Model comparison
DeepSeek-V3.2-Speciale vs Muse Spark 1.1
Muse Spark 1.1 is the stronger model overall, scoring 49.9 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 1.1'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 1.1 in 3 categories; 3 gaps are clear of the uncertainty.
- The widest gap is in writing & preference, where Muse Spark 1.1 leads 73.4 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 1.1.
- Muse Spark 1.1 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 1.1 | |
|---|---|---|
| Provider | DeepSeek | Meta |
| Noometry Index | 39.7 | 49.9 |
| Released | 2025-12-01 | 2026-04-08 |
| 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 1.1 leads
DeepSeek-V3.2-Speciale: 40.4 (#140), Muse Spark 1.1: 51.3 (#40)
| Benchmark | DeepSeek-V3.2-Speciale | Muse Spark 1.1 |
|---|---|---|
| DeepSWE | — | 53.3% |
| LMArena WebDev | — | 1542 |
| SciCode | — | 58.8% |
| WeirdML | 46.7% | — |
| LMArena Coding | — | 1498 |
Agentic & Tool Use Not comparable
DeepSeek-V3.2-Speciale: —, Muse Spark 1.1: 30.8 (#73)
| Benchmark | DeepSeek-V3.2-Speciale | Muse Spark 1.1 |
|---|---|---|
| APEX-Agents | — | 31.8% |
| τ²-bench Banking | — | 40.5% |
| GBAEval | — | 7.9% |
| GDP.pdf | — | 15% |
| Vending-Bench 2 | — | 6,520 |
Reasoning Muse Spark 1.1 leads
DeepSeek-V3.2-Speciale: 32.9 (#73), Muse Spark 1.1: 47.1 (#44)
| Benchmark | DeepSeek-V3.2-Speciale | Muse Spark 1.1 |
|---|---|---|
| SimpleBench | 52.6% | — |
| NYT Connections (extended) | — | 84.9% |
| CritPt | — | 15.1% |
| LMArena Hard Prompts | — | 1486 |
| DTBench | — | 94.4% |
| LMCA | — | 49.9% |
| Surface Evolver Bench | — | 52.5% |
| Epoch Capabilities Index | — | 154.21 |
Math Not comparable
DeepSeek-V3.2-Speciale: —, Muse Spark 1.1: 45.5 (#76)
| Benchmark | DeepSeek-V3.2-Speciale | Muse Spark 1.1 |
|---|---|---|
| ProofBench | — | 39% |
| LMArena Math | — | 1483 |
Knowledge Not comparable
DeepSeek-V3.2-Speciale: —, Muse Spark 1.1: 53.1 (#59)
| Benchmark | DeepSeek-V3.2-Speciale | Muse Spark 1.1 |
|---|---|---|
| SimpleQA Verified | — | 57.8% |
| LMArena Expert | — | 1478 |
Multimodal Not comparable
DeepSeek-V3.2-Speciale: —, Muse Spark 1.1: 42.6 (#29)
| Benchmark | DeepSeek-V3.2-Speciale | Muse Spark 1.1 |
|---|---|---|
| LMArena Vision | — | 1293 |
| LMArena Document | — | 1465 |
Multilingual Not comparable
DeepSeek-V3.2-Speciale: —, Muse Spark 1.1: 56.7 (#17)
| Benchmark | DeepSeek-V3.2-Speciale | Muse Spark 1.1 |
|---|---|---|
| LMArena Non-English | — | 1472 |
| LMArena Chinese | — | 1518 |
| LMArena French | — | 1494 |
| LMArena German | — | 1466 |
| LMArena Japanese | — | 1451 |
| LMArena Korean | — | 1458 |
| LMArena Russian | — | 1483 |
| LMArena Spanish | — | 1464 |
Instruction Following Not comparable
DeepSeek-V3.2-Speciale: —, Muse Spark 1.1: 76.5 (#39)
| Benchmark | DeepSeek-V3.2-Speciale | Muse Spark 1.1 |
|---|---|---|
| LMArena Instruction Following | — | 1457 |
Long Context Not comparable
DeepSeek-V3.2-Speciale: —, Muse Spark 1.1: 44.8 (#58)
| Benchmark | DeepSeek-V3.2-Speciale | Muse Spark 1.1 |
|---|---|---|
| LMArena Longer Query | — | 1462 |
Writing & Preference Muse Spark 1.1 leads
DeepSeek-V3.2-Speciale: 46.0 (#222), Muse Spark 1.1: 73.4 (#11)
| Benchmark | DeepSeek-V3.2-Speciale | Muse Spark 1.1 |
|---|---|---|
| EQ-Bench Creative Writing | 1276 | 1927 |
| LMArena Text | — | 1479 |
| LMArena Creative Writing | — | 1437 |
| EQ-Bench 4 | — | 1260 |
| LMArena Multi-Turn | — | 1485 |
Frequently asked questions
Is DeepSeek-V3.2-Speciale better than Muse Spark 1.1?
Muse Spark 1.1 is the stronger model overall, scoring 49.9 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 1.1's lead doesn't matter for your workload.
Which is cheaper, DeepSeek-V3.2-Speciale or Muse Spark 1.1?
DeepSeek-V3.2-Speciale is cheaper. It lists at $0.58 per million input tokens and $1.68 per million output tokens; Muse Spark 1.1 lists at $1.25 and $4.25.
Is DeepSeek-V3.2-Speciale or Muse Spark 1.1 better for coding?
Muse Spark 1.1 scores higher on coding benchmarks: 51.3 versus 40.4 in the Noometry coding category.
Which has the bigger context window?
Muse Spark 1.1 does, with 1.05M tokens against 128K.
How many benchmarks do DeepSeek-V3.2-Speciale and Muse Spark 1.1 share?
1 benchmark has published results for both models. DeepSeek-V3.2-Speciale has 3 scored results on Noometry and Muse Spark 1.1 has 37.