# DeepSeek-R1 vs Muse Spark 1.3

> Muse Spark 1.3 is the stronger model overall, scoring 54.8 to 42.3 on the Noometry Index. DeepSeek-R1 costs 2.2× 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-r1-vs-muse-spark-1-3
- Last updated: 2026-10-10
- Shared benchmarks: 22

## Summary

- They share 22 benchmarks with published results for both. DeepSeek-R1 scores higher in 1 category and Muse Spark 1.3 in 8 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where Muse Spark 1.3 leads 54.0 to 18.6.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 66.4% for DeepSeek-R1 and 99.2% for Muse Spark 1.3.
- DeepSeek-R1 is cheaper at $0.50 / $2.15 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.

## Snapshot

| | DeepSeek-R1 | Muse Spark 1.3 |
|---|---|---|
| Provider | DeepSeek | Meta |
| Noometry Index | 42.3 | 54.8 |
| Rank | 115 | 27 |
| Context | 164K | 1.05M |
| Input $/M | $0.50 | $1.25 |
| Output $/M | $2.15 | $4.25 |
| Weights | Proprietary | Proprietary |

## Coding

- DeepSeek-R1: 46.3 (#68)
- Muse Spark 1.3: 56.6 (#21)

| Benchmark | DeepSeek-R1 | Muse Spark 1.3 |
|---|---|---|
| SciCode | 35.7% | 59.7% |
| LMArena Coding | 1427 | 1514 |
| Aider Polyglot | 71.4% | — |
| CursorBench | — | 41.6% |
| LMArena WebDev | — | 1657 |
| WeirdML | 41.6% | — |
| LiveBench Coding | 66.7% | — |
| ALE-Bench | 804.12 | — |
| AlgoTune | 1.7 | — |

## Agentic & Tool Use

- DeepSeek-R1: 30.7 (#75)
- Muse Spark 1.3: 38.6 (#30)

| Benchmark | DeepSeek-R1 | Muse Spark 1.3 |
|---|---|---|
| APEX-Agents | — | 57.8% |
| DeepResearch Bench | 35.1% | — |
| BALROG | 34.9% | — |
| GDP.pdf | — | 27.6% |
| METR Time Horizons | 53.8% | — |

## Reasoning

- DeepSeek-R1: 18.6 (#278)
- Muse Spark 1.3: 54.0 (#27)

| Benchmark | DeepSeek-R1 | Muse Spark 1.3 |
|---|---|---|
| CritPt | 1.1% | 26% |
| LMArena Hard Prompts | 1416 | 1503 |
| Epoch Capabilities Index | 141.29 | 156.75 |
| ARC-AGI-2 | 1.3% | — |
| SimpleBench | 40.8% | — |
| Kagi LLM Benchmark | 69.4% | — |
| NYT Connections (extended) | — | 85.1% |
| ARC-AGI-1 | 21.2% | — |
| Chess Puzzles | — | 38% |
| LiveBench Reasoning | 83.2% | — |
| Mystery Game Puzzles | — | 25% |
| DTBench | — | 96.5% |
| LiveBench Data Analysis | 69.8% | — |
| LMCA | — | 53.9% |
| Bench to the Future 3 | — | 0.14 |
| ForecastBench | 60 | — |
| LiveBench | 71.6% | — |

## Math

- DeepSeek-R1: 43.8 (#79)
- Muse Spark 1.3: 73.1 (#21)

| Benchmark | DeepSeek-R1 | Muse Spark 1.3 |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 66.4% | 99.2% |
| LMArena Math | 1400 | 1494 |
| FrontierMath (Tiers 1-3) | — | 74.4% |
| FrontierMath Tier 4 | — | 46.3% |
| ProofBench | — | 58% |
| Omni-MATH | 42.4% | — |
| LiveBench Math | 80.7% | — |
| MATH Level 5 | 96.6% | — |

## Knowledge

- DeepSeek-R1: 44.5 (#87)
- Muse Spark 1.3: 42.6 (#95)

| Benchmark | DeepSeek-R1 | Muse Spark 1.3 |
|---|---|---|
| LMArena Expert | 1394 | 1516 |
| GPQA Diamond | 76.3% | — |
| MMLU-Pro | 79.3% | — |
| Confabulations | 12.7% | — |
| Vectara Hallucination Rate | 11.3% | — |
| GPQA (HELM) | 66.6% | — |

## Multimodal

- DeepSeek-R1: —
- Muse Spark 1.3: 43.7 (#22)

| Benchmark | DeepSeek-R1 | Muse Spark 1.3 |
|---|---|---|
| LMArena Vision | — | 1309 |
| LMArena Document | — | 1471 |

## Multilingual

- DeepSeek-R1: 52.4 (#85)
- Muse Spark 1.3: 57.4 (#8)

| Benchmark | DeepSeek-R1 | Muse Spark 1.3 |
|---|---|---|
| LMArena Non-English | 1412 | 1481 |
| LMArena Chinese | 1442 | 1529 |
| LMArena French | 1417 | 1524 |
| LMArena German | 1404 | 1515 |
| LMArena Japanese | 1391 | 1474 |
| LMArena Korean | 1360 | 1501 |
| LMArena Russian | 1423 | 1490 |
| LMArena Spanish | 1411 | 1490 |

## Instruction Following

- DeepSeek-R1: 72.0 (#143)
- Muse Spark 1.3: 77.5 (#22)

| Benchmark | DeepSeek-R1 | Muse Spark 1.3 |
|---|---|---|
| LMArena Instruction Following | 1382 | 1477 |
| LiveBench Instruction Following | 80.5% | — |
| IFEval | 78.4% | — |

## Long Context

- DeepSeek-R1: 45.4 (#36)
- Muse Spark 1.3: 45.6 (#32)

| Benchmark | DeepSeek-R1 | Muse Spark 1.3 |
|---|---|---|
| LMArena Longer Query | 1391 | 1488 |
| Fiction.LiveBench | 75% | — |

## Writing & Preference

- DeepSeek-R1: 61.4 (#88)
- Muse Spark 1.3: 73.6 (#9)

| Benchmark | DeepSeek-R1 | Muse Spark 1.3 |
|---|---|---|
| LMArena Text | 1428 | 1490 |
| LMArena Creative Writing | 1405 | 1455 |
| EQ-Bench Creative Writing | 1500 | 1906 |
| LMArena Multi-Turn | 1405 | 1482 |
| Short-Story Creative Writing | 83% | — |
| WildBench | 82.8% | — |
| LiveBench Language | 48.5% | — |

## FAQ

### Is DeepSeek-R1 better than Muse Spark 1.3?

Muse Spark 1.3 is the stronger model overall, scoring 54.8 to 42.3 on the Noometry Index. DeepSeek-R1 costs 2.2× 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-R1 or Muse Spark 1.3?

DeepSeek-R1 is cheaper. It lists at $0.50 per million input tokens and $2.15 per million output tokens; Muse Spark 1.3 lists at $1.25 and $4.25.

### Is DeepSeek-R1 or Muse Spark 1.3 better for coding?

Muse Spark 1.3 scores higher on coding benchmarks: 56.6 versus 46.3 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-R1 and Muse Spark 1.3 share?

22 benchmarks have published results for both models. DeepSeek-R1 has 52 scored results on Noometry and Muse Spark 1.3 has 37.