# DeepSeek-R1 vs Muse Spark 1.1

> Muse Spark 1.1 is the stronger model overall, scoring 49.9 to 42.0 on the Noometry Index. DeepSeek-R1 costs 2.2× less per token, which makes it the better buy when Muse Spark 1.1's lead doesn't matter for your workload.

- Canonical page: https://noometry.com/compare/deepseek-r1-vs-muse-spark-1-1
- Last updated: 2026-10-10
- Shared benchmarks: 21

## Summary

- They share 21 benchmarks with published results for both. DeepSeek-R1 scores higher in 1 category and Muse Spark 1.1 in 8 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where Muse Spark 1.1 leads 45.9 to 18.7.
- The biggest single-benchmark swing is SciCode: 35.7% for DeepSeek-R1 and 58.8% for Muse Spark 1.1.
- DeepSeek-R1 is cheaper at $0.50 / $2.15 per million input/output tokens, against $1.25 / $4.25 for Muse Spark 1.1.
- Muse Spark 1.1 accepts more context: 1.05M tokens versus 164K.

## Snapshot

| | DeepSeek-R1 | Muse Spark 1.1 |
|---|---|---|
| Provider | DeepSeek | Meta |
| Noometry Index | 42.0 | 49.9 |
| Rank | 110 | 43 |
| Context | 164K | 1.05M |
| Input $/M | $0.50 | $1.25 |
| Output $/M | $2.15 | $4.25 |
| Weights | Proprietary | Proprietary |

## Coding

- DeepSeek-R1: 45.9 (#63)
- Muse Spark 1.1: 50.8 (#38)

| Benchmark | DeepSeek-R1 | Muse Spark 1.1 |
|---|---|---|
| SciCode | 35.7% | 58.8% |
| LMArena Coding | 1427 | 1497 |
| DeepSWE | — | 53.3% |
| Aider Polyglot | 71.4% | — |
| LMArena WebDev | — | 1542 |
| WeirdML | 41.6% | — |
| LiveBench Coding | 66.7% | — |
| ALE-Bench | 804.12 | — |
| AlgoTune | 1.7 | — |

## Agentic & Tool Use

- DeepSeek-R1: 30.4 (#68)
- Muse Spark 1.1: 33.9 (#45)

| Benchmark | DeepSeek-R1 | Muse Spark 1.1 |
|---|---|---|
| τ²-bench Banking | — | 40.5% |
| DeepResearch Bench | 35.1% | — |
| BALROG | 34.9% | — |
| GBAEval | — | 7.9% |
| GDP.pdf | — | 15% |
| METR Time Horizons | 53.8% | — |
| Vending-Bench 2 | — | 6,520 |

## Reasoning

- DeepSeek-R1: 18.7 (#253)
- Muse Spark 1.1: 45.9 (#41)

| Benchmark | DeepSeek-R1 | Muse Spark 1.1 |
|---|---|---|
| CritPt | 1.1% | 15.1% |
| LMArena Hard Prompts | 1415 | 1486 |
| Epoch Capabilities Index | 141.3 | 154.32 |
| ARC-AGI-2 | 1.3% | — |
| SimpleBench | 40.8% | — |
| Kagi LLM Benchmark | 69.4% | — |
| NYT Connections (extended) | — | 84.9% |
| ARC-AGI-1 | 21.2% | — |
| LiveBench Reasoning | 83.2% | — |
| DTBench | — | 94.4% |
| LiveBench Data Analysis | 69.8% | — |
| LMCA | — | 49.9% |
| Surface Evolver Bench | — | 52.5% |
| ForecastBench | 60 | — |
| LiveBench | 71.6% | — |

## Math

- DeepSeek-R1: 42.0 (#70)
- Muse Spark 1.1: 44.0 (#67)

| Benchmark | DeepSeek-R1 | Muse Spark 1.1 |
|---|---|---|
| LMArena Math | 1401 | 1477 |
| OTIS Mock AIME 2024-2025 | 66.4% | — |
| ProofBench | — | 39% |
| Omni-MATH | 42.4% | — |
| LiveBench Math | 80.7% | — |
| MATH Level 5 | 96.6% | — |

## Knowledge

- DeepSeek-R1: 44.3 (#81)
- Muse Spark 1.1: 52.8 (#55)

| Benchmark | DeepSeek-R1 | Muse Spark 1.1 |
|---|---|---|
| LMArena Expert | 1394 | 1480 |
| GPQA Diamond | 76.3% | — |
| SimpleQA Verified | — | 57.8% |
| MMLU-Pro | 79.3% | — |
| Confabulations | 12.7% | — |
| Vectara Hallucination Rate | 11.3% | — |
| GPQA (HELM) | 66.6% | — |

## Multimodal

- DeepSeek-R1: —
- Muse Spark 1.1: 41.9 (#28)

| Benchmark | DeepSeek-R1 | Muse Spark 1.1 |
|---|---|---|
| LMArena Vision | — | 1292 |
| LMArena Document | — | 1465 |

## Multilingual

- DeepSeek-R1: 52.6 (#80)
- Muse Spark 1.1: 56.8 (#16)

| Benchmark | DeepSeek-R1 | Muse Spark 1.1 |
|---|---|---|
| LMArena Non-English | 1412 | 1471 |
| LMArena Chinese | 1443 | 1518 |
| LMArena French | 1415 | 1494 |
| LMArena German | 1405 | 1466 |
| LMArena Japanese | 1394 | 1445 |
| LMArena Korean | 1359 | 1459 |
| LMArena Russian | 1423 | 1482 |
| LMArena Spanish | 1410 | 1471 |

## Instruction Following

- DeepSeek-R1: 72.2 (#138)
- Muse Spark 1.1: 76.8 (#36)

| Benchmark | DeepSeek-R1 | Muse Spark 1.1 |
|---|---|---|
| LMArena Instruction Following | 1382 | 1457 |
| LiveBench Instruction Following | 80.5% | — |
| IFEval | 78.4% | — |

## Long Context

- DeepSeek-R1: 45.4 (#36)
- Muse Spark 1.1: 44.8 (#55)

| Benchmark | DeepSeek-R1 | Muse Spark 1.1 |
|---|---|---|
| LMArena Longer Query | 1391 | 1463 |
| Fiction.LiveBench | 75% | — |

## Writing & Preference

- DeepSeek-R1: 61.5 (#84)
- Muse Spark 1.1: 73.6 (#11)

| Benchmark | DeepSeek-R1 | Muse Spark 1.1 |
|---|---|---|
| LMArena Text | 1428 | 1479 |
| LMArena Creative Writing | 1406 | 1440 |
| EQ-Bench Creative Writing | 1500 | 1927 |
| LMArena Multi-Turn | 1404 | 1484 |
| Short-Story Creative Writing | 83% | — |
| WildBench | 82.8% | — |
| EQ-Bench 4 | — | 1260 |
| LiveBench Language | 48.5% | — |

## FAQ

### Is DeepSeek-R1 better than Muse Spark 1.1?

Muse Spark 1.1 is the stronger model overall, scoring 49.9 to 42.0 on the Noometry Index. DeepSeek-R1 costs 2.2× less per token, which makes it the better buy when Muse Spark 1.1's lead doesn't matter for your workload.

### Which is cheaper, DeepSeek-R1 or Muse Spark 1.1?

DeepSeek-R1 is cheaper. It lists at $0.50 per million input tokens and $2.15 per million output tokens; Muse Spark 1.1 lists at $1.25 and $4.25.

### Is DeepSeek-R1 or Muse Spark 1.1 better for coding?

Muse Spark 1.1 scores higher on coding benchmarks: 50.8 versus 45.9 in the Noometry coding category.

### Which has the bigger context window?

Muse Spark 1.1 does, with 1.05M tokens against 164K.

### How many benchmarks do DeepSeek-R1 and Muse Spark 1.1 share?

21 benchmarks have published results for both models. DeepSeek-R1 has 52 scored results on Noometry and Muse Spark 1.1 has 36.