# GLM-4.7-Flash vs gpt-oss-20b

> GLM-4.7-Flash is the stronger model overall, scoring 38.4 to 32.1 on the Noometry Index. gpt-oss-20b costs 4.0× 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-20b
- Last updated: 2026-10-10
- Shared benchmarks: 19

## Summary

- They share 19 benchmarks with published results for both. GLM-4.7-Flash scores higher in 7 categories and gpt-oss-20b in 1 category; 7 gaps are clear of the uncertainty.
- The widest gap is in writing & preference, where GLM-4.7-Flash leads 47.7 to 35.8.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 58.3% for GLM-4.7-Flash and 65.3% for gpt-oss-20b.
- gpt-oss-20b is cheaper at $0.018 / $0.09 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-20b |
|---|---|---|
| Provider | Z.ai (Zhipu) | OpenAI |
| Noometry Index | 38.4 | 32.1 |
| Rank | 175 | 249 |
| Context | 200K | 131K |
| Input $/M | $0.06 | $0.018 |
| Output $/M | $0.40 | $0.09 |
| Weights | Open | Open |

## Coding

- GLM-4.7-Flash: 40.0 (#133)
- gpt-oss-20b: 37.1 (#185)

| Benchmark | GLM-4.7-Flash | gpt-oss-20b |
|---|---|---|
| LMArena Coding | 1383 | 1307 |
| SciCode | — | 34.4% |
| WeirdML | — | 40.9% |
| ALE-Bench | — | 566.05 |

## Agentic & Tool Use

- GLM-4.7-Flash: —
- gpt-oss-20b: 9.2 (#146)

| Benchmark | GLM-4.7-Flash | gpt-oss-20b |
|---|---|---|
| Terminal-Bench | — | 3.4% |

## Reasoning

- GLM-4.7-Flash: 20.2 (#224)
- gpt-oss-20b: 18.8 (#251)

| Benchmark | GLM-4.7-Flash | gpt-oss-20b |
|---|---|---|
| Chess Puzzles | 0% | 4% |
| LMArena Hard Prompts | 1356 | 1274 |
| Kagi LLM Benchmark | — | 53.2% |
| CritPt | — | 1.4% |
| DTBench | — | 68% |
| LMCA | — | 14.5% |
| Epoch Capabilities Index | — | 137.81 |

## Math

- GLM-4.7-Flash: 34.4 (#159)
- gpt-oss-20b: 37.6 (#93)

| Benchmark | GLM-4.7-Flash | gpt-oss-20b |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 58.3% | 65.3% |
| LMArena Math | 1354 | 1317 |
| Omni-MATH | — | 56.5% |

## Knowledge

- GLM-4.7-Flash: 35.4 (#177)
- gpt-oss-20b: 34.6 (#186)

| Benchmark | GLM-4.7-Flash | gpt-oss-20b |
|---|---|---|
| GPQA Diamond | 60.5% | 60.8% |
| LMArena Expert | 1357 | 1258 |
| MMLU-Pro | — | 74% |
| Vectara Hallucination Rate | 9.3% | — |
| GPQA (HELM) | — | 59.4% |

## Multilingual

- GLM-4.7-Flash: 46.8 (#152)
- gpt-oss-20b: 42.4 (#192)

| Benchmark | GLM-4.7-Flash | gpt-oss-20b |
|---|---|---|
| LMArena Non-English | 1331 | 1269 |
| LMArena Chinese | 1403 | 1311 |
| LMArena German | 1336 | 1249 |
| LMArena Korean | 1284 | 1239 |
| LMArena Russian | 1333 | 1279 |
| LMArena Spanish | 1351 | 1267 |
| LMArena French | 1332 | — |
| LMArena Japanese | — | 1245 |

## Instruction Following

- GLM-4.7-Flash: 70.3 (#162)
- gpt-oss-20b: 62.0 (#235)

| Benchmark | GLM-4.7-Flash | gpt-oss-20b |
|---|---|---|
| LMArena Instruction Following | 1327 | 1236 |
| IFEval | — | 73.2% |

## Long Context

- GLM-4.7-Flash: 40.9 (#143)
- gpt-oss-20b: 37.9 (#203)

| Benchmark | GLM-4.7-Flash | gpt-oss-20b |
|---|---|---|
| LMArena Longer Query | 1345 | 1251 |

## Writing & Preference

- GLM-4.7-Flash: 47.7 (#206)
- gpt-oss-20b: 35.8 (#260)

| Benchmark | GLM-4.7-Flash | gpt-oss-20b |
|---|---|---|
| LMArena Text | 1351 | 1288 |
| LMArena Creative Writing | 1297 | 1201 |
| EQ-Bench Creative Writing | 1125 | 666 |
| LMArena Multi-Turn | 1342 | 1268 |
| WildBench | — | 73.7% |

## FAQ

### Is GLM-4.7-Flash better than gpt-oss-20b?

GLM-4.7-Flash is the stronger model overall, scoring 38.4 to 32.1 on the Noometry Index. gpt-oss-20b costs 4.0× 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-20b?

gpt-oss-20b is cheaper. It lists at $0.018 per million input tokens and $0.09 per million output tokens; GLM-4.7-Flash lists at $0.06 and $0.40.

### Is GLM-4.7-Flash or gpt-oss-20b better for coding?

GLM-4.7-Flash scores higher on coding benchmarks: 40.0 versus 37.1 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-20b share?

19 benchmarks have published results for both models. GLM-4.7-Flash has 21 scored results on Noometry and gpt-oss-20b has 34.