Model comparison
DeepSeek-V3.2-Speciale vs Muse Spark 1.3
Muse Spark 1.3 is the stronger model overall, scoring 54.8 to 39.7 on the Noometry Index. DeepSeek-V3.2-Speciale costs 2.3× less per token, which makes it the better buy when Muse Spark 1.3's lead doesn't matter for your workload.
Last verified . 1 shared benchmarks.
DeepSeek
MetaSummary
- They share 1 benchmark with published results for both. DeepSeek-V3.2-Speciale scores higher in 0 categories and Muse Spark 1.3 in 3 categories; 3 gaps are clear of the uncertainty.
- The widest gap is in writing & preference, where Muse Spark 1.3 leads 73.6 to 46.0.
- DeepSeek-V3.2-Speciale is cheaper at $0.58 / $1.68 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 128K.
- DeepSeek-V3.2-Speciale has downloadable open weights; the other is API-only.
Side by side
| DeepSeek-V3.2-Speciale | Muse Spark 1.3 | |
|---|---|---|
| Provider | DeepSeek | Meta |
| Noometry Index | 39.7 | 54.8 |
| Released | 2025-12-01 | 2026-09-02 |
| Weights | Open | Proprietary |
| Context window | 128K | 1.05M |
| Max output | 128K | 131K |
| Input $ / M tokens | $0.58 | $1.25 |
| Output $ / M tokens | $1.68 | $4.25 |
| Results tracked | 3 | 37 |
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Category by category
Coding Muse Spark 1.3 leads
DeepSeek-V3.2-Speciale: 40.4 (#140), Muse Spark 1.3: 56.6 (#21)
| Benchmark | DeepSeek-V3.2-Speciale | Muse Spark 1.3 |
|---|---|---|
| CursorBench | — | 41.6% |
| LMArena WebDev | — | 1657 |
| SciCode | — | 59.7% |
| WeirdML | 46.7% | — |
| LMArena Coding | — | 1514 |
Agentic & Tool Use Not comparable
DeepSeek-V3.2-Speciale: —, Muse Spark 1.3: 38.6 (#30)
| Benchmark | DeepSeek-V3.2-Speciale | Muse Spark 1.3 |
|---|---|---|
| APEX-Agents | — | 57.8% |
| GDP.pdf | — | 27.6% |
Reasoning Muse Spark 1.3 leads
DeepSeek-V3.2-Speciale: 32.9 (#73), Muse Spark 1.3: 54.0 (#27)
| Benchmark | DeepSeek-V3.2-Speciale | Muse Spark 1.3 |
|---|---|---|
| SimpleBench | 52.6% | — |
| NYT Connections (extended) | — | 85.1% |
| CritPt | — | 26% |
| Chess Puzzles | — | 38% |
| LMArena Hard Prompts | — | 1503 |
| Mystery Game Puzzles | — | 25% |
| DTBench | — | 96.5% |
| LMCA | — | 53.9% |
| Bench to the Future 3 | — | 0.14 |
| Epoch Capabilities Index | — | 156.75 |
Math Not comparable
DeepSeek-V3.2-Speciale: —, Muse Spark 1.3: 73.1 (#21)
| Benchmark | DeepSeek-V3.2-Speciale | Muse Spark 1.3 |
|---|---|---|
| FrontierMath (Tiers 1-3) | — | 74.4% |
| FrontierMath Tier 4 | — | 46.3% |
| OTIS Mock AIME 2024-2025 | — | 99.2% |
| ProofBench | — | 58% |
| LMArena Math | — | 1494 |
Knowledge Not comparable
DeepSeek-V3.2-Speciale: —, Muse Spark 1.3: 42.6 (#95)
| Benchmark | DeepSeek-V3.2-Speciale | Muse Spark 1.3 |
|---|---|---|
| LMArena Expert | — | 1516 |
Multimodal Not comparable
DeepSeek-V3.2-Speciale: —, Muse Spark 1.3: 43.7 (#22)
| Benchmark | DeepSeek-V3.2-Speciale | Muse Spark 1.3 |
|---|---|---|
| LMArena Vision | — | 1309 |
| LMArena Document | — | 1471 |
Multilingual Not comparable
DeepSeek-V3.2-Speciale: —, Muse Spark 1.3: 57.4 (#8)
| Benchmark | DeepSeek-V3.2-Speciale | Muse Spark 1.3 |
|---|---|---|
| LMArena Non-English | — | 1481 |
| LMArena Chinese | — | 1529 |
| LMArena French | — | 1524 |
| LMArena German | — | 1515 |
| LMArena Japanese | — | 1474 |
| LMArena Korean | — | 1501 |
| LMArena Russian | — | 1490 |
| LMArena Spanish | — | 1490 |
Instruction Following Not comparable
DeepSeek-V3.2-Speciale: —, Muse Spark 1.3: 77.5 (#22)
| Benchmark | DeepSeek-V3.2-Speciale | Muse Spark 1.3 |
|---|---|---|
| LMArena Instruction Following | — | 1477 |
Long Context Not comparable
DeepSeek-V3.2-Speciale: —, Muse Spark 1.3: 45.6 (#32)
| Benchmark | DeepSeek-V3.2-Speciale | Muse Spark 1.3 |
|---|---|---|
| LMArena Longer Query | — | 1488 |
Writing & Preference Muse Spark 1.3 leads
DeepSeek-V3.2-Speciale: 46.0 (#222), Muse Spark 1.3: 73.6 (#9)
| Benchmark | DeepSeek-V3.2-Speciale | Muse Spark 1.3 |
|---|---|---|
| EQ-Bench Creative Writing | 1276 | 1906 |
| LMArena Text | — | 1490 |
| LMArena Creative Writing | — | 1455 |
| LMArena Multi-Turn | — | 1482 |
Frequently asked questions
Is DeepSeek-V3.2-Speciale better than Muse Spark 1.3?
Muse Spark 1.3 is the stronger model overall, scoring 54.8 to 39.7 on the Noometry Index. DeepSeek-V3.2-Speciale costs 2.3× 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-V3.2-Speciale or Muse Spark 1.3?
DeepSeek-V3.2-Speciale is cheaper. It lists at $0.58 per million input tokens and $1.68 per million output tokens; Muse Spark 1.3 lists at $1.25 and $4.25.
Is DeepSeek-V3.2-Speciale or Muse Spark 1.3 better for coding?
Muse Spark 1.3 scores higher on coding benchmarks: 56.6 versus 40.4 in the Noometry coding category.
Which has the bigger context window?
Muse Spark 1.3 does, with 1.05M tokens against 128K.
How many benchmarks do DeepSeek-V3.2-Speciale and Muse Spark 1.3 share?
1 benchmark has published results for both models. DeepSeek-V3.2-Speciale has 3 scored results on Noometry and Muse Spark 1.3 has 37.