# GLM-4.6 vs Mistral Small

> GLM-4.6 is the stronger model overall, scoring 41.4 to 33.4 on the Noometry Index. Mistral Small costs 3.8× less per token, which makes it the better buy when GLM-4.6's lead doesn't matter for your workload.

- Canonical page: https://noometry.com/compare/glm-4-6-vs-mistral-small
- Last updated: 2026-10-11
- Shared benchmarks: 23

## Summary

- They share 23 benchmarks with published results for both. GLM-4.6 scores higher in 9 categories and Mistral Small in 0 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in math, where GLM-4.6 leads 39.1 to 16.4.
- The biggest single-benchmark swing is Berkeley Function Calling Leaderboard: 72.4% for GLM-4.6 and 37.1% for Mistral Small.
- Mistral Small is cheaper at $0.15 / $0.60 per million input/output tokens, against $0.60 / $2.20 for GLM-4.6.
- Mistral Small accepts more context: 262K tokens versus 205K.

## Snapshot

| | GLM-4.6 | Mistral Small |
|---|---|---|
| Provider | Z.ai (Zhipu) | Mistral AI |
| Noometry Index | 41.4 | 33.4 |
| Rank | 135 | 243 |
| Context | 205K | 262K |
| Input $/M | $0.60 | $0.15 |
| Output $/M | $2.20 | $0.60 |
| Weights | Open | Open |

## Coding

- GLM-4.6: 40.1 (#148)
- Mistral Small: 34.0 (#247)

| Benchmark | GLM-4.6 | Mistral Small |
|---|---|---|
| SciCode | 38.4% | 26.5% |
| LMArena Coding | 1449 | 1362 |
| ALE-Bench | 340.82 | 497.62 |
| SWE-bench Verified (bash only) | 55.4% | — |
| LMArena WebDev | 1340 | — |
| BigCodeBench Instruct | — | 36.1% |
| LiveBench Coding | — | 36.2% |
| BigCodeBench Complete | — | 46.6% |

## Agentic & Tool Use

- GLM-4.6: 32.3 (#66)
- Mistral Small: 28.1 (#93)

| Benchmark | GLM-4.6 | Mistral Small |
|---|---|---|
| Berkeley Function Calling Leaderboard | 72.4% | 37.1% |
| Terminal-Bench | 24.5% | — |

## Reasoning

- GLM-4.6: 23.7 (#172)
- Mistral Small: 19.8 (#250)

| Benchmark | GLM-4.6 | Mistral Small |
|---|---|---|
| Kagi LLM Benchmark | 47.4% | 37.8% |
| CritPt | 1.1% | 0% |
| LMArena Hard Prompts | 1440 | 1335 |
| LiveBench Reasoning | — | 44.8% |
| DTBench | — | 70.9% |
| LiveBench Data Analysis | — | 53.7% |
| LMCA | — | 20.6% |
| LiveBench | — | 44% |

## Math

- GLM-4.6: 39.1 (#111)
- Mistral Small: 16.4 (#293)

| Benchmark | GLM-4.6 | Mistral Small |
|---|---|---|
| LMArena Math | 1432 | 1341 |
| OTIS Mock AIME 2024-2025 | — | 5.8% |
| LiveBench Math | — | 39.9% |
| MATH Level 5 | — | 46.8% |
| FrontierMath (Feb 2025 set) | 3.8% | — |
| FrontierMath Tier 4 (v1) | 2.1% | — |

## Knowledge

- GLM-4.6: 40.2 (#124)
- Mistral Small: 31.0 (#222)

| Benchmark | GLM-4.6 | Mistral Small |
|---|---|---|
| Vectara Hallucination Rate | 9.5% | 5.1% |
| LMArena Expert | 1431 | 1291 |
| GPQA Diamond | — | 47.5% |
| MMLU | — | 68.7% |

## Multimodal

- GLM-4.6: —
- Mistral Small: 33.5 (#96)

| Benchmark | GLM-4.6 | Mistral Small |
|---|---|---|
| LMArena Vision | — | 1142 |

## Multilingual

- GLM-4.6: 53.5 (#66)
- Mistral Small: 45.5 (#169)

| Benchmark | GLM-4.6 | Mistral Small |
|---|---|---|
| LMArena Non-English | 1426 | 1315 |
| LMArena Chinese | 1499 | 1340 |
| LMArena French | 1459 | 1337 |
| LMArena German | 1447 | 1340 |
| LMArena Japanese | 1393 | 1275 |
| LMArena Korean | 1400 | 1259 |
| LMArena Russian | 1419 | 1324 |
| LMArena Spanish | 1436 | 1346 |

## Instruction Following

- GLM-4.6: 74.3 (#98)
- Mistral Small: 66.4 (#209)

| Benchmark | GLM-4.6 | Mistral Small |
|---|---|---|
| LMArena Instruction Following | 1410 | 1310 |
| LiveBench Instruction Following | — | 63.7% |

## Long Context

- GLM-4.6: 43.4 (#94)
- Mistral Small: 40.4 (#156)

| Benchmark | GLM-4.6 | Mistral Small |
|---|---|---|
| LMArena Longer Query | 1422 | 1327 |

## Writing & Preference

- GLM-4.6: 61.1 (#90)
- Mistral Small: 52.5 (#171)

| Benchmark | GLM-4.6 | Mistral Small |
|---|---|---|
| LMArena Text | 1440 | 1338 |
| LMArena Creative Writing | 1411 | 1305 |
| LMArena Multi-Turn | 1427 | 1344 |
| EQ-Bench Creative Writing | 1411 | — |
| LiveBench Language | — | 30.5% |

## FAQ

### Is GLM-4.6 better than Mistral Small?

GLM-4.6 is the stronger model overall, scoring 41.4 to 33.4 on the Noometry Index. Mistral Small costs 3.8× less per token, which makes it the better buy when GLM-4.6's lead doesn't matter for your workload.

### Which is cheaper, GLM-4.6 or Mistral Small?

Mistral Small is cheaper. It lists at $0.15 per million input tokens and $0.60 per million output tokens; GLM-4.6 lists at $0.60 and $2.20.

### Is GLM-4.6 or Mistral Small better for coding?

GLM-4.6 scores higher on coding benchmarks: 40.1 versus 34.0 in the Noometry coding category.

### Which has the bigger context window?

Mistral Small does, with 262K tokens against 205K.

### How many benchmarks do GLM-4.6 and Mistral Small share?

23 benchmarks have published results for both models. GLM-4.6 has 29 scored results on Noometry and Mistral Small has 39.