4 (#36), gpt-oss-20b: 34.6 (#195)

BenchmarkGLM-5.3-Flashgpt-oss-20b
GPQA Diamond90.2%60.8%
LMArena Expert15131258
MMLU-Pro—74%
GPQA (HELM)—59.4%

Multimodal Not comparable

GLM-5.3-Flash: 42.8 (#27), gpt-oss-20b: —

BenchmarkGLM-5.3-Flashgpt-oss-20b
LMArena Vision1296—

Multilingual GLM-5.3-Flash leads

GLM-5.3-Flash: 56.0 (#25), gpt-oss-20b: 42.2 (#197)

BenchmarkGLM-5.3-Flashgpt-oss-20b
LMArena Non-English14621268
LMArena Chinese15271314
LMArena German14701255
LMArena Japanese14291244
LMArena Korean14461236
LMArena Russian14691278
LMArena Spanish14711267
LMArena French1496—

Instruction Following GLM-5.3-Flash leads

GLM-5.3-Flash: 77.5 (#20), gpt-oss-20b: 61.8 (#240)

BenchmarkGLM-5.3-Flashgpt-oss-20b
LMArena Instruction Following14781236
IFEval—73.2%

Long Context GLM-5.3-Flash leads

GLM-5.3-Flash: 45.4 (#39), gpt-oss-20b: 37.9 (#209)

BenchmarkGLM-5.3-Flashgpt-oss-20b
LMArena Longer Query14821250

Writing & Preference GLM-5.3-Flash leads

GLM-5.3-Flash: 65.3 (#50), gpt-oss-20b: 35.5 (#265)

BenchmarkGLM-5.3-Flashgpt-oss-20b
LMArena Text14711287
LMArena Creative Writing14421201
LMArena Multi-Turn14671268
EQ-Bench Creative Writing—666
WildBench—73.7%

Frequently asked questions

Is GLM-5.3-Flash better than gpt-oss-20b?

GLM-5.3-Flash is the stronger model overall, scoring 51.8 to 32.5 on the Noometry Index. gpt-oss-20b costs 6.6× less per token, which makes it the better buy when GLM-5.3-Flash's lead doesn't matter for your workload.

Which is cheaper, GLM-5.3-Flash or gpt-oss-20b?

gpt-oss-20b is cheaper. It lists at $0.018 per million input tokens and $0.09 per million output tokens; GLM-5.3-Flash lists at $0.15 and $0.50.

Is GLM-5.3-Flash or gpt-oss-20b better for coding?

GLM-5.3-Flash scores higher on coding benchmarks: 53.1 versus 37.6 in the Noometry coding category.

Which has the bigger context window?

GLM-5.3-Flash does, with 1M tokens against 131K.

How many benchmarks do GLM-5.3-Flash and gpt-oss-20b share?

23 benchmarks have published results for both models. GLM-5.3-Flash has 40 scored results on Noometry and gpt-oss-20b has 34.

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