Model comparison
DeepSeek-V3.2-Exp vs Muse Spark 1.1
Muse Spark 1.1 is the stronger model overall, scoring 49.9 to 44.3 on the Noometry Index. DeepSeek-V3.2-Exp costs 6.9× less per token, which makes it the better buy when Muse Spark 1.1's lead doesn't matter for your workload.
Last verified . 28 shared benchmarks.
DeepSeek
MetaSummary
- They share 28 benchmarks with published results for both. DeepSeek-V3.2-Exp scores higher in 2 categories and Muse Spark 1.1 in 7 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where Muse Spark 1.1 leads 47.1 to 22.1.
- The biggest single-benchmark swing is NYT Connections (extended): 36.7% for DeepSeek-V3.2-Exp and 84.9% for Muse Spark 1.1.
- DeepSeek-V3.2-Exp is cheaper at $0.26 / $0.38 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 164K.
- DeepSeek-V3.2-Exp has downloadable open weights; the other is API-only.
Side by side
| DeepSeek-V3.2-Exp | Muse Spark 1.1 | |
|---|---|---|
| Provider | DeepSeek | Meta |
| Noometry Index | 44.3 | 49.9 |
| Released | 2025-09-29 | 2026-04-08 |
| Weights | Open | Proprietary |
| Context window | 164K | 1.05M |
| Max output | 66K | 131K |
| Input $ / M tokens | $0.26 | $1.25 |
| Output $ / M tokens | $0.38 | $4.25 |
| Results tracked | 49 | 37 |
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Category by category
Coding Muse Spark 1.1 leads
DeepSeek-V3.2-Exp: 46.5 (#65), Muse Spark 1.1: 51.3 (#40)
| Benchmark | DeepSeek-V3.2-Exp | Muse Spark 1.1 |
|---|---|---|
| LMArena WebDev | 1362 | 1542 |
| SciCode | 38.9% | 58.8% |
| LMArena Coding | 1454 | 1498 |
| DeepSWE | — | 53.3% |
| SWE-bench Verified (bash only) | 70% | — |
| Aider Polyglot | 74.2% | — |
| SWE-bench Multilingual | 59% | — |
| WeirdML | 39.5% | — |
Agentic & Tool Use DeepSeek-V3.2-Exp leads
DeepSeek-V3.2-Exp: 32.7 (#59), Muse Spark 1.1: 30.8 (#73)
| Benchmark | DeepSeek-V3.2-Exp | Muse Spark 1.1 |
|---|---|---|
| APEX-Agents | 21.3% | 31.8% |
| Vending-Bench 2 | 1,034 | 6,520 |
| Terminal-Bench | 39.6% | — |
| Berkeley Function Calling Leaderboard | 56.7% | — |
| TheAgentCompany | 42.9% | — |
| τ²-bench Banking | — | 40.5% |
| GBAEval | — | 7.9% |
| GDP.pdf | — | 15% |
Reasoning Muse Spark 1.1 leads
DeepSeek-V3.2-Exp: 22.1 (#208), Muse Spark 1.1: 47.1 (#44)
| Benchmark | DeepSeek-V3.2-Exp | Muse Spark 1.1 |
|---|---|---|
| NYT Connections (extended) | 36.7% | 84.9% |
| CritPt | 2.9% | 15.1% |
| LMArena Hard Prompts | 1434 | 1486 |
| DTBench | 87.7% | 94.4% |
| LMCA | 29.1% | 49.9% |
| Epoch Capabilities Index | 146.27 | 154.21 |
| ARC-AGI-2 | 4% | — |
| Kagi LLM Benchmark | 52.2% | — |
| ARC-AGI-1 | 57% | — |
| Chess Puzzles | 14% | — |
| Thematic Generalization | 65% | — |
| Surface Evolver Bench | — | 52.5% |
Math Muse Spark 1.1 leads
DeepSeek-V3.2-Exp: 41.7 (#87), Muse Spark 1.1: 45.5 (#76)
| Benchmark | DeepSeek-V3.2-Exp | Muse Spark 1.1 |
|---|---|---|
| ProofBench | 8% | 39% |
| LMArena Math | 1435 | 1483 |
| MathArena Final-Answer Competitions | 57.7% | — |
| OTIS Mock AIME 2024-2025 | 87.8% | — |
| FrontierMath (Feb 2025 set) | 22.1% | — |
| FrontierMath Tier 4 (v1) | 2.1% | — |
Knowledge Muse Spark 1.1 leads
DeepSeek-V3.2-Exp: 51.7 (#66), Muse Spark 1.1: 53.1 (#59)
| Benchmark | DeepSeek-V3.2-Exp | Muse Spark 1.1 |
|---|---|---|
| LMArena Expert | 1436 | 1478 |
| GPQA Diamond | 83.4% | — |
| SimpleQA Verified | — | 57.8% |
| Vectara Hallucination Rate | 5.3% | — |
Multimodal Not comparable
DeepSeek-V3.2-Exp: —, Muse Spark 1.1: 42.6 (#29)
| Benchmark | DeepSeek-V3.2-Exp | Muse Spark 1.1 |
|---|---|---|
| LMArena Vision | — | 1293 |
| LMArena Document | — | 1465 |
Multilingual Muse Spark 1.1 leads
DeepSeek-V3.2-Exp: 52.2 (#90), Muse Spark 1.1: 56.7 (#17)
| Benchmark | DeepSeek-V3.2-Exp | Muse Spark 1.1 |
|---|---|---|
| LMArena Non-English | 1409 | 1472 |
| LMArena Chinese | 1461 | 1518 |
| LMArena French | 1433 | 1494 |
| LMArena German | 1440 | 1466 |
| LMArena Japanese | 1374 | 1451 |
| LMArena Korean | 1371 | 1458 |
| LMArena Russian | 1424 | 1483 |
| LMArena Spanish | 1440 | 1464 |
Instruction Following Muse Spark 1.1 leads
DeepSeek-V3.2-Exp: 74.5 (#93), Muse Spark 1.1: 76.5 (#39)
| Benchmark | DeepSeek-V3.2-Exp | Muse Spark 1.1 |
|---|---|---|
| LMArena Instruction Following | 1413 | 1457 |
Long Context DeepSeek-V3.2-Exp leads
DeepSeek-V3.2-Exp: 47.6 (#16), Muse Spark 1.1: 44.8 (#58)
| Benchmark | DeepSeek-V3.2-Exp | Muse Spark 1.1 |
|---|---|---|
| LMArena Longer Query | 1428 | 1462 |
| Fiction.LiveBench | 83.3% | — |
| CL-bench | 13.2% | — |
| CL-bench Life | 9.5% | — |
Writing & Preference Muse Spark 1.1 leads
DeepSeek-V3.2-Exp: 62.4 (#77), Muse Spark 1.1: 73.4 (#11)
| Benchmark | DeepSeek-V3.2-Exp | Muse Spark 1.1 |
|---|---|---|
| LMArena Text | 1425 | 1479 |
| LMArena Creative Writing | 1403 | 1437 |
| EQ-Bench Creative Writing | 1515 | 1927 |
| LMArena Multi-Turn | 1427 | 1485 |
| EQ-Bench 4 | — | 1260 |
Frequently asked questions
Is DeepSeek-V3.2-Exp better than Muse Spark 1.1?
Muse Spark 1.1 is the stronger model overall, scoring 49.9 to 44.3 on the Noometry Index. DeepSeek-V3.2-Exp costs 6.9× 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-Exp or Muse Spark 1.1?
DeepSeek-V3.2-Exp is cheaper. It lists at $0.26 per million input tokens and $0.38 per million output tokens; Muse Spark 1.1 lists at $1.25 and $4.25.
Is DeepSeek-V3.2-Exp or Muse Spark 1.1 better for coding?
Muse Spark 1.1 scores higher on coding benchmarks: 51.3 versus 46.5 in the Noometry coding category.
Which has the bigger context window?
Muse Spark 1.1 does, with 1.05M tokens against 164K.
How many benchmarks do DeepSeek-V3.2-Exp and Muse Spark 1.1 share?
28 benchmarks have published results for both models. DeepSeek-V3.2-Exp has 49 scored results on Noometry and Muse Spark 1.1 has 37.