# DeepSeek-V3 vs Muse Spark 1.2

> Muse Spark 1.2 is the stronger model overall, scoring 49.8 to 39.2 on the Noometry Index. DeepSeek-V3 costs 4.9× 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-v3-vs-muse-spark-1-2
- Last updated: 2026-10-10
- Shared benchmarks: 19

## Summary

- They share 19 benchmarks with published results for both. DeepSeek-V3 scores higher in 0 categories and Muse Spark 1.2 in 8 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where Muse Spark 1.2 leads 50.4 to 20.0.
- The biggest single-benchmark swing is SimpleBench: 27.2% for DeepSeek-V3 and 74.5% for Muse Spark 1.2.
- DeepSeek-V3 is cheaper at $0.24 / $0.90 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.
- DeepSeek-V3 has downloadable open weights; the other is API-only.

## Snapshot

| | DeepSeek-V3 | Muse Spark 1.2 |
|---|---|---|
| Provider | DeepSeek | Meta |
| Noometry Index | 39.2 | 49.8 |
| Rank | 157 | 44 |
| Context | 164K | 1.05M |
| Input $/M | $0.24 | $1.25 |
| Output $/M | $0.90 | $4.25 |
| Weights | Open | Proprietary |

## Coding

- DeepSeek-V3: 41.9 (#99)
- Muse Spark 1.2: 48.6 (#49)

| Benchmark | DeepSeek-V3 | Muse Spark 1.2 |
|---|---|---|
| SciCode | 35.8% | 56.4% |
| WeirdML | 36.1% | 60.3% |
| LMArena Coding | 1368 | 1497 |
| DeepSWE | — | 54.9% |
| Aider Polyglot | 55.1% | — |
| LMArena WebDev | — | 1532 |
| FrontierSWE | — | 12% |
| BigCodeBench Instruct | 50% | — |
| LiveBench Coding | 70.9% | — |
| BigCodeBench Complete | 62.2% | — |
| HumanEval+ | 86.6% | — |
| MBPP+ | 73% | — |

## Agentic & Tool Use

- DeepSeek-V3: —
- Muse Spark 1.2: 29.5 (#77)

| Benchmark | DeepSeek-V3 | Muse Spark 1.2 |
|---|---|---|
| APEX-Agents | — | 36.4% |
| GDP.pdf | — | 16% |
| METR Time Horizons | 49.6% | — |

## Reasoning

- DeepSeek-V3: 20.0 (#226)
- Muse Spark 1.2: 50.4 (#32)

| Benchmark | DeepSeek-V3 | Muse Spark 1.2 |
|---|---|---|
| SimpleBench | 27.2% | 74.5% |
| CritPt | 0% | 17.7% |
| LMArena Hard Prompts | 1365 | 1490 |
| DTBench | 64.8% | 94.7% |
| LMCA | 15.5% | 48.4% |
| Epoch Capabilities Index | 135.95 | 155.18 |
| Kagi LLM Benchmark | 52.3% | — |
| NYT Connections (extended) | — | 79.2% |
| LiveBench Reasoning | 65.8% | — |
| LiveBench Data Analysis | 60.9% | — |
| BIG-Bench Hard | 87.5% | — |
| ForecastBench | 59.1 | — |
| HellaSwag | 88.9% | — |
| LiveBench | 66.9% | — |
| PIQA | 84.7% | — |
| WinoGrande | 85.2% | — |

## Math

- DeepSeek-V3: 30.4 (#208)
- Muse Spark 1.2: 43.8 (#68)

| Benchmark | DeepSeek-V3 | Muse Spark 1.2 |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 37.8% | — |
| ProofBench | — | 43% |
| Omni-MATH | 40.3% | — |
| LiveBench Math | 73.5% | — |
| LMArena Math | 1373 | — |
| MATH Level 5 | 75.5% | — |
| FrontierMath (Feb 2025 set) | 1.7% | — |

## Knowledge

- DeepSeek-V3: 37.5 (#147)
- Muse Spark 1.2: 53.9 (#49)

| Benchmark | DeepSeek-V3 | Muse Spark 1.2 |
|---|---|---|
| LMArena Expert | 1352 | 1485 |
| GPQA Diamond | 67.6% | — |
| SimpleQA Verified | — | 60.3% |
| MMLU-Pro | 72.3% | — |
| Confabulations | 26.1% | — |
| Vectara Hallucination Rate | 6.1% | — |
| GPQA (HELM) | 53.8% | — |
| ARC (AI2) Challenge | 95.3% | — |
| MMLU | 87.2% | — |
| TriviaQA | 82.9% | — |

## Multimodal

- DeepSeek-V3: —
- Muse Spark 1.2: 42.6 (#24)

| Benchmark | DeepSeek-V3 | Muse Spark 1.2 |
|---|---|---|
| LMArena Vision | — | 1304 |

## Multilingual

- DeepSeek-V3: 48.8 (#138)
- Muse Spark 1.2: 57.4 (#10)

| Benchmark | DeepSeek-V3 | Muse Spark 1.2 |
|---|---|---|
| LMArena Non-English | 1358 | 1479 |
| LMArena Chinese | 1390 | 1519 |
| LMArena Russian | 1372 | 1494 |
| LMArena French | 1386 | — |
| LMArena German | 1374 | — |
| LMArena Japanese | 1333 | — |
| LMArena Korean | 1321 | — |
| LMArena Spanish | 1358 | — |

## Instruction Following

- DeepSeek-V3: 73.0 (#125)
- Muse Spark 1.2: 77.1 (#30)

| Benchmark | DeepSeek-V3 | Muse Spark 1.2 |
|---|---|---|
| LMArena Instruction Following | 1345 | 1466 |
| LiveBench Instruction Following | 81.5% | — |
| IFEval | 83.2% | — |

## Long Context

- DeepSeek-V3: 34.0 (#248)
- Muse Spark 1.2: 45.4 (#35)

| Benchmark | DeepSeek-V3 | Muse Spark 1.2 |
|---|---|---|
| LMArena Longer Query | 1353 | 1483 |
| Fiction.LiveBench | 50% | — |

## Writing & Preference

- DeepSeek-V3: 57.6 (#124)
- Muse Spark 1.2: 72.6 (#14)

| Benchmark | DeepSeek-V3 | Muse Spark 1.2 |
|---|---|---|
| LMArena Text | 1375 | 1486 |
| LMArena Creative Writing | 1364 | 1444 |
| EQ-Bench Creative Writing | 1472 | 1840 |
| LMArena Multi-Turn | 1389 | 1504 |
| Short-Story Creative Writing | 77% | — |
| WildBench | 83% | — |
| LiveBench Language | 49.1% | — |

## FAQ

### Is DeepSeek-V3 better than Muse Spark 1.2?

Muse Spark 1.2 is the stronger model overall, scoring 49.8 to 39.2 on the Noometry Index. DeepSeek-V3 costs 4.9× 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-V3 or Muse Spark 1.2?

DeepSeek-V3 is cheaper. It lists at $0.24 per million input tokens and $0.90 per million output tokens; Muse Spark 1.2 lists at $1.25 and $4.25.

### Is DeepSeek-V3 or Muse Spark 1.2 better for coding?

Muse Spark 1.2 scores higher on coding benchmarks: 48.6 versus 41.9 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-V3 and Muse Spark 1.2 share?

19 benchmarks have published results for both models. DeepSeek-V3 has 60 scored results on Noometry and Muse Spark 1.2 has 28.