# GLM-4.7-Flash vs gpt-oss-120b

> GLM-4.7-Flash is the stronger model overall, scoring 38.4 to 36.0 on the Noometry Index. gpt-oss-120b costs 2.1× less per token, which makes it the better buy when GLM-4.7-Flash's lead doesn't matter for your workload.

- Canonical page: https://noometry.com/compare/glm-4-7-flash-vs-gpt-oss-120b
- Last updated: 2026-10-10
- Shared benchmarks: 21

## Summary

- They share 21 benchmarks with published results for both. GLM-4.7-Flash scores higher in 5 categories and gpt-oss-120b in 3 categories; 5 gaps are clear of the uncertainty.
- The widest gap is in math, where gpt-oss-120b leads 50.6 to 34.4.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 58.3% for GLM-4.7-Flash and 88.9% for gpt-oss-120b.
- gpt-oss-120b is cheaper at $0.037 / $0.17 per million input/output tokens, against $0.06 / $0.40 for GLM-4.7-Flash.
- GLM-4.7-Flash accepts more context: 200K tokens versus 131K.

## Snapshot

| | GLM-4.7-Flash | gpt-oss-120b |
|---|---|---|
| Provider | Z.ai (Zhipu) | OpenAI |
| Noometry Index | 38.4 | 36.0 |
| Rank | 175 | 210 |
| Context | 200K | 131K |
| Input $/M | $0.06 | $0.037 |
| Output $/M | $0.40 | $0.17 |
| Weights | Open | Open |

## Coding

- GLM-4.7-Flash: 40.0 (#133)
- gpt-oss-120b: 33.2 (#251)

| Benchmark | GLM-4.7-Flash | gpt-oss-120b |
|---|---|---|
| LMArena Coding | 1383 | 1380 |
| SWE-bench Verified (bash only) | — | 26% |
| Aider Polyglot | — | 41.8% |
| SciCode | — | 36% |
| WeirdML | — | 48.2% |
| ALE-Bench | — | 575.62 |
| AlgoTune | — | 1.41 |

## Agentic & Tool Use

- GLM-4.7-Flash: —
- gpt-oss-120b: 12.1 (#145)

| Benchmark | GLM-4.7-Flash | gpt-oss-120b |
|---|---|---|
| Terminal-Bench | — | 18.7% |
| APEX-Agents | — | 4.4% |
| METR Time Horizons | — | 56.6% |
| Vending-Bench 2 | — | -21.53 |

## Reasoning

- GLM-4.7-Flash: 20.2 (#224)
- gpt-oss-120b: 19.5 (#236)

| Benchmark | GLM-4.7-Flash | gpt-oss-120b |
|---|---|---|
| Chess Puzzles | 0% | 20% |
| LMArena Hard Prompts | 1356 | 1364 |
| SimpleBench | — | 22.1% |
| Kagi LLM Benchmark | — | 58.6% |
| CritPt | — | 1.1% |
| Mystery Game Puzzles | — | 2% |
| DTBench | — | 76.3% |
| LMCA | — | 22.1% |
| Surface Evolver Bench | — | 25% |
| Epoch Capabilities Index | — | 139.94 |

## Math

- GLM-4.7-Flash: 34.4 (#159)
- gpt-oss-120b: 50.6 (#43)

| Benchmark | GLM-4.7-Flash | gpt-oss-120b |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 58.3% | 88.9% |
| LMArena Math | 1354 | 1388 |
| Omni-MATH | — | 68.8% |

## Knowledge

- GLM-4.7-Flash: 35.4 (#177)
- gpt-oss-120b: 42.3 (#89)

| Benchmark | GLM-4.7-Flash | gpt-oss-120b |
|---|---|---|
| GPQA Diamond | 60.5% | 75.8% |
| Vectara Hallucination Rate | 9.3% | 14.2% |
| LMArena Expert | 1357 | 1356 |
| MMLU-Pro | — | 79.5% |
| Confabulations | — | 15.7% |
| GPQA (HELM) | — | 68.4% |

## Multilingual

- GLM-4.7-Flash: 46.8 (#152)
- gpt-oss-120b: 48.2 (#142)

| Benchmark | GLM-4.7-Flash | gpt-oss-120b |
|---|---|---|
| LMArena Non-English | 1331 | 1350 |
| LMArena Chinese | 1403 | 1384 |
| LMArena French | 1332 | 1370 |
| LMArena German | 1336 | 1355 |
| LMArena Korean | 1284 | 1283 |
| LMArena Russian | 1333 | 1343 |
| LMArena Spanish | 1351 | 1388 |
| LMArena Japanese | — | 1332 |

## Instruction Following

- GLM-4.7-Flash: 70.3 (#162)
- gpt-oss-120b: 69.5 (#168)

| Benchmark | GLM-4.7-Flash | gpt-oss-120b |
|---|---|---|
| LMArena Instruction Following | 1327 | 1318 |
| IFEval | — | 83.6% |

## Long Context

- GLM-4.7-Flash: 40.9 (#143)
- gpt-oss-120b: 31.4 (#273)

| Benchmark | GLM-4.7-Flash | gpt-oss-120b |
|---|---|---|
| LMArena Longer Query | 1345 | 1319 |
| Fiction.LiveBench | — | 44.4% |

## Writing & Preference

- GLM-4.7-Flash: 47.7 (#206)
- gpt-oss-120b: 46.7 (#213)

| Benchmark | GLM-4.7-Flash | gpt-oss-120b |
|---|---|---|
| LMArena Text | 1351 | 1366 |
| LMArena Creative Writing | 1297 | 1274 |
| EQ-Bench Creative Writing | 1125 | 961 |
| LMArena Multi-Turn | 1342 | 1340 |
| Short-Story Creative Writing | — | 77.1% |
| WildBench | — | 84.5% |

## FAQ

### Is GLM-4.7-Flash better than gpt-oss-120b?

GLM-4.7-Flash is the stronger model overall, scoring 38.4 to 36.0 on the Noometry Index. gpt-oss-120b costs 2.1× less per token, which makes it the better buy when GLM-4.7-Flash's lead doesn't matter for your workload.

### Which is cheaper, GLM-4.7-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-4.7-Flash lists at $0.06 and $0.40.

### Is GLM-4.7-Flash or gpt-oss-120b better for coding?

GLM-4.7-Flash scores higher on coding benchmarks: 40.0 versus 33.2 in the Noometry coding category.

### Which has the bigger context window?

GLM-4.7-Flash does, with 200K tokens against 131K.

### How many benchmarks do GLM-4.7-Flash and gpt-oss-120b share?

21 benchmarks have published results for both models. GLM-4.7-Flash has 21 scored results on Noometry and gpt-oss-120b has 48.