# GLM-4.6 vs Muse Spark 1.1

> Muse Spark 1.1 is the stronger model overall, scoring 49.9 to 41.4 on the Noometry Index. GLM-4.6 costs 2.0× less per token, which makes it the better buy when Muse Spark 1.1's lead doesn't matter for your workload.

- Canonical page: https://noometry.com/compare/glm-4-6-vs-muse-spark-1-1
- Last updated: 2026-10-12
- Shared benchmarks: 21

## Summary

- They share 21 benchmarks with published results for both. GLM-4.6 scores higher in 1 category and Muse Spark 1.1 in 8 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where Muse Spark 1.1 leads 47.1 to 23.7.
- The biggest single-benchmark swing is SciCode: 38.4% for GLM-4.6 and 58.8% for Muse Spark 1.1.
- GLM-4.6 is cheaper at $0.60 / $2.20 per million input/output tokens, against $1.25 / $4.25 for Muse Spark 1.1.
- Muse Spark 1.1 accepts more context: 1.05M tokens versus 205K.
- GLM-4.6 has downloadable open weights; the other is API-only.

## Snapshot

| | GLM-4.6 | Muse Spark 1.1 |
|---|---|---|
| Provider | Z.ai (Zhipu) | Meta |
| Noometry Index | 41.4 | 49.9 |
| Rank | 135 | 51 |
| Context | 205K | 1.05M |
| Input $/M | $0.60 | $1.25 |
| Output $/M | $2.20 | $4.25 |
| Weights | Open | Proprietary |

## Coding

- GLM-4.6: 40.1 (#148)
- Muse Spark 1.1: 51.3 (#40)

| Benchmark | GLM-4.6 | Muse Spark 1.1 |
|---|---|---|
| LMArena WebDev | 1340 | 1542 |
| SciCode | 38.4% | 58.8% |
| LMArena Coding | 1449 | 1498 |
| DeepSWE | — | 53.3% |
| SWE-bench Verified (bash only) | 55.4% | — |
| ALE-Bench | 340.82 | — |

## Agentic & Tool Use

- GLM-4.6: 32.3 (#66)
- Muse Spark 1.1: 30.8 (#73)

| Benchmark | GLM-4.6 | Muse Spark 1.1 |
|---|---|---|
| Terminal-Bench | 24.5% | — |
| APEX-Agents | — | 31.8% |
| Berkeley Function Calling Leaderboard | 72.4% | — |
| τ²-bench Banking | — | 40.5% |
| GBAEval | — | 7.9% |
| GDP.pdf | — | 15% |
| Vending-Bench 2 | — | 6,520 |

## Reasoning

- GLM-4.6: 23.7 (#172)
- Muse Spark 1.1: 47.1 (#44)

| Benchmark | GLM-4.6 | Muse Spark 1.1 |
|---|---|---|
| CritPt | 1.1% | 15.1% |
| LMArena Hard Prompts | 1440 | 1486 |
| Kagi LLM Benchmark | 47.4% | — |
| NYT Connections (extended) | — | 84.9% |
| DTBench | — | 94.4% |
| LMCA | — | 49.9% |
| Surface Evolver Bench | — | 52.5% |
| Epoch Capabilities Index | — | 154.21 |

## Math

- GLM-4.6: 39.1 (#111)
- Muse Spark 1.1: 45.5 (#76)

| Benchmark | GLM-4.6 | Muse Spark 1.1 |
|---|---|---|
| LMArena Math | 1432 | 1483 |
| ProofBench | — | 39% |
| FrontierMath (Feb 2025 set) | 3.8% | — |
| FrontierMath Tier 4 (v1) | 2.1% | — |

## Knowledge

- GLM-4.6: 40.2 (#124)
- Muse Spark 1.1: 53.1 (#59)

| Benchmark | GLM-4.6 | Muse Spark 1.1 |
|---|---|---|
| LMArena Expert | 1431 | 1478 |
| SimpleQA Verified | — | 57.8% |
| Vectara Hallucination Rate | 9.5% | — |

## Multimodal

- GLM-4.6: —
- Muse Spark 1.1: 42.6 (#29)

| Benchmark | GLM-4.6 | Muse Spark 1.1 |
|---|---|---|
| LMArena Vision | — | 1293 |
| LMArena Document | — | 1465 |

## Multilingual

- GLM-4.6: 53.5 (#66)
- Muse Spark 1.1: 56.7 (#17)

| Benchmark | GLM-4.6 | Muse Spark 1.1 |
|---|---|---|
| LMArena Non-English | 1426 | 1472 |
| LMArena Chinese | 1499 | 1518 |
| LMArena French | 1459 | 1494 |
| LMArena German | 1447 | 1466 |
| LMArena Japanese | 1393 | 1451 |
| LMArena Korean | 1400 | 1458 |
| LMArena Russian | 1419 | 1483 |
| LMArena Spanish | 1436 | 1464 |

## Instruction Following

- GLM-4.6: 74.3 (#98)
- Muse Spark 1.1: 76.5 (#39)

| Benchmark | GLM-4.6 | Muse Spark 1.1 |
|---|---|---|
| LMArena Instruction Following | 1410 | 1457 |

## Long Context

- GLM-4.6: 43.4 (#94)
- Muse Spark 1.1: 44.8 (#58)

| Benchmark | GLM-4.6 | Muse Spark 1.1 |
|---|---|---|
| LMArena Longer Query | 1422 | 1462 |

## Writing & Preference

- GLM-4.6: 61.1 (#90)
- Muse Spark 1.1: 73.4 (#11)

| Benchmark | GLM-4.6 | Muse Spark 1.1 |
|---|---|---|
| LMArena Text | 1440 | 1479 |
| LMArena Creative Writing | 1411 | 1437 |
| EQ-Bench Creative Writing | 1411 | 1927 |
| LMArena Multi-Turn | 1427 | 1485 |
| EQ-Bench 4 | — | 1260 |

## FAQ

### Is GLM-4.6 better than Muse Spark 1.1?

Muse Spark 1.1 is the stronger model overall, scoring 49.9 to 41.4 on the Noometry Index. GLM-4.6 costs 2.0× less per token, which makes it the better buy when Muse Spark 1.1's lead doesn't matter for your workload.

### Which is cheaper, GLM-4.6 or Muse Spark 1.1?

GLM-4.6 is cheaper. It lists at $0.60 per million input tokens and $2.20 per million output tokens; Muse Spark 1.1 lists at $1.25 and $4.25.

### Is GLM-4.6 or Muse Spark 1.1 better for coding?

Muse Spark 1.1 scores higher on coding benchmarks: 51.3 versus 40.1 in the Noometry coding category.

### Which has the bigger context window?

Muse Spark 1.1 does, with 1.05M tokens against 205K.

### How many benchmarks do GLM-4.6 and Muse Spark 1.1 share?

21 benchmarks have published results for both models. GLM-4.6 has 29 scored results on Noometry and Muse Spark 1.1 has 37.