# GLM-4.7-Flash vs GPT-5.6 Terra

> GPT-5.6 Terra is the stronger model overall, scoring 59.2 to 38.8 on the Noometry Index. GLM-4.7-Flash costs 31× less per token, which makes it the better buy when GPT-5.6 Terra's lead doesn't matter for your workload.

- Canonical page: https://noometry.com/compare/glm-4-7-flash-vs-gpt-5-6-terra
- 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 0 categories and GPT-5.6 Terra in 8 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in math, where GPT-5.6 Terra leads 81.6 to 36.1.
- The biggest single-benchmark swing is Chess Puzzles: 0% for GLM-4.7-Flash and 54% for GPT-5.6 Terra.
- GLM-4.7-Flash is cheaper at $0.06 / $0.40 per million input/output tokens, against $2 / $12 for GPT-5.6 Terra.
- GPT-5.6 Terra accepts more context: 1.05M tokens versus 200K.
- GLM-4.7-Flash has downloadable open weights; the other is API-only.

## Snapshot

| | GLM-4.7-Flash | GPT-5.6 Terra |
|---|---|---|
| Provider | Z.ai (Zhipu) | OpenAI |
| Noometry Index | 38.8 | 59.2 |
| Rank | 180 | 17 |
| Context | 200K | 1.05M |
| Input $/M | $0.06 | $2 |
| Output $/M | $0.40 | $12 |
| Weights | Open | Proprietary |

## Coding

- GLM-4.7-Flash: 40.6 (#135)
- GPT-5.6 Terra: 57.7 (#19)

| Benchmark | GLM-4.7-Flash | GPT-5.6 Terra |
|---|---|---|
| LMArena Coding | 1383 | 1484 |
| DeepSWE | — | 69.6% |
| FrontierCode | — | 41.3% |
| CursorBench | — | 41.3% |
| LMArena WebDev | — | 1522 |
| SciCode | — | 55% |
| WeirdML | — | 78.3% |
| ALE-Bench | — | 1,951 |

## Agentic & Tool Use

- GLM-4.7-Flash: —
- GPT-5.6 Terra: 40.1 (#25)

| Benchmark | GLM-4.7-Flash | GPT-5.6 Terra |
|---|---|---|
| APEX-Agents | — | 58.2% |
| BALROG | — | 53.2% |
| GDP.pdf | — | 24.7% |
| Vending-Bench 2 | — | 7,343 |

## Reasoning

- GLM-4.7-Flash: 20.9 (#229)
- GPT-5.6 Terra: 60.7 (#21)

| Benchmark | GLM-4.7-Flash | GPT-5.6 Terra |
|---|---|---|
| Chess Puzzles | 0% | 54% |
| LMArena Hard Prompts | 1356 | 1468 |
| ARC-AGI-2 | — | 83.9% |
| SimpleBench | — | 48.9% |
| Kagi LLM Benchmark | — | 51.3% |
| NYT Connections (extended) | — | 78.4% |
| ARC-AGI-1 | — | 96.5% |
| CritPt | — | 30% |
| Mystery Game Puzzles | — | 35% |
| DTBench | — | 93.3% |
| LMCA | — | 55% |
| Surface Evolver Bench | — | 83.8% |
| Epoch Capabilities Index | — | 159.62 |

## Math

- GLM-4.7-Flash: 36.1 (#173)
- GPT-5.6 Terra: 81.6 (#12)

| Benchmark | GLM-4.7-Flash | GPT-5.6 Terra |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 58.3% | 99.7% |
| LMArena Math | 1355 | 1466 |
| FrontierMath (Tiers 1-3) | — | 86% |
| FrontierMath Tier 4 | — | 70.7% |
| ProofBench | — | 74% |

## Knowledge

- GLM-4.7-Flash: 35.5 (#184)
- GPT-5.6 Terra: 61.2 (#30)

| Benchmark | GLM-4.7-Flash | GPT-5.6 Terra |
|---|---|---|
| GPQA Diamond | 60.5% | 93.3% |
| LMArena Expert | 1357 | 1492 |
| SimpleQA Verified | — | 43.2% |
| Vectara Hallucination Rate | 9.3% | — |

## Multimodal

- GLM-4.7-Flash: —
- GPT-5.6 Terra: 47.3 (#11)

| Benchmark | GLM-4.7-Flash | GPT-5.6 Terra |
|---|---|---|
| LMArena Vision | — | 1271 |
| Blueprint-Bench 2 | — | 30.8% |
| Furniture Assembly | — | 54.2% |
| LMArena Document | — | 1472 |

## Multilingual

- GLM-4.7-Flash: 46.5 (#158)
- GPT-5.6 Terra: 54.4 (#44)

| Benchmark | GLM-4.7-Flash | GPT-5.6 Terra |
|---|---|---|
| LMArena Non-English | 1330 | 1439 |
| LMArena Chinese | 1403 | 1513 |
| LMArena French | 1332 | 1471 |
| LMArena German | 1337 | 1460 |
| LMArena Korean | 1283 | 1425 |
| LMArena Russian | 1332 | 1450 |
| LMArena Spanish | 1350 | 1448 |
| LMArena Japanese | — | 1457 |

## Instruction Following

- GLM-4.7-Flash: 70.1 (#167)
- GPT-5.6 Terra: 76.4 (#40)

| Benchmark | GLM-4.7-Flash | GPT-5.6 Terra |
|---|---|---|
| LMArena Instruction Following | 1327 | 1454 |

## Long Context

- GLM-4.7-Flash: 40.9 (#148)
- GPT-5.6 Terra: 44.4 (#68)

| Benchmark | GLM-4.7-Flash | GPT-5.6 Terra |
|---|---|---|
| LMArena Longer Query | 1345 | 1451 |

## Writing & Preference

- GLM-4.7-Flash: 47.4 (#210)
- GPT-5.6 Terra: 70.2 (#23)

| Benchmark | GLM-4.7-Flash | GPT-5.6 Terra |
|---|---|---|
| LMArena Text | 1351 | 1447 |
| LMArena Creative Writing | 1297 | 1410 |
| EQ-Bench Creative Writing | 1125 | 1855 |
| LMArena Multi-Turn | 1342 | 1449 |
| EQ-Bench 4 | — | 1234 |

## FAQ

### Is GLM-4.7-Flash better than GPT-5.6 Terra?

GPT-5.6 Terra is the stronger model overall, scoring 59.2 to 38.8 on the Noometry Index. GLM-4.7-Flash costs 31× less per token, which makes it the better buy when GPT-5.6 Terra's lead doesn't matter for your workload.

### Which is cheaper, GLM-4.7-Flash or GPT-5.6 Terra?

GLM-4.7-Flash is cheaper. It lists at $0.06 per million input tokens and $0.40 per million output tokens; GPT-5.6 Terra lists at $2 and $12.

### Is GLM-4.7-Flash or GPT-5.6 Terra better for coding?

GPT-5.6 Terra scores higher on coding benchmarks: 57.7 versus 40.6 in the Noometry coding category.

### Which has the bigger context window?

GPT-5.6 Terra does, with 1.05M tokens against 200K.

### How many benchmarks do GLM-4.7-Flash and GPT-5.6 Terra share?

20 benchmarks have published results for both models. GLM-4.7-Flash has 21 scored results on Noometry and GPT-5.6 Terra has 52.