# GLM-5.3 vs gpt-oss-120b

> GLM-5.3 is the stronger model overall, scoring 54.6 to 36.0 on the Noometry Index. gpt-oss-120b costs 31× less per token, which makes it the better buy when GLM-5.3's lead doesn't matter for your workload.

- Canonical page: https://noometry.com/compare/glm-5-3-vs-gpt-oss-120b
- Last updated: 2026-10-10
- Shared benchmarks: 31

## Summary

- They share 31 benchmarks with published results for both. GLM-5.3 scores higher in 9 categories and gpt-oss-120b in 0 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in writing & preference, where GLM-5.3 leads 75.9 to 46.7.
- The biggest single-benchmark swing is APEX-Agents: 56.6% for GLM-5.3 and 4.4% for gpt-oss-120b.
- gpt-oss-120b is cheaper at $0.037 / $0.17 per million input/output tokens, against $1.40 / $4.40 for GLM-5.3.
- GLM-5.3 accepts more context: 1M tokens versus 131K.

## Snapshot

| | GLM-5.3 | gpt-oss-120b |
|---|---|---|
| Provider | Z.ai (Zhipu) | OpenAI |
| Noometry Index | 54.6 | 36.0 |
| Rank | 25 | 210 |
| Context | 1M | 131K |
| Input $/M | $1.40 | $0.037 |
| Output $/M | $4.40 | $0.17 |
| Weights | Open | Open |

## Coding

- GLM-5.3: 59.0 (#12)
- gpt-oss-120b: 33.2 (#251)

| Benchmark | GLM-5.3 | gpt-oss-120b |
|---|---|---|
| SciCode | 59% | 36% |
| WeirdML | 75.4% | 48.2% |
| LMArena Coding | 1499 | 1380 |
| ALE-Bench | 1,317 | 575.62 |
| DeepSWE | 69% | — |
| FrontierCode | 40.1% | — |
| SWE-bench Verified (bash only) | — | 26% |
| Aider Polyglot | — | 41.8% |
| LMArena WebDev | 1623 | — |
| FrontierSWE | 30.2% | — |
| AlgoTune | — | 1.41 |

## Agentic & Tool Use

- GLM-5.3: 36.3 (#34)
- gpt-oss-120b: 12.1 (#145)

| Benchmark | GLM-5.3 | gpt-oss-120b |
|---|---|---|
| APEX-Agents | 56.6% | 4.4% |
| Vending-Bench 2 | 8,164 | -21.53 |
| Terminal-Bench | — | 18.7% |
| METR Time Horizons | — | 56.6% |

## Reasoning

- GLM-5.3: 45.2 (#42)
- gpt-oss-120b: 19.5 (#236)

| Benchmark | GLM-5.3 | gpt-oss-120b |
|---|---|---|
| CritPt | 19.1% | 1.1% |
| Chess Puzzles | 21% | 20% |
| LMArena Hard Prompts | 1491 | 1364 |
| Mystery Game Puzzles | 33% | 2% |
| DTBench | 87.7% | 76.3% |
| LMCA | 55.5% | 22.1% |
| Epoch Capabilities Index | 155.56 | 139.94 |
| SimpleBench | — | 22.1% |
| Kagi LLM Benchmark | — | 58.6% |
| NYT Connections (extended) | 74.2% | — |
| Surface Evolver Bench | — | 25% |
| Bench to the Future 3 | 0.15 | — |

## Math

- GLM-5.3: 62.1 (#27)
- gpt-oss-120b: 50.6 (#43)

| Benchmark | GLM-5.3 | gpt-oss-120b |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 91.1% | 88.9% |
| LMArena Math | 1493 | 1388 |
| FrontierMath (Tiers 1-3) | 68.8% | — |
| FrontierMath Tier 4 | 29.3% | — |
| ProofBench | 49% | — |
| Omni-MATH | — | 68.8% |

## Knowledge

- GLM-5.3: 58.3 (#34)
- gpt-oss-120b: 42.3 (#89)

| Benchmark | GLM-5.3 | gpt-oss-120b |
|---|---|---|
| GPQA Diamond | 90.9% | 75.8% |
| LMArena Expert | 1512 | 1356 |
| SimpleQA Verified | 41% | — |
| MMLU-Pro | — | 79.5% |
| Confabulations | — | 15.7% |
| Vectara Hallucination Rate | — | 14.2% |
| GPQA (HELM) | — | 68.4% |

## Multilingual

- GLM-5.3: 55.9 (#27)
- gpt-oss-120b: 48.2 (#142)

| Benchmark | GLM-5.3 | gpt-oss-120b |
|---|---|---|
| LMArena Non-English | 1457 | 1350 |
| LMArena Chinese | 1523 | 1384 |
| LMArena French | 1499 | 1370 |
| LMArena German | 1505 | 1355 |
| LMArena Japanese | 1438 | 1332 |
| LMArena Korean | 1478 | 1283 |
| LMArena Russian | 1460 | 1343 |
| LMArena Spanish | 1444 | 1388 |

## Instruction Following

- GLM-5.3: 77.8 (#15)
- gpt-oss-120b: 69.5 (#168)

| Benchmark | GLM-5.3 | gpt-oss-120b |
|---|---|---|
| LMArena Instruction Following | 1481 | 1318 |
| IFEval | — | 83.6% |

## Long Context

- GLM-5.3: 45.5 (#32)
- gpt-oss-120b: 31.4 (#273)

| Benchmark | GLM-5.3 | gpt-oss-120b |
|---|---|---|
| LMArena Longer Query | 1485 | 1319 |
| Fiction.LiveBench | — | 44.4% |

## Writing & Preference

- GLM-5.3: 75.9 (#6)
- gpt-oss-120b: 46.7 (#213)

| Benchmark | GLM-5.3 | gpt-oss-120b |
|---|---|---|
| LMArena Text | 1473 | 1366 |
| LMArena Creative Writing | 1459 | 1274 |
| EQ-Bench Creative Writing | 2075 | 961 |
| LMArena Multi-Turn | 1473 | 1340 |
| Short-Story Creative Writing | — | 77.1% |
| WildBench | — | 84.5% |

## FAQ

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

GLM-5.3 is the stronger model overall, scoring 54.6 to 36.0 on the Noometry Index. gpt-oss-120b costs 31× less per token, which makes it the better buy when GLM-5.3's lead doesn't matter for your workload.

### Which is cheaper, GLM-5.3 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 lists at $1.40 and $4.40.

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

GLM-5.3 scores higher on coding benchmarks: 59.0 versus 33.2 in the Noometry coding category.

### Which has the bigger context window?

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

### How many benchmarks do GLM-5.3 and gpt-oss-120b share?

31 benchmarks have published results for both models. GLM-5.3 has 41 scored results on Noometry and gpt-oss-120b has 48.