Model comparison
DeepSeek-V3.1 vs Muse Spark 1.3
Muse Spark 1.3 is the stronger model overall, scoring 54.8 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 1.3's lead doesn't matter for your workload.
Last verified . 21 shared benchmarks.
DeepSeek
MetaSummary
- They share 21 benchmarks with published results for both. DeepSeek-V3.1 scores higher in 1 category and Muse Spark 1.3 in 7 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in math, where Muse Spark 1.3 leads 73.1 to 38.9.
- The biggest single-benchmark swing is LMCA: 24.3% for DeepSeek-V3.1 and 53.9% for Muse Spark 1.3.
- DeepSeek-V3.1 is cheaper at $0.25 / $0.95 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.
- DeepSeek-V3.1 has downloadable open weights; the other is API-only.
Side by side
| DeepSeek-V3.1 | Muse Spark 1.3 | |
|---|---|---|
| Provider | DeepSeek | Meta |
| Noometry Index | 42.8 | 54.8 |
| Released | 2025-08-21 | 2026-09-02 |
| 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 | 37 |
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Category by category
Coding Muse Spark 1.3 leads
DeepSeek-V3.1: 40.3 (#144), Muse Spark 1.3: 56.6 (#21)
| Benchmark | DeepSeek-V3.1 | Muse Spark 1.3 |
|---|---|---|
| LMArena Coding | 1417 | 1514 |
| CursorBench | — | 41.6% |
| LMArena WebDev | — | 1657 |
| SciCode | — | 59.7% |
| WeirdML | 38.4% | — |
Agentic & Tool Use Not comparable
DeepSeek-V3.1: —, Muse Spark 1.3: 38.6 (#30)
| Benchmark | DeepSeek-V3.1 | Muse Spark 1.3 |
|---|---|---|
| APEX-Agents | — | 57.8% |
| GDP.pdf | — | 27.6% |
Reasoning Muse Spark 1.3 leads
DeepSeek-V3.1: 27.9 (#110), Muse Spark 1.3: 54.0 (#27)
| Benchmark | DeepSeek-V3.1 | Muse Spark 1.3 |
|---|---|---|
| LMArena Hard Prompts | 1417 | 1503 |
| DTBench | 82.7% | 96.5% |
| LMCA | 24.3% | 53.9% |
| Epoch Capabilities Index | 139.92 | 156.75 |
| SimpleBench | 40% | — |
| Kagi LLM Benchmark | 53.2% | — |
| NYT Connections (extended) | — | 85.1% |
| CritPt | — | 26% |
| Chess Puzzles | — | 38% |
| Mystery Game Puzzles | — | 25% |
| Bench to the Future 3 | — | 0.14 |
| ForecastBench | 58 | — |
Math Muse Spark 1.3 leads
DeepSeek-V3.1: 38.9 (#122), Muse Spark 1.3: 73.1 (#21)
| Benchmark | DeepSeek-V3.1 | Muse Spark 1.3 |
|---|---|---|
| LMArena Math | 1420 | 1494 |
| FrontierMath (Tiers 1-3) | — | 74.4% |
| FrontierMath Tier 4 | — | 46.3% |
| OTIS Mock AIME 2024-2025 | — | 99.2% |
| ProofBench | — | 58% |
Knowledge DeepSeek-V3.1 leads
DeepSeek-V3.1: 43.7 (#90), Muse Spark 1.3: 42.6 (#95)
| Benchmark | DeepSeek-V3.1 | Muse Spark 1.3 |
|---|---|---|
| LMArena Expert | 1405 | 1516 |
| Vectara Hallucination Rate | 5.5% | — |
Multimodal Not comparable
DeepSeek-V3.1: —, Muse Spark 1.3: 43.7 (#22)
| Benchmark | DeepSeek-V3.1 | Muse Spark 1.3 |
|---|---|---|
| LMArena Vision | — | 1309 |
| LMArena Document | — | 1471 |
Multilingual Muse Spark 1.3 leads
DeepSeek-V3.1: 51.6 (#106), Muse Spark 1.3: 57.4 (#8)
| Benchmark | DeepSeek-V3.1 | Muse Spark 1.3 |
|---|---|---|
| LMArena Non-English | 1400 | 1481 |
| LMArena Chinese | 1469 | 1529 |
| LMArena French | 1447 | 1524 |
| LMArena German | 1411 | 1515 |
| LMArena Japanese | 1378 | 1474 |
| LMArena Korean | 1337 | 1501 |
| LMArena Russian | 1405 | 1490 |
| LMArena Spanish | 1431 | 1490 |
Instruction Following Muse Spark 1.3 leads
DeepSeek-V3.1: 73.9 (#110), Muse Spark 1.3: 77.5 (#22)
| Benchmark | DeepSeek-V3.1 | Muse Spark 1.3 |
|---|---|---|
| LMArena Instruction Following | 1400 | 1477 |
Long Context Muse Spark 1.3 leads
DeepSeek-V3.1: 36.3 (#232), Muse Spark 1.3: 45.6 (#32)
| Benchmark | DeepSeek-V3.1 | Muse Spark 1.3 |
|---|---|---|
| LMArena Longer Query | 1422 | 1488 |
| Fiction.LiveBench | 52.8% | — |
Writing & Preference Muse Spark 1.3 leads
DeepSeek-V3.1: 60.3 (#98), Muse Spark 1.3: 73.6 (#9)
| Benchmark | DeepSeek-V3.1 | Muse Spark 1.3 |
|---|---|---|
| LMArena Text | 1420 | 1490 |
| LMArena Creative Writing | 1401 | 1455 |
| EQ-Bench Creative Writing | 1436 | 1906 |
| LMArena Multi-Turn | 1408 | 1482 |
Frequently asked questions
Is DeepSeek-V3.1 better than Muse Spark 1.3?
Muse Spark 1.3 is the stronger model overall, scoring 54.8 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 1.3's lead doesn't matter for your workload.
Which is cheaper, DeepSeek-V3.1 or Muse Spark 1.3?
DeepSeek-V3.1 is cheaper. It lists at $0.25 per million input tokens and $0.95 per million output tokens; Muse Spark 1.3 lists at $1.25 and $4.25.
Is DeepSeek-V3.1 or Muse Spark 1.3 better for coding?
Muse Spark 1.3 scores higher on coding benchmarks: 56.6 versus 40.3 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-V3.1 and Muse Spark 1.3 share?
21 benchmarks have published results for both models. DeepSeek-V3.1 has 27 scored results on Noometry and Muse Spark 1.3 has 37.