# GLM-4.7-Flash vs Qwen3-30B-A3B

> GLM-4.7-Flash and Qwen3-30B-A3B score almost the same on the Noometry Index (38.8 vs 38.9), so choose on price, context window or the category you care about most.

- Canonical page: https://noometry.com/compare/glm-4-7-flash-vs-qwen3-30b-a3b
- 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 2 categories and Qwen3-30B-A3B in 6 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in long context, where GLM-4.7-Flash leads 40.9 to 31.0.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 58.3% for GLM-4.7-Flash and 70.3% for Qwen3-30B-A3B.
- GLM-4.7-Flash is cheaper at $0.06 / $0.40 per million input/output tokens, against $0.12 / $0.50 for Qwen3-30B-A3B.
- GLM-4.7-Flash accepts more context: 200K tokens versus 41K.

## Snapshot

| | GLM-4.7-Flash | Qwen3-30B-A3B |
|---|---|---|
| Provider | Z.ai (Zhipu) | Alibaba (Qwen) |
| Noometry Index | 38.8 | 38.9 |
| Rank | 180 | 179 |
| Context | 200K | 41K |
| Input $/M | $0.06 | $0.12 |
| Output $/M | $0.40 | $0.50 |
| Weights | Open | Open |

## Coding

- GLM-4.7-Flash: 40.6 (#135)
- Qwen3-30B-A3B: 37.5 (#194)

| Benchmark | GLM-4.7-Flash | Qwen3-30B-A3B |
|---|---|---|
| LMArena Coding | 1383 | 1416 |
| SciCode | — | 33.3% |
| WeirdML | — | 29.8% |

## Agentic & Tool Use

- GLM-4.7-Flash: —
- Qwen3-30B-A3B: 29.8 (#82)

| Benchmark | GLM-4.7-Flash | Qwen3-30B-A3B |
|---|---|---|
| Berkeley Function Calling Leaderboard | — | 41.4% |

## Reasoning

- GLM-4.7-Flash: 20.9 (#229)
- Qwen3-30B-A3B: 22.2 (#204)

| Benchmark | GLM-4.7-Flash | Qwen3-30B-A3B |
|---|---|---|
| Chess Puzzles | 0% | 8% |
| LMArena Hard Prompts | 1356 | 1398 |
| Kagi LLM Benchmark | — | 54.9% |
| CritPt | — | 0.3% |
| DTBench | — | 69.3% |
| LMCA | — | 22.4% |
| Epoch Capabilities Index | — | 139.63 |

## Math

- GLM-4.7-Flash: 36.1 (#173)
- Qwen3-30B-A3B: 37.4 (#157)

| Benchmark | GLM-4.7-Flash | Qwen3-30B-A3B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 58.3% | 70.3% |
| LMArena Math | 1355 | 1394 |
| MathArena Final-Answer Competitions | — | 47.8% |

## Knowledge

- GLM-4.7-Flash: 35.5 (#184)
- Qwen3-30B-A3B: 41.8 (#105)

| Benchmark | GLM-4.7-Flash | Qwen3-30B-A3B |
|---|---|---|
| GPQA Diamond | 60.5% | 70.1% |
| LMArena Expert | 1357 | 1396 |
| Confabulations | — | 12.3% |
| Vectara Hallucination Rate | 9.3% | — |

## Multilingual

- GLM-4.7-Flash: 46.5 (#158)
- Qwen3-30B-A3B: 49.5 (#132)

| Benchmark | GLM-4.7-Flash | Qwen3-30B-A3B |
|---|---|---|
| LMArena Non-English | 1330 | 1372 |
| LMArena Chinese | 1403 | 1433 |
| LMArena French | 1332 | 1418 |
| LMArena German | 1337 | 1380 |
| LMArena Korean | 1283 | 1331 |
| LMArena Russian | 1332 | 1370 |
| LMArena Spanish | 1350 | 1404 |
| LMArena Japanese | — | 1337 |

## Instruction Following

- GLM-4.7-Flash: 70.1 (#167)
- Qwen3-30B-A3B: 72.0 (#142)

| Benchmark | GLM-4.7-Flash | Qwen3-30B-A3B |
|---|---|---|
| LMArena Instruction Following | 1327 | 1363 |

## Long Context

- GLM-4.7-Flash: 40.9 (#148)
- Qwen3-30B-A3B: 31.0 (#283)

| Benchmark | GLM-4.7-Flash | Qwen3-30B-A3B |
|---|---|---|
| LMArena Longer Query | 1345 | 1379 |
| Fiction.LiveBench | — | 40.6% |

## Writing & Preference

- GLM-4.7-Flash: 47.4 (#210)
- Qwen3-30B-A3B: 55.6 (#143)

| Benchmark | GLM-4.7-Flash | Qwen3-30B-A3B |
|---|---|---|
| LMArena Text | 1351 | 1384 |
| LMArena Creative Writing | 1297 | 1317 |
| LMArena Multi-Turn | 1342 | 1378 |
| Short-Story Creative Writing | — | 75.3% |
| EQ-Bench Creative Writing | 1125 | — |

## FAQ

### Is GLM-4.7-Flash better than Qwen3-30B-A3B?

GLM-4.7-Flash and Qwen3-30B-A3B score almost the same on the Noometry Index (38.8 vs 38.9), so choose on price, context window or the category you care about most.

### Which is cheaper, GLM-4.7-Flash or Qwen3-30B-A3B?

GLM-4.7-Flash is cheaper. It lists at $0.06 per million input tokens and $0.40 per million output tokens; Qwen3-30B-A3B lists at $0.12 and $0.50.

### Is GLM-4.7-Flash or Qwen3-30B-A3B better for coding?

GLM-4.7-Flash scores higher on coding benchmarks: 40.6 versus 37.5 in the Noometry coding category.

### Which has the bigger context window?

GLM-4.7-Flash does, with 200K tokens against 41K.

### How many benchmarks do GLM-4.7-Flash and Qwen3-30B-A3B share?

19 benchmarks have published results for both models. GLM-4.7-Flash has 21 scored results on Noometry and Qwen3-30B-A3B has 32.