Model comparison
DeepSeek-V3.1 vs Muse Spark 1.2
Muse Spark 1.2 is the stronger model overall, scoring 50.3 to 42.8 on the Noometry Index. DeepSeek-V3.1 costs 4.7× less per token, which makes it the better buy when Muse Spark 1.2's lead doesn't matter for your workload.
Last verified . 20 shared benchmarks.
DeepSeek
MetaSummary
- They share 20 benchmarks with published results for both. DeepSeek-V3.1 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 51.3 to 27.9.
- The biggest single-benchmark swing is SimpleBench: 40% for DeepSeek-V3.1 and 74.5% for Muse Spark 1.2.
- DeepSeek-V3.1 is cheaper at $0.25 / $0.95 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-V3.1 has downloadable open weights; the other is API-only.
Side by side
| DeepSeek-V3.1 | Muse Spark 1.2 | |
|---|---|---|
| Provider | DeepSeek | Meta |
| Noometry Index | 42.8 | 50.3 |
| Released | 2025-08-21 | 2026-08-05 |
| Weights | Open | Proprietary |
| Context window | 164K | 1.05M |
| Max output | 8K | 131K |
| Input $ / M tokens | $0.25 | $1.25 |
| Output $ / M tokens | $0.95 | $4.25 |
| Results tracked | 27 | 31 |
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Category by category
Coding Muse Spark 1.2 leads
DeepSeek-V3.1: 40.3 (#144), Muse Spark 1.2: 49.2 (#51)
| Benchmark | DeepSeek-V3.1 | Muse Spark 1.2 |
|---|---|---|
| WeirdML | 38.4% | 60.3% |
| LMArena Coding | 1417 | 1495 |
| DeepSWE | — | 54.9% |
| LMArena WebDev | — | 1533 |
| FrontierSWE | — | 12% |
| SciCode | — | 56.4% |
Agentic & Tool Use Not comparable
DeepSeek-V3.1: —, Muse Spark 1.2: 29.4 (#87)
| Benchmark | DeepSeek-V3.1 | Muse Spark 1.2 |
|---|---|---|
| APEX-Agents | — | 36.4% |
| GDP.pdf | — | 16% |
Reasoning Muse Spark 1.2 leads
DeepSeek-V3.1: 27.9 (#110), Muse Spark 1.2: 51.3 (#34)
| Benchmark | DeepSeek-V3.1 | Muse Spark 1.2 |
|---|---|---|
| SimpleBench | 40% | 74.5% |
| LMArena Hard Prompts | 1417 | 1486 |
| DTBench | 82.7% | 94.7% |
| LMCA | 24.3% | 48.4% |
| Epoch Capabilities Index | 139.92 | 154.87 |
| Kagi LLM Benchmark | 53.2% | — |
| NYT Connections (extended) | — | 79.2% |
| CritPt | — | 17.7% |
| ForecastBench | 58 | — |
Math Muse Spark 1.2 leads
DeepSeek-V3.1: 38.9 (#122), Muse Spark 1.2: 46.4 (#70)
| Benchmark | DeepSeek-V3.1 | Muse Spark 1.2 |
|---|---|---|
| LMArena Math | 1420 | 1471 |
| ProofBench | — | 43% |
Knowledge Muse Spark 1.2 leads
DeepSeek-V3.1: 43.7 (#90), Muse Spark 1.2: 54.1 (#53)
| Benchmark | DeepSeek-V3.1 | Muse Spark 1.2 |
|---|---|---|
| LMArena Expert | 1405 | 1480 |
| SimpleQA Verified | — | 60.3% |
| Vectara Hallucination Rate | 5.5% | — |
Multimodal Not comparable
DeepSeek-V3.1: —, Muse Spark 1.2: 43.4 (#25)
| Benchmark | DeepSeek-V3.1 | Muse Spark 1.2 |
|---|---|---|
| LMArena Vision | — | 1305 |
Multilingual Muse Spark 1.2 leads
DeepSeek-V3.1: 51.6 (#106), Muse Spark 1.2: 57.1 (#11)
| Benchmark | DeepSeek-V3.1 | Muse Spark 1.2 |
|---|---|---|
| LMArena Non-English | 1400 | 1478 |
| LMArena Chinese | 1469 | 1511 |
| LMArena French | 1447 | 1513 |
| LMArena Russian | 1405 | 1487 |
| LMArena Spanish | 1431 | 1498 |
| LMArena German | 1411 | — |
| LMArena Japanese | 1378 | — |
| LMArena Korean | 1337 | — |
Instruction Following Muse Spark 1.2 leads
DeepSeek-V3.1: 73.9 (#110), Muse Spark 1.2: 76.7 (#36)
| Benchmark | DeepSeek-V3.1 | Muse Spark 1.2 |
|---|---|---|
| LMArena Instruction Following | 1400 | 1461 |
Long Context Muse Spark 1.2 leads
DeepSeek-V3.1: 36.3 (#232), Muse Spark 1.2: 45.2 (#48)
| Benchmark | DeepSeek-V3.1 | Muse Spark 1.2 |
|---|---|---|
| LMArena Longer Query | 1422 | 1475 |
| Fiction.LiveBench | 52.8% | — |
Writing & Preference Muse Spark 1.2 leads
DeepSeek-V3.1: 60.3 (#98), Muse Spark 1.2: 72.3 (#14)
| Benchmark | DeepSeek-V3.1 | Muse Spark 1.2 |
|---|---|---|
| LMArena Text | 1420 | 1482 |
| LMArena Creative Writing | 1401 | 1449 |
| EQ-Bench Creative Writing | 1436 | 1840 |
| LMArena Multi-Turn | 1408 | 1494 |
Frequently asked questions
Is DeepSeek-V3.1 better than Muse Spark 1.2?
Muse Spark 1.2 is the stronger model overall, scoring 50.3 to 42.8 on the Noometry Index. DeepSeek-V3.1 costs 4.7× 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-V3.1 or Muse Spark 1.2?
DeepSeek-V3.1 is cheaper. It lists at $0.25 per million input tokens and $0.95 per million output tokens; Muse Spark 1.2 lists at $1.25 and $4.25.
Is DeepSeek-V3.1 or Muse Spark 1.2 better for coding?
Muse Spark 1.2 scores higher on coding benchmarks: 49.2 versus 40.3 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-V3.1 and Muse Spark 1.2 share?
20 benchmarks have published results for both models. DeepSeek-V3.1 has 27 scored results on Noometry and Muse Spark 1.2 has 31.