# GLM-5.3 vs Mistral Medium

> GLM-5.3 is the stronger model overall, scoring 54.8 to 36.3 on the Noometry Index.

- Canonical page: https://noometry.com/compare/glm-5-3-vs-mistral-medium
- Last updated: 2026-10-10
- Shared benchmarks: 27

## Summary

- They share 27 benchmarks with published results for both. GLM-5.3 scores higher in 9 categories and Mistral Medium in 0 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in math, where GLM-5.3 leads 62.3 to 28.1.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 91.1% for GLM-5.3 and 32.2% for Mistral Medium.
- GLM-5.3 is cheaper at $1.40 / $4.40 per million input/output tokens, against $1.50 / $7.50 for Mistral Medium.
- GLM-5.3 accepts more context: 1M tokens versus 262K.

## Snapshot

| | GLM-5.3 | Mistral Medium |
|---|---|---|
| Provider | Z.ai (Zhipu) | Mistral AI |
| Noometry Index | 54.8 | 36.3 |
| Rank | 26 | 218 |
| Context | 1M | 262K |
| Input $/M | $1.40 | $1.50 |
| Output $/M | $4.40 | $7.50 |
| Weights | Open | Open |

## Coding

- GLM-5.3: 59.5 (#14)
- Mistral Medium: 34.2 (#243)

| Benchmark | GLM-5.3 | Mistral Medium |
|---|---|---|
| FrontierCode | 40.1% | 8% |
| SciCode | 59% | 40.2% |
| WeirdML | 75.4% | 43.7% |
| LMArena Coding | 1496 | 1434 |
| ALE-Bench | 1,317 | 763.98 |
| DeepSWE | 69% | — |
| CursorBench | 42.6% | — |
| LMArena WebDev | 1622 | — |
| FrontierSWE | 30.2% | — |

## Agentic & Tool Use

- GLM-5.3: 36.4 (#38)
- Mistral Medium: 28.3 (#90)

| Benchmark | GLM-5.3 | Mistral Medium |
|---|---|---|
| APEX-Agents | 56.6% | — |
| Berkeley Function Calling Leaderboard | — | 37.7% |
| Vending-Bench 2 | 8,164 | — |

## Reasoning

- GLM-5.3: 46.1 (#46)
- Mistral Medium: 24.0 (#167)

| Benchmark | GLM-5.3 | Mistral Medium |
|---|---|---|
| CritPt | 19.1% | 0% |
| LMArena Hard Prompts | 1489 | 1426 |
| DTBench | 87.7% | 75.5% |
| LMCA | 55.5% | 26.1% |
| Kagi LLM Benchmark | — | 50% |
| NYT Connections (extended) | 74.2% | — |
| Chess Puzzles | 21% | — |
| Mystery Game Puzzles | 33% | — |
| Surface Evolver Bench | — | 26.9% |
| Bench to the Future 3 | 0.15 | — |
| Epoch Capabilities Index | 155.61 | — |

## Math

- GLM-5.3: 62.3 (#33)
- Mistral Medium: 28.1 (#245)

| Benchmark | GLM-5.3 | Mistral Medium |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 91.1% | 32.2% |
| ProofBench | 49% | 9% |
| LMArena Math | 1489 | 1408 |
| FrontierMath (Tiers 1-3) | 68.8% | — |
| FrontierMath Tier 4 | 29.3% | — |
| MATH Level 5 | — | 81.6% |
| FrontierMath (Feb 2025 set) | — | 0.3% |

## Knowledge

- GLM-5.3: 58.3 (#37)
- Mistral Medium: 25.0 (#265)

| Benchmark | GLM-5.3 | Mistral Medium |
|---|---|---|
| GPQA Diamond | 90.9% | 59.5% |
| LMArena Expert | 1516 | 1408 |
| Humanity's Last Exam | — | 4.5% |
| SimpleQA Verified | 41% | — |
| Vectara Hallucination Rate | — | 22.7% |

## Multimodal

- GLM-5.3: —
- Mistral Medium: 35.3 (#88)

| Benchmark | GLM-5.3 | Mistral Medium |
|---|---|---|
| LMArena Vision | — | 1172 |

## Multilingual

- GLM-5.3: 55.7 (#28)
- Mistral Medium: 52.1 (#91)

| Benchmark | GLM-5.3 | Mistral Medium |
|---|---|---|
| LMArena Non-English | 1457 | 1408 |
| LMArena Chinese | 1528 | 1447 |
| LMArena French | 1499 | 1459 |
| LMArena German | 1499 | 1432 |
| LMArena Japanese | 1453 | 1378 |
| LMArena Korean | 1472 | 1380 |
| LMArena Russian | 1463 | 1411 |
| LMArena Spanish | 1460 | 1433 |

## Instruction Following

- GLM-5.3: 77.5 (#23)
- Mistral Medium: 73.7 (#116)

| Benchmark | GLM-5.3 | Mistral Medium |
|---|---|---|
| LMArena Instruction Following | 1477 | 1398 |

## Long Context

- GLM-5.3: 45.4 (#41)
- Mistral Medium: 42.9 (#114)

| Benchmark | GLM-5.3 | Mistral Medium |
|---|---|---|
| LMArena Longer Query | 1482 | 1406 |

## Writing & Preference

- GLM-5.3: 75.7 (#6)
- Mistral Medium: 60.0 (#103)

| Benchmark | GLM-5.3 | Mistral Medium |
|---|---|---|
| LMArena Text | 1471 | 1424 |
| LMArena Creative Writing | 1457 | 1391 |
| LMArena Multi-Turn | 1472 | 1418 |
| Short-Story Creative Writing | — | 77.3% |
| EQ-Bench Creative Writing | 2075 | — |

## FAQ

### Is GLM-5.3 better than Mistral Medium?

GLM-5.3 is the stronger model overall, scoring 54.8 to 36.3 on the Noometry Index.

### Which is cheaper, GLM-5.3 or Mistral Medium?

GLM-5.3 is cheaper. It lists at $1.40 per million input tokens and $4.40 per million output tokens; Mistral Medium lists at $1.50 and $7.50.

### Is GLM-5.3 or Mistral Medium better for coding?

GLM-5.3 scores higher on coding benchmarks: 59.5 versus 34.2 in the Noometry coding category.

### Which has the bigger context window?

GLM-5.3 does, with 1M tokens against 262K.

### How many benchmarks do GLM-5.3 and Mistral Medium share?

27 benchmarks have published results for both models. GLM-5.3 has 42 scored results on Noometry and Mistral Medium has 36.