# Mistral Large vs Muse Spark 1.3

> Muse Spark 1.3 is the stronger model overall, scoring 54.8 to 31.9 on the Noometry Index.

- Canonical page: https://noometry.com/compare/mistral-large-vs-muse-spark-1-3
- Last updated: 2026-10-11
- Shared benchmarks: 24

## Summary

- They share 24 benchmarks with published results for both. Mistral Large scores higher in 0 categories and Muse Spark 1.3 in 9 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in math, where Muse Spark 1.3 leads 73.1 to 18.2.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 8.5% for Mistral Large and 99.2% for Muse Spark 1.3.
- Muse Spark 1.3 is cheaper at $1.25 / $4.25 per million input/output tokens, against $2 / $6 for Mistral Large.
- Muse Spark 1.3 accepts more context: 1.05M tokens versus 131K.
- Mistral Large has downloadable open weights; the other is API-only.

## Snapshot

| | Mistral Large | Muse Spark 1.3 |
|---|---|---|
| Provider | Mistral AI | Meta |
| Noometry Index | 31.9 | 54.8 |
| Rank | 263 | 27 |
| Context | 131K | 1.05M |
| Input $/M | $2 | $1.25 |
| Output $/M | $6 | $4.25 |
| Weights | Open | Proprietary |

## Coding

- Mistral Large: 34.3 (#240)
- Muse Spark 1.3: 56.6 (#21)

| Benchmark | Mistral Large | Muse Spark 1.3 |
|---|---|---|
| SciCode | 36.2% | 59.7% |
| LMArena Coding | 1277 | 1514 |
| CursorBench | — | 41.6% |
| LMArena WebDev | — | 1657 |
| BigCodeBench Instruct | 30% | — |
| LiveBench Coding | 47.1% | — |
| BigCodeBench Complete | 38.3% | — |
| ALE-Bench | 264.7 | — |
| HumanEval+ | 62.2% | — |
| MBPP+ | 59.5% | — |

## Agentic & Tool Use

- Mistral Large: 28.6 (#89)
- Muse Spark 1.3: 38.6 (#30)

| Benchmark | Mistral Large | Muse Spark 1.3 |
|---|---|---|
| APEX-Agents | — | 57.8% |
| Berkeley Function Calling Leaderboard | 38.4% | — |
| GDP.pdf | — | 27.6% |

## Reasoning

- Mistral Large: 15.8 (#310)
- Muse Spark 1.3: 54.0 (#27)

| Benchmark | Mistral Large | Muse Spark 1.3 |
|---|---|---|
| CritPt | 0% | 26% |
| LMArena Hard Prompts | 1257 | 1503 |
| DTBench | 65.1% | 96.5% |
| LMCA | 16.7% | 53.9% |
| Epoch Capabilities Index | 128.52 | 156.75 |
| SimpleBench | 22.5% | — |
| NYT Connections (extended) | — | 85.1% |
| Chess Puzzles | — | 38% |
| LiveBench Reasoning | 43.5% | — |
| Mystery Game Puzzles | — | 25% |
| LiveBench Data Analysis | 50.1% | — |
| Bench to the Future 3 | — | 0.14 |
| ForecastBench | 57.1 | — |
| LiveBench | 48.4% | — |

## Math

- Mistral Large: 18.2 (#291)
- Muse Spark 1.3: 73.1 (#21)

| Benchmark | Mistral Large | Muse Spark 1.3 |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 8.5% | 99.2% |
| LMArena Math | 1262 | 1494 |
| FrontierMath (Tiers 1-3) | — | 74.4% |
| FrontierMath Tier 4 | — | 46.3% |
| ProofBench | — | 58% |
| Omni-MATH | 28.1% | — |
| LiveBench Math | 42.5% | — |
| MATH Level 5 | 50.3% | — |
| FrontierMath (Feb 2025 set) | 0.3% | — |

## Knowledge

- Mistral Large: 30.1 (#230)
- Muse Spark 1.3: 42.6 (#95)

| Benchmark | Mistral Large | Muse Spark 1.3 |
|---|---|---|
| LMArena Expert | 1232 | 1516 |
| GPQA Diamond | 51.3% | — |
| MMLU-Pro | 59.9% | — |
| Confabulations | 21.4% | — |
| Vectara Hallucination Rate | 4.5% | — |
| GPQA (HELM) | 43.5% | — |
| MMLU | 80% | — |

## Multimodal

- Mistral Large: —
- Muse Spark 1.3: 43.7 (#22)

| Benchmark | Mistral Large | Muse Spark 1.3 |
|---|---|---|
| LMArena Vision | — | 1309 |
| LMArena Document | — | 1471 |

## Multilingual

- Mistral Large: 40.0 (#219)
- Muse Spark 1.3: 57.4 (#8)

| Benchmark | Mistral Large | Muse Spark 1.3 |
|---|---|---|
| LMArena Non-English | 1237 | 1481 |
| LMArena Chinese | 1240 | 1529 |
| LMArena French | 1325 | 1524 |
| LMArena German | 1254 | 1515 |
| LMArena Japanese | 1188 | 1474 |
| LMArena Korean | 1202 | 1501 |
| LMArena Russian | 1257 | 1490 |
| LMArena Spanish | 1268 | 1490 |

## Instruction Following

- Mistral Large: 67.9 (#191)
- Muse Spark 1.3: 77.5 (#22)

| Benchmark | Mistral Large | Muse Spark 1.3 |
|---|---|---|
| LMArena Instruction Following | 1249 | 1477 |
| LiveBench Instruction Following | 67.9% | — |
| IFEval | 87.7% | — |

## Long Context

- Mistral Large: 38.3 (#199)
- Muse Spark 1.3: 45.6 (#32)

| Benchmark | Mistral Large | Muse Spark 1.3 |
|---|---|---|
| LMArena Longer Query | 1261 | 1488 |

## Writing & Preference

- Mistral Large: 40.7 (#242)
- Muse Spark 1.3: 73.6 (#9)

| Benchmark | Mistral Large | Muse Spark 1.3 |
|---|---|---|
| LMArena Text | 1266 | 1490 |
| LMArena Creative Writing | 1243 | 1455 |
| EQ-Bench Creative Writing | 985 | 1906 |
| LMArena Multi-Turn | 1260 | 1482 |
| Short-Story Creative Writing | 69% | — |
| WildBench | 80.1% | — |
| LiveBench Language | 39.4% | — |

## FAQ

### Is Mistral Large better than Muse Spark 1.3?

Muse Spark 1.3 is the stronger model overall, scoring 54.8 to 31.9 on the Noometry Index.

### Which is cheaper, Mistral Large or Muse Spark 1.3?

Muse Spark 1.3 is cheaper. It lists at $1.25 per million input tokens and $4.25 per million output tokens; Mistral Large lists at $2 and $6.

### Is Mistral Large or Muse Spark 1.3 better for coding?

Muse Spark 1.3 scores higher on coding benchmarks: 56.6 versus 34.3 in the Noometry coding category.

### Which has the bigger context window?

Muse Spark 1.3 does, with 1.05M tokens against 131K.

### How many benchmarks do Mistral Large and Muse Spark 1.3 share?

24 benchmarks have published results for both models. Mistral Large has 51 scored results on Noometry and Muse Spark 1.3 has 37.