# GLM-4.7-Flash vs GPT-4o

> GLM-4.7-Flash is the stronger model overall, scoring 38.4 to 28.5 on the Noometry Index.

- Canonical page: https://noometry.com/compare/glm-4-7-flash-vs-gpt-4o
- Last updated: 2026-10-10
- Shared benchmarks: 20

## Summary

- They share 20 benchmarks with published results for both. GLM-4.7-Flash scores higher in 7 categories and GPT-4o in 1 category; 8 gaps are clear of the uncertainty.
- The widest gap is in math, where GLM-4.7-Flash leads 34.4 to 10.2.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 58.3% for GLM-4.7-Flash and 6.4% for GPT-4o.
- GLM-4.7-Flash is cheaper at $0.06 / $0.40 per million input/output tokens, against $2.50 / $10 for GPT-4o.
- GLM-4.7-Flash accepts more context: 200K tokens versus 128K.
- GLM-4.7-Flash has downloadable open weights; the other is API-only.

## Snapshot

| | GLM-4.7-Flash | GPT-4o |
|---|---|---|
| Provider | Z.ai (Zhipu) | OpenAI |
| Noometry Index | 38.4 | 28.5 |
| Rank | 175 | 316 |
| Context | 200K | 128K |
| Input $/M | $0.06 | $2.50 |
| Output $/M | $0.40 | $10 |
| Weights | Open | Proprietary |

## Coding

- GLM-4.7-Flash: 40.0 (#133)
- GPT-4o: 25.1 (#323)

| Benchmark | GLM-4.7-Flash | GPT-4o |
|---|---|---|
| LMArena Coding | 1383 | 1297 |
| SWE-bench Verified | — | 31% |
| SWE-bench Verified (bash only) | — | 21.6% |
| Aider Polyglot | — | 45.3% |
| GSO | — | 0% |
| WeirdML | — | 25.1% |
| BigCodeBench Instruct | — | 51.1% |
| LiveBench Coding | — | 51.4% |
| BigCodeBench Complete | — | 61.1% |
| CadEval | — | 26% |
| HumanEval+ | — | 87.2% |
| MBPP+ | — | 72.2% |

## Agentic & Tool Use

- GLM-4.7-Flash: —
- GPT-4o: 20.8 (#133)

| Benchmark | GLM-4.7-Flash | GPT-4o |
|---|---|---|
| GDPval | — | 9.9% |
| TheAgentCompany | — | 8.6% |
| Cybench | — | 12.5% |
| BALROG | — | 32.3% |
| LMArena Search | — | 1006 |
| METR Time Horizons | — | 40.8% |

## Reasoning

- GLM-4.7-Flash: 20.2 (#224)
- GPT-4o: 9.4 (#338)

| Benchmark | GLM-4.7-Flash | GPT-4o |
|---|---|---|
| Chess Puzzles | 0% | 13% |
| LMArena Hard Prompts | 1356 | 1281 |
| ARC-AGI-2 | — | 0% |
| SimpleBench | — | 17.8% |
| ARC-AGI-1 | — | 4.5% |
| CritPt | — | 0% |
| EnigmaEval | — | 0.8% |
| LiveBench Reasoning | — | 55.8% |
| DTBench | — | 64.5% |
| LiveBench Data Analysis | — | 60.9% |
| LMCA | — | 16.6% |
| Epoch Capabilities Index | — | 128.97 |
| ForecastBench | — | 57.7 |
| LiveBench | — | 55.3% |

## Math

- GLM-4.7-Flash: 34.4 (#159)
- GPT-4o: 10.2 (#303)

| Benchmark | GLM-4.7-Flash | GPT-4o |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 58.3% | 6.4% |
| LMArena Math | 1354 | 1285 |
| FrontierMath (Tiers 1-3) | — | 0.4% |
| Omni-MATH | — | 29.3% |
| LiveBench Math | — | 49.5% |
| MATH Level 5 | — | 53.3% |
| FrontierMath (Feb 2025 set) | — | 0.3% |

## Knowledge

- GLM-4.7-Flash: 35.4 (#177)
- GPT-4o: 28.8 (#235)

| Benchmark | GLM-4.7-Flash | GPT-4o |
|---|---|---|
| GPQA Diamond | 60.5% | 49.2% |
| Vectara Hallucination Rate | 9.3% | 9.6% |
| LMArena Expert | 1357 | 1250 |
| Humanity's Last Exam | — | 2.7% |
| SimpleQA Verified | — | 26% |
| MMLU-Pro | — | 71.3% |
| Confabulations | — | 15.3% |
| GPQA (HELM) | — | 52% |
| MMLU | — | 88.1% |

## Multimodal

- GLM-4.7-Flash: —
- GPT-4o: 33.9 (#85)

| Benchmark | GLM-4.7-Flash | GPT-4o |
|---|---|---|
| LMArena Vision | — | 1137 |
| Video-MME | — | 71.9% |
| GeoBench | — | 71% |
| VPCT | — | 40% |
| ScienceQA | — | 88.5% |

## Multilingual

- GLM-4.7-Flash: 46.8 (#152)
- GPT-4o: 43.4 (#181)

| Benchmark | GLM-4.7-Flash | GPT-4o |
|---|---|---|
| LMArena Non-English | 1331 | 1283 |
| LMArena Chinese | 1403 | 1277 |
| LMArena French | 1332 | 1303 |
| LMArena German | 1336 | 1282 |
| LMArena Korean | 1284 | 1234 |
| LMArena Russian | 1333 | 1286 |
| LMArena Spanish | 1351 | 1292 |
| LMArena Japanese | — | 1257 |

## Instruction Following

- GLM-4.7-Flash: 70.3 (#162)
- GPT-4o: 66.8 (#202)

| Benchmark | GLM-4.7-Flash | GPT-4o |
|---|---|---|
| LMArena Instruction Following | 1327 | 1278 |
| LiveBench Instruction Following | — | 68.6% |
| IFEval | — | 81.7% |

## Long Context

- GLM-4.7-Flash: 40.9 (#143)
- GPT-4o: 39.4 (#174)

| Benchmark | GLM-4.7-Flash | GPT-4o |
|---|---|---|
| LMArena Longer Query | 1345 | 1289 |
| Fiction.LiveBench | — | 66.7% |

## Writing & Preference

- GLM-4.7-Flash: 47.7 (#206)
- GPT-4o: 52.8 (#160)

| Benchmark | GLM-4.7-Flash | GPT-4o |
|---|---|---|
| LMArena Text | 1351 | 1300 |
| LMArena Creative Writing | 1297 | 1291 |
| LMArena Multi-Turn | 1342 | 1302 |
| Short-Story Creative Writing | — | 81.8% |
| EQ-Bench Creative Writing | 1125 | — |
| WildBench | — | 82.8% |
| LiveBench Language | — | 47.6% |

## FAQ

### Is GLM-4.7-Flash better than GPT-4o?

GLM-4.7-Flash is the stronger model overall, scoring 38.4 to 28.5 on the Noometry Index.

### Which is cheaper, GLM-4.7-Flash or GPT-4o?

GLM-4.7-Flash is cheaper. It lists at $0.06 per million input tokens and $0.40 per million output tokens; GPT-4o lists at $2.50 and $10.

### Is GLM-4.7-Flash or GPT-4o better for coding?

GLM-4.7-Flash scores higher on coding benchmarks: 40.0 versus 25.1 in the Noometry coding category.

### Which has the bigger context window?

GLM-4.7-Flash does, with 200K tokens against 128K.

### How many benchmarks do GLM-4.7-Flash and GPT-4o share?

20 benchmarks have published results for both models. GLM-4.7-Flash has 21 scored results on Noometry and GPT-4o has 72.