# DeepSeek-V3.1 vs Muse Spark 1.3

> Muse Spark 1.3 is the stronger model overall, scoring 54.4 to 42.2 on the Noometry Index. DeepSeek-V3.1 costs 4.7× less per token, which makes it the better buy when Muse Spark 1.3's lead doesn't matter for your workload.

- Canonical page: https://noometry.com/compare/deepseek-v3-1-vs-muse-spark-1-3
- Last updated: 2026-10-10
- Shared benchmarks: 19

## Summary

- They share 19 benchmarks with published results for both. DeepSeek-V3.1 scores higher in 1 category and Muse Spark 1.3 in 7 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in math, where Muse Spark 1.3 leads 72.4 to 36.1.
- The biggest single-benchmark swing is LMCA: 24.3% for DeepSeek-V3.1 and 53.9% for Muse Spark 1.3.
- DeepSeek-V3.1 is cheaper at $0.25 / $0.95 per million input/output tokens, against $1.25 / $4.25 for Muse Spark 1.3.
- Muse Spark 1.3 accepts more context: 1.05M tokens versus 164K.
- DeepSeek-V3.1 has downloadable open weights; the other is API-only.

## Snapshot

| | DeepSeek-V3.1 | Muse Spark 1.3 |
|---|---|---|
| Provider | DeepSeek | Meta |
| Noometry Index | 42.2 | 54.4 |
| Rank | 106 | 27 |
| Context | 164K | 1.05M |
| Input $/M | $0.25 | $1.25 |
| Output $/M | $0.95 | $4.25 |
| Weights | Open | Proprietary |

## Coding

- DeepSeek-V3.1: 39.8 (#140)
- Muse Spark 1.3: 56.1 (#18)

| Benchmark | DeepSeek-V3.1 | Muse Spark 1.3 |
|---|---|---|
| LMArena Coding | 1417 | 1516 |
| CursorBench | — | 41.6% |
| LMArena WebDev | — | 1655 |
| SciCode | — | 59.7% |
| WeirdML | 38.4% | — |

## Agentic & Tool Use

- DeepSeek-V3.1: —
- Muse Spark 1.3: 38.6 (#28)

| Benchmark | DeepSeek-V3.1 | Muse Spark 1.3 |
|---|---|---|
| APEX-Agents | — | 57.8% |
| GDP.pdf | — | 27.6% |

## Reasoning

- DeepSeek-V3.1: 27.3 (#109)
- Muse Spark 1.3: 52.9 (#27)

| Benchmark | DeepSeek-V3.1 | Muse Spark 1.3 |
|---|---|---|
| LMArena Hard Prompts | 1417 | 1503 |
| DTBench | 82.7% | 96.5% |
| LMCA | 24.3% | 53.9% |
| Epoch Capabilities Index | 139.92 | 156.89 |
| SimpleBench | 40% | — |
| Kagi LLM Benchmark | 53.2% | — |
| NYT Connections (extended) | — | 85.1% |
| CritPt | — | 24.9% |
| Chess Puzzles | — | 38% |
| Mystery Game Puzzles | — | 25% |
| Bench to the Future 3 | — | 0.14 |
| ForecastBench | 58 | — |

## Math

- DeepSeek-V3.1: 36.1 (#125)
- Muse Spark 1.3: 72.4 (#16)

| Benchmark | DeepSeek-V3.1 | Muse Spark 1.3 |
|---|---|---|
| LMArena Math | 1420 | 1497 |
| FrontierMath (Tiers 1-3) | — | 74.4% |
| FrontierMath Tier 4 | — | 46.3% |
| OTIS Mock AIME 2024-2025 | — | 99.2% |

## Knowledge

- DeepSeek-V3.1: 43.0 (#86)
- Muse Spark 1.3: 42.0 (#92)

| Benchmark | DeepSeek-V3.1 | Muse Spark 1.3 |
|---|---|---|
| LMArena Expert | 1403 | 1516 |
| Vectara Hallucination Rate | 5.5% | — |

## Multimodal

- DeepSeek-V3.1: —
- Muse Spark 1.3: 43.0 (#21)

| Benchmark | DeepSeek-V3.1 | Muse Spark 1.3 |
|---|---|---|
| LMArena Vision | — | 1310 |
| LMArena Document | — | 1471 |

## Multilingual

- DeepSeek-V3.1: 51.8 (#100)
- Muse Spark 1.3: 57.8 (#6)

| Benchmark | DeepSeek-V3.1 | Muse Spark 1.3 |
|---|---|---|
| LMArena Non-English | 1400 | 1485 |
| LMArena Chinese | 1470 | 1527 |
| LMArena French | 1447 | 1534 |
| LMArena Korean | 1337 | 1513 |
| LMArena Russian | 1405 | 1491 |
| LMArena Spanish | 1429 | 1485 |
| LMArena German | 1412 | — |
| LMArena Japanese | 1374 | — |

## Instruction Following

- DeepSeek-V3.1: 74.1 (#105)
- Muse Spark 1.3: 77.8 (#14)

| Benchmark | DeepSeek-V3.1 | Muse Spark 1.3 |
|---|---|---|
| LMArena Instruction Following | 1400 | 1481 |

## Long Context

- DeepSeek-V3.1: 36.3 (#227)
- Muse Spark 1.3: 45.7 (#29)

| Benchmark | DeepSeek-V3.1 | Muse Spark 1.3 |
|---|---|---|
| LMArena Longer Query | 1422 | 1491 |
| Fiction.LiveBench | 52.8% | — |

## Writing & Preference

- DeepSeek-V3.1: 60.5 (#93)
- Muse Spark 1.3: 73.7 (#10)

| Benchmark | DeepSeek-V3.1 | Muse Spark 1.3 |
|---|---|---|
| LMArena Text | 1420 | 1490 |
| LMArena Creative Writing | 1401 | 1450 |
| EQ-Bench Creative Writing | 1436 | 1906 |
| LMArena Multi-Turn | 1408 | 1486 |

## FAQ

### Is DeepSeek-V3.1 better than Muse Spark 1.3?

Muse Spark 1.3 is the stronger model overall, scoring 54.4 to 42.2 on the Noometry Index. DeepSeek-V3.1 costs 4.7× less per token, which makes it the better buy when Muse Spark 1.3's lead doesn't matter for your workload.

### Which is cheaper, DeepSeek-V3.1 or Muse Spark 1.3?

DeepSeek-V3.1 is cheaper. It lists at $0.25 per million input tokens and $0.95 per million output tokens; Muse Spark 1.3 lists at $1.25 and $4.25.

### Is DeepSeek-V3.1 or Muse Spark 1.3 better for coding?

Muse Spark 1.3 scores higher on coding benchmarks: 56.1 versus 39.8 in the Noometry coding category.

### Which has the bigger context window?

Muse Spark 1.3 does, with 1.05M tokens against 164K.

### How many benchmarks do DeepSeek-V3.1 and Muse Spark 1.3 share?

19 benchmarks have published results for both models. DeepSeek-V3.1 has 27 scored results on Noometry and Muse Spark 1.3 has 34.