# DeepSeek-R1 vs Muse Spark 1.2

> Muse Spark 1.2 is the stronger model overall, scoring 49.8 to 42.0 on the Noometry Index. DeepSeek-R1 costs 2.2× less per token, which makes it the better buy when Muse Spark 1.2's lead doesn't matter for your workload.

- Canonical page: https://noometry.com/compare/deepseek-r1-vs-muse-spark-1-2
- Last updated: 2026-10-10
- Shared benchmarks: 17

## Summary

- They share 17 benchmarks with published results for both. DeepSeek-R1 scores higher in 1 category and Muse Spark 1.2 in 8 categories; 7 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where Muse Spark 1.2 leads 50.4 to 18.7.
- The biggest single-benchmark swing is SimpleBench: 40.8% for DeepSeek-R1 and 74.5% for Muse Spark 1.2.
- DeepSeek-R1 is cheaper at $0.50 / $2.15 per million input/output tokens, against $1.25 / $4.25 for Muse Spark 1.2.
- Muse Spark 1.2 accepts more context: 1.05M tokens versus 164K.

## Snapshot

| | DeepSeek-R1 | Muse Spark 1.2 |
|---|---|---|
| Provider | DeepSeek | Meta |
| Noometry Index | 42.0 | 49.8 |
| Rank | 110 | 44 |
| 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 1.2: 48.6 (#49)

| Benchmark | DeepSeek-R1 | Muse Spark 1.2 |
|---|---|---|
| SciCode | 35.7% | 56.4% |
| WeirdML | 41.6% | 60.3% |
| LMArena Coding | 1427 | 1497 |
| DeepSWE | — | 54.9% |
| Aider Polyglot | 71.4% | — |
| LMArena WebDev | — | 1532 |
| FrontierSWE | — | 12% |
| LiveBench Coding | 66.7% | — |
| ALE-Bench | 804.12 | — |
| AlgoTune | 1.7 | — |

## Agentic & Tool Use

- DeepSeek-R1: 30.4 (#68)
- Muse Spark 1.2: 29.5 (#77)

| Benchmark | DeepSeek-R1 | Muse Spark 1.2 |
|---|---|---|
| APEX-Agents | — | 36.4% |
| DeepResearch Bench | 35.1% | — |
| BALROG | 34.9% | — |
| GDP.pdf | — | 16% |
| METR Time Horizons | 53.8% | — |

## Reasoning

- DeepSeek-R1: 18.7 (#253)
- Muse Spark 1.2: 50.4 (#32)

| Benchmark | DeepSeek-R1 | Muse Spark 1.2 |
|---|---|---|
| SimpleBench | 40.8% | 74.5% |
| CritPt | 1.1% | 17.7% |
| LMArena Hard Prompts | 1415 | 1490 |
| Epoch Capabilities Index | 141.3 | 155.18 |
| ARC-AGI-2 | 1.3% | — |
| Kagi LLM Benchmark | 69.4% | — |
| NYT Connections (extended) | — | 79.2% |
| ARC-AGI-1 | 21.2% | — |
| LiveBench Reasoning | 83.2% | — |
| DTBench | — | 94.7% |
| LiveBench Data Analysis | 69.8% | — |
| LMCA | — | 48.4% |
| ForecastBench | 60 | — |
| LiveBench | 71.6% | — |

## Math

- DeepSeek-R1: 42.0 (#70)
- Muse Spark 1.2: 43.8 (#68)

| Benchmark | DeepSeek-R1 | Muse Spark 1.2 |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 66.4% | — |
| ProofBench | — | 43% |
| Omni-MATH | 42.4% | — |
| LiveBench Math | 80.7% | — |
| LMArena Math | 1401 | — |
| MATH Level 5 | 96.6% | — |

## Knowledge

- DeepSeek-R1: 44.3 (#81)
- Muse Spark 1.2: 53.9 (#49)

| Benchmark | DeepSeek-R1 | Muse Spark 1.2 |
|---|---|---|
| LMArena Expert | 1394 | 1485 |
| GPQA Diamond | 76.3% | — |
| SimpleQA Verified | — | 60.3% |
| MMLU-Pro | 79.3% | — |
| Confabulations | 12.7% | — |
| Vectara Hallucination Rate | 11.3% | — |
| GPQA (HELM) | 66.6% | — |

## Multimodal

- DeepSeek-R1: —
- Muse Spark 1.2: 42.6 (#24)

| Benchmark | DeepSeek-R1 | Muse Spark 1.2 |
|---|---|---|
| LMArena Vision | — | 1304 |

## Multilingual

- DeepSeek-R1: 52.6 (#80)
- Muse Spark 1.2: 57.4 (#10)

| Benchmark | DeepSeek-R1 | Muse Spark 1.2 |
|---|---|---|
| LMArena Non-English | 1412 | 1479 |
| LMArena Chinese | 1443 | 1519 |
| LMArena Russian | 1423 | 1494 |
| LMArena French | 1415 | — |
| LMArena German | 1405 | — |
| LMArena Japanese | 1394 | — |
| LMArena Korean | 1359 | — |
| LMArena Spanish | 1410 | — |

## Instruction Following

- DeepSeek-R1: 72.2 (#138)
- Muse Spark 1.2: 77.1 (#30)

| Benchmark | DeepSeek-R1 | Muse Spark 1.2 |
|---|---|---|
| LMArena Instruction Following | 1382 | 1466 |
| LiveBench Instruction Following | 80.5% | — |
| IFEval | 78.4% | — |

## Long Context

- DeepSeek-R1: 45.4 (#36)
- Muse Spark 1.2: 45.4 (#35)

| Benchmark | DeepSeek-R1 | Muse Spark 1.2 |
|---|---|---|
| LMArena Longer Query | 1391 | 1483 |
| Fiction.LiveBench | 75% | — |

## Writing & Preference

- DeepSeek-R1: 61.5 (#84)
- Muse Spark 1.2: 72.6 (#14)

| Benchmark | DeepSeek-R1 | Muse Spark 1.2 |
|---|---|---|
| LMArena Text | 1428 | 1486 |
| LMArena Creative Writing | 1406 | 1444 |
| EQ-Bench Creative Writing | 1500 | 1840 |
| LMArena Multi-Turn | 1404 | 1504 |
| Short-Story Creative Writing | 83% | — |
| WildBench | 82.8% | — |
| LiveBench Language | 48.5% | — |

## FAQ

### Is DeepSeek-R1 better than Muse Spark 1.2?

Muse Spark 1.2 is the stronger model overall, scoring 49.8 to 42.0 on the Noometry Index. DeepSeek-R1 costs 2.2× less per token, which makes it the better buy when Muse Spark 1.2's lead doesn't matter for your workload.

### Which is cheaper, DeepSeek-R1 or Muse Spark 1.2?

DeepSeek-R1 is cheaper. It lists at $0.50 per million input tokens and $2.15 per million output tokens; Muse Spark 1.2 lists at $1.25 and $4.25.

### Is DeepSeek-R1 or Muse Spark 1.2 better for coding?

Muse Spark 1.2 scores higher on coding benchmarks: 48.6 versus 45.9 in the Noometry coding category.

### Which has the bigger context window?

Muse Spark 1.2 does, with 1.05M tokens against 164K.

### How many benchmarks do DeepSeek-R1 and Muse Spark 1.2 share?

17 benchmarks have published results for both models. DeepSeek-R1 has 52 scored results on Noometry and Muse Spark 1.2 has 28.