# GLM-4.6 vs Mixtral 8x7B

> GLM-4.6 is the stronger model overall, scoring 40.9 to 26.8 on the Noometry Index.

- Canonical page: https://noometry.com/compare/glm-4-6-vs-mixtral-8x7b
- Last updated: 2026-10-10
- Shared benchmarks: 17

## Summary

- They share 17 benchmarks with published results for both. GLM-4.6 scores higher in 8 categories and Mixtral 8x7B in 0 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where GLM-4.6 leads 39.6 to 11.0.
- Mixtral 8x7B is cheaper at $0.70 / $0.70 per million input/output tokens, against $0.60 / $2.20 for GLM-4.6.
- GLM-4.6 accepts more context: 205K tokens versus 32K.

## Snapshot

| | GLM-4.6 | Mixtral 8x7B |
|---|---|---|
| Provider | Z.ai (Zhipu) | Mistral AI |
| Noometry Index | 40.9 | 26.8 |
| Rank | 132 | 328 |
| Context | 205K | 32K |
| Input $/M | $0.60 | $0.70 |
| Output $/M | $2.20 | $0.70 |
| Weights | Open | Open |

## Coding

- GLM-4.6: 39.9 (#137)
- Mixtral 8x7B: 32.2 (#265)

| Benchmark | GLM-4.6 | Mixtral 8x7B |
|---|---|---|
| LMArena Coding | 1449 | 1127 |
| SWE-bench Verified (bash only) | 55.4% | — |
| LMArena WebDev | 1339 | — |
| SciCode | 38.4% | — |
| ALE-Bench | 340.82 | — |
| HumanEval+ | — | 39.6% |
| MBPP+ | — | 49.7% |

## Agentic & Tool Use

- GLM-4.6: 32.1 (#60)
- Mixtral 8x7B: —

| Benchmark | GLM-4.6 | Mixtral 8x7B |
|---|---|---|
| Terminal-Bench | 24.5% | — |
| Berkeley Function Calling Leaderboard | 72.4% | — |

## Reasoning

- GLM-4.6: 23.1 (#168)
- Mixtral 8x7B: 17.4 (#283)

| Benchmark | GLM-4.6 | Mixtral 8x7B |
|---|---|---|
| LMArena Hard Prompts | 1440 | 1115 |
| Kagi LLM Benchmark | 47.4% | — |
| CritPt | 1.1% | — |
| DTBench | — | 49.6% |
| Adversarial NLI | — | 55.2% |
| Epoch Capabilities Index | — | 118.38 |
| ForecastBench | — | 56.3 |
| HellaSwag | — | 86.7% |
| PIQA | — | 83.6% |
| WinoGrande | — | 77.2% |

## Math

- GLM-4.6: 36.4 (#115)
- Mixtral 8x7B: 17.2 (#284)

| Benchmark | GLM-4.6 | Mixtral 8x7B |
|---|---|---|
| LMArena Math | 1431 | 1147 |
| Omni-MATH | — | 10.5% |
| MATH Level 5 | — | 10% |
| FrontierMath (Feb 2025 set) | 3.8% | — |
| FrontierMath Tier 4 (v1) | 2.1% | — |
| GSM8K | — | 74.4% |

## Knowledge

- GLM-4.6: 39.6 (#118)
- Mixtral 8x7B: 11.0 (#296)

| Benchmark | GLM-4.6 | Mixtral 8x7B |
|---|---|---|
| LMArena Expert | 1432 | 1088 |
| GPQA Diamond | — | 30.6% |
| MMLU-Pro | — | 33.5% |
| Vectara Hallucination Rate | 9.5% | — |
| GPQA (HELM) | — | 29.6% |
| ARC (AI2) Challenge | — | 87.3% |
| MMLU | — | 70.6% |
| OpenBookQA | — | 85.8% |
| TriviaQA | — | 82.2% |

## Multilingual

- GLM-4.6: 53.6 (#62)
- Mixtral 8x7B: 29.8 (#261)

| Benchmark | GLM-4.6 | Mixtral 8x7B |
|---|---|---|
| LMArena Non-English | 1426 | 1077 |
| LMArena Chinese | 1500 | 1055 |
| LMArena French | 1459 | 1165 |
| LMArena German | 1447 | 1114 |
| LMArena Japanese | 1394 | 931 |
| LMArena Korean | 1401 | 968 |
| LMArena Russian | 1418 | 1090 |
| LMArena Spanish | 1436 | 1110 |

## Instruction Following

- GLM-4.6: 74.5 (#93)
- Mixtral 8x7B: 51.2 (#292)

| Benchmark | GLM-4.6 | Mixtral 8x7B |
|---|---|---|
| LMArena Instruction Following | 1410 | 1110 |
| IFEval | — | 57.5% |

## Long Context

- GLM-4.6: 43.4 (#91)
- Mixtral 8x7B: 33.4 (#255)

| Benchmark | GLM-4.6 | Mixtral 8x7B |
|---|---|---|
| LMArena Longer Query | 1423 | 1103 |

## Writing & Preference

- GLM-4.6: 61.3 (#86)
- Mixtral 8x7B: 34.4 (#265)

| Benchmark | GLM-4.6 | Mixtral 8x7B |
|---|---|---|
| LMArena Text | 1440 | 1132 |
| LMArena Creative Writing | 1411 | 1109 |
| LMArena Multi-Turn | 1427 | 1115 |
| EQ-Bench Creative Writing | 1411 | — |
| WildBench | — | 67.3% |

## FAQ

### Is GLM-4.6 better than Mixtral 8x7B?

GLM-4.6 is the stronger model overall, scoring 40.9 to 26.8 on the Noometry Index.

### Which is cheaper, GLM-4.6 or Mixtral 8x7B?

Mixtral 8x7B is cheaper. It lists at $0.70 per million input tokens and $0.70 per million output tokens; GLM-4.6 lists at $0.60 and $2.20.

### Is GLM-4.6 or Mixtral 8x7B better for coding?

GLM-4.6 scores higher on coding benchmarks: 39.9 versus 32.2 in the Noometry coding category.

### Which has the bigger context window?

GLM-4.6 does, with 205K tokens against 32K.

### How many benchmarks do GLM-4.6 and Mixtral 8x7B share?

17 benchmarks have published results for both models. GLM-4.6 has 29 scored results on Noometry and Mixtral 8x7B has 38.