# GLM-4.6 vs gpt-oss-120b

> GLM-4.6 is the stronger model overall, scoring 40.9 to 36.0 on the Noometry Index. gpt-oss-120b costs 14× less per token, which makes it the better buy when GLM-4.6's lead doesn't matter for your workload.

- Canonical page: https://noometry.com/compare/glm-4-6-vs-gpt-oss-120b
- Last updated: 2026-10-10
- Shared benchmarks: 25

## Summary

- They share 25 benchmarks with published results for both. GLM-4.6 scores higher in 7 categories and gpt-oss-120b in 2 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in agentic & tool use, where GLM-4.6 leads 32.1 to 12.1.
- The biggest single-benchmark swing is SWE-bench Verified (bash only): 55.4% for GLM-4.6 and 26% for gpt-oss-120b.
- gpt-oss-120b is cheaper at $0.037 / $0.17 per million input/output tokens, against $0.60 / $2.20 for GLM-4.6.
- GLM-4.6 accepts more context: 205K tokens versus 131K.

## Snapshot

| | GLM-4.6 | gpt-oss-120b |
|---|---|---|
| Provider | Z.ai (Zhipu) | OpenAI |
| Noometry Index | 40.9 | 36.0 |
| Rank | 132 | 210 |
| Context | 205K | 131K |
| Input $/M | $0.60 | $0.037 |
| Output $/M | $2.20 | $0.17 |
| Weights | Open | Open |

## Coding

- GLM-4.6: 39.9 (#137)
- gpt-oss-120b: 33.2 (#251)

| Benchmark | GLM-4.6 | gpt-oss-120b |
|---|---|---|
| SWE-bench Verified (bash only) | 55.4% | 26% |
| SciCode | 38.4% | 36% |
| LMArena Coding | 1449 | 1380 |
| ALE-Bench | 340.82 | 575.62 |
| Aider Polyglot | — | 41.8% |
| LMArena WebDev | 1339 | — |
| WeirdML | — | 48.2% |
| AlgoTune | — | 1.41 |

## Agentic & Tool Use

- GLM-4.6: 32.1 (#60)
- gpt-oss-120b: 12.1 (#145)

| Benchmark | GLM-4.6 | gpt-oss-120b |
|---|---|---|
| Terminal-Bench | 24.5% | 18.7% |
| APEX-Agents | — | 4.4% |
| Berkeley Function Calling Leaderboard | 72.4% | — |
| METR Time Horizons | — | 56.6% |
| Vending-Bench 2 | — | -21.53 |

## Reasoning

- GLM-4.6: 23.1 (#168)
- gpt-oss-120b: 19.5 (#236)

| Benchmark | GLM-4.6 | gpt-oss-120b |
|---|---|---|
| Kagi LLM Benchmark | 47.4% | 58.6% |
| CritPt | 1.1% | 1.1% |
| LMArena Hard Prompts | 1440 | 1364 |
| SimpleBench | — | 22.1% |
| Chess Puzzles | — | 20% |
| Mystery Game Puzzles | — | 2% |
| DTBench | — | 76.3% |
| LMCA | — | 22.1% |
| Surface Evolver Bench | — | 25% |
| Epoch Capabilities Index | — | 139.94 |

## Math

- GLM-4.6: 36.4 (#115)
- gpt-oss-120b: 50.6 (#43)

| Benchmark | GLM-4.6 | gpt-oss-120b |
|---|---|---|
| LMArena Math | 1431 | 1388 |
| OTIS Mock AIME 2024-2025 | — | 88.9% |
| Omni-MATH | — | 68.8% |
| FrontierMath (Feb 2025 set) | 3.8% | — |
| FrontierMath Tier 4 (v1) | 2.1% | — |

## Knowledge

- GLM-4.6: 39.6 (#118)
- gpt-oss-120b: 42.3 (#89)

| Benchmark | GLM-4.6 | gpt-oss-120b |
|---|---|---|
| Vectara Hallucination Rate | 9.5% | 14.2% |
| LMArena Expert | 1432 | 1356 |
| GPQA Diamond | — | 75.8% |
| MMLU-Pro | — | 79.5% |
| Confabulations | — | 15.7% |
| GPQA (HELM) | — | 68.4% |

## Multilingual

- GLM-4.6: 53.6 (#62)
- gpt-oss-120b: 48.2 (#142)

| Benchmark | GLM-4.6 | gpt-oss-120b |
|---|---|---|
| LMArena Non-English | 1426 | 1350 |
| LMArena Chinese | 1500 | 1384 |
| LMArena French | 1459 | 1370 |
| LMArena German | 1447 | 1355 |
| LMArena Japanese | 1394 | 1332 |
| LMArena Korean | 1401 | 1283 |
| LMArena Russian | 1418 | 1343 |
| LMArena Spanish | 1436 | 1388 |

## Instruction Following

- GLM-4.6: 74.5 (#93)
- gpt-oss-120b: 69.5 (#168)

| Benchmark | GLM-4.6 | gpt-oss-120b |
|---|---|---|
| LMArena Instruction Following | 1410 | 1318 |
| IFEval | — | 83.6% |

## Long Context

- GLM-4.6: 43.4 (#91)
- gpt-oss-120b: 31.4 (#273)

| Benchmark | GLM-4.6 | gpt-oss-120b |
|---|---|---|
| LMArena Longer Query | 1423 | 1319 |
| Fiction.LiveBench | — | 44.4% |

## Writing & Preference

- GLM-4.6: 61.3 (#86)
- gpt-oss-120b: 46.7 (#213)

| Benchmark | GLM-4.6 | gpt-oss-120b |
|---|---|---|
| LMArena Text | 1440 | 1366 |
| LMArena Creative Writing | 1411 | 1274 |
| EQ-Bench Creative Writing | 1411 | 961 |
| LMArena Multi-Turn | 1427 | 1340 |
| Short-Story Creative Writing | — | 77.1% |
| WildBench | — | 84.5% |

## FAQ

### Is GLM-4.6 better than gpt-oss-120b?

GLM-4.6 is the stronger model overall, scoring 40.9 to 36.0 on the Noometry Index. gpt-oss-120b costs 14× less per token, which makes it the better buy when GLM-4.6's lead doesn't matter for your workload.

### Which is cheaper, GLM-4.6 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-4.6 lists at $0.60 and $2.20.

### Is GLM-4.6 or gpt-oss-120b better for coding?

GLM-4.6 scores higher on coding benchmarks: 39.9 versus 33.2 in the Noometry coding category.

### Which has the bigger context window?

GLM-4.6 does, with 205K tokens against 131K.

### How many benchmarks do GLM-4.6 and gpt-oss-120b share?

25 benchmarks have published results for both models. GLM-4.6 has 29 scored results on Noometry and gpt-oss-120b has 48.