# GLM-5.3-Flash vs gpt-oss-120b

> GLM-5.3-Flash is the stronger model overall, scoring 49.6 to 36.0 on the Noometry Index. gpt-oss-120b costs 3.4× less per token, which makes it the better buy when GLM-5.3-Flash's lead doesn't matter for your workload.

- Canonical page: https://noometry.com/compare/glm-5-3-flash-vs-gpt-oss-120b
- Last updated: 2026-10-10
- Shared benchmarks: 27

## Summary

- They share 27 benchmarks with published results for both. GLM-5.3-Flash scores higher in 9 categories and gpt-oss-120b in 0 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in agentic & tool use, where GLM-5.3-Flash leads 34.3 to 12.1.
- The biggest single-benchmark swing is APEX-Agents: 52.8% for GLM-5.3-Flash and 4.4% for gpt-oss-120b.
- gpt-oss-120b is cheaper at $0.037 / $0.17 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.

## Snapshot

| | GLM-5.3-Flash | gpt-oss-120b |
|---|---|---|
| Provider | Z.ai (Zhipu) | OpenAI |
| Noometry Index | 49.6 | 36.0 |
| Rank | 45 | 210 |
| Context | 1M | 131K |
| Input $/M | $0.15 | $0.037 |
| Output $/M | $0.50 | $0.17 |
| Weights | Open | Open |

## Coding

- GLM-5.3-Flash: 53.7 (#26)
- gpt-oss-120b: 33.2 (#251)

| Benchmark | GLM-5.3-Flash | gpt-oss-120b |
|---|---|---|
| SciCode | 51.6% | 36% |
| LMArena Coding | 1506 | 1380 |
| ALE-Bench | 303.55 | 575.62 |
| DeepSWE | 63.4% | — |
| FrontierCode | 31.8% | — |
| SWE-bench Verified (bash only) | — | 26% |
| Aider Polyglot | — | 41.8% |
| LMArena WebDev | 1614 | — |
| WeirdML | — | 48.2% |
| AlgoTune | — | 1.41 |

## Agentic & Tool Use

- GLM-5.3-Flash: 34.3 (#41)
- gpt-oss-120b: 12.1 (#145)

| Benchmark | GLM-5.3-Flash | gpt-oss-120b |
|---|---|---|
| APEX-Agents | 52.8% | 4.4% |
| Terminal-Bench | — | 18.7% |
| GDP.pdf | 14% | — |
| METR Time Horizons | — | 56.6% |
| Vending-Bench 2 | — | -21.53 |

## Reasoning

- GLM-5.3-Flash: 31.7 (#69)
- gpt-oss-120b: 19.5 (#236)

| Benchmark | GLM-5.3-Flash | gpt-oss-120b |
|---|---|---|
| CritPt | 15.4% | 1.1% |
| Chess Puzzles | 14% | 20% |
| LMArena Hard Prompts | 1490 | 1364 |
| Mystery Game Puzzles | 8% | 2% |
| Surface Evolver Bench | 52.5% | 25% |
| Epoch Capabilities Index | 151.9 | 139.94 |
| SimpleBench | — | 22.1% |
| Kagi LLM Benchmark | — | 58.6% |
| DTBench | — | 76.3% |
| LMCA | — | 22.1% |
| Bench to the Future 3 | 0.15 | — |

## Math

- GLM-5.3-Flash: 53.1 (#38)
- gpt-oss-120b: 50.6 (#43)

| Benchmark | GLM-5.3-Flash | gpt-oss-120b |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 93.9% | 88.9% |
| LMArena Math | 1499 | 1388 |
| FrontierMath (Tiers 1-3) | 55.8% | — |
| FrontierMath Tier 4 | 17.1% | — |
| ProofBench | 21% | — |
| Omni-MATH | — | 68.8% |

## Knowledge

- GLM-5.3-Flash: 58.4 (#33)
- gpt-oss-120b: 42.3 (#89)

| Benchmark | GLM-5.3-Flash | gpt-oss-120b |
|---|---|---|
| GPQA Diamond | 90.2% | 75.8% |
| LMArena Expert | 1512 | 1356 |
| MMLU-Pro | — | 79.5% |
| Confabulations | — | 15.7% |
| Vectara Hallucination Rate | — | 14.2% |
| GPQA (HELM) | — | 68.4% |

## Multimodal

- GLM-5.3-Flash: 42.6 (#25)
- gpt-oss-120b: —

| Benchmark | GLM-5.3-Flash | gpt-oss-120b |
|---|---|---|
| LMArena Vision | 1304 | — |

## Multilingual

- GLM-5.3-Flash: 56.2 (#24)
- gpt-oss-120b: 48.2 (#142)

| Benchmark | GLM-5.3-Flash | gpt-oss-120b |
|---|---|---|
| LMArena Non-English | 1462 | 1350 |
| LMArena Chinese | 1529 | 1384 |
| LMArena French | 1495 | 1370 |
| LMArena German | 1457 | 1355 |
| LMArena Japanese | 1420 | 1332 |
| LMArena Korean | 1443 | 1283 |
| LMArena Russian | 1472 | 1343 |
| LMArena Spanish | 1471 | 1388 |

## Instruction Following

- GLM-5.3-Flash: 77.8 (#17)
- gpt-oss-120b: 69.5 (#168)

| Benchmark | GLM-5.3-Flash | gpt-oss-120b |
|---|---|---|
| LMArena Instruction Following | 1480 | 1318 |
| IFEval | — | 83.6% |

## Long Context

- GLM-5.3-Flash: 45.3 (#41)
- gpt-oss-120b: 31.4 (#273)

| Benchmark | GLM-5.3-Flash | gpt-oss-120b |
|---|---|---|
| LMArena Longer Query | 1479 | 1319 |
| Fiction.LiveBench | — | 44.4% |

## Writing & Preference

- GLM-5.3-Flash: 65.4 (#49)
- gpt-oss-120b: 46.7 (#213)

| Benchmark | GLM-5.3-Flash | gpt-oss-120b |
|---|---|---|
| LMArena Text | 1471 | 1366 |
| LMArena Creative Writing | 1440 | 1274 |
| LMArena Multi-Turn | 1471 | 1340 |
| Short-Story Creative Writing | — | 77.1% |
| EQ-Bench Creative Writing | — | 961 |
| WildBench | — | 84.5% |

## FAQ

### Is GLM-5.3-Flash better than gpt-oss-120b?

GLM-5.3-Flash is the stronger model overall, scoring 49.6 to 36.0 on the Noometry Index. gpt-oss-120b costs 3.4× 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-120b?

gpt-oss-120b is cheaper. It lists at $0.037 per million input tokens and $0.17 per million output tokens; GLM-5.3-Flash lists at $0.15 and $0.50.

### Is GLM-5.3-Flash or gpt-oss-120b better for coding?

GLM-5.3-Flash scores higher on coding benchmarks: 53.7 versus 33.2 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-120b share?

27 benchmarks have published results for both models. GLM-5.3-Flash has 36 scored results on Noometry and gpt-oss-120b has 48.