Model comparison
DeepSeek-V3.2-Exp vs Muse Spark
Muse Spark is the stronger model overall, scoring 50.3 to 44.9 on the Noometry Index.
Last verified . 24 shared benchmarks.
DeepSeek
MetaSummary
- They share 24 benchmarks with published results for both. DeepSeek-V3.2-Exp scores higher in 2 categories and Muse Spark in 6 categories; 7 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where Muse Spark leads 65.7 to 51.6.
- The biggest single-benchmark swing is SciCode: 38.9% for DeepSeek-V3.2-Exp and 51.5% for Muse Spark.
- DeepSeek-V3.2-Exp has downloadable open weights; the other is API-only.
Side by side
| DeepSeek-V3.2-Exp | Muse Spark | |
|---|---|---|
| Provider | DeepSeek | Meta |
| Noometry Index | 44.9 | 50.3 |
| Released | 2025-09-29 | 2026-04-08 |
| Weights | Open | Proprietary |
| Context window | 164K | — |
| Max output | 66K | — |
| Input $ / M tokens | $0.26 | — |
| Output $ / M tokens | $0.38 | — |
| Results tracked | 48 | 27 |
Sponsored placements are available on pages like this one. Advertise on Noometry
Category by category
Coding Too close to call
DeepSeek-V3.2-Exp: 46.4 (#61), Muse Spark: 45.6 (#65)
| Benchmark | DeepSeek-V3.2-Exp | Muse Spark |
|---|---|---|
| SciCode | 38.9% | 51.5% |
| LMArena Coding | 1454 | 1482 |
| SWE-bench Verified (bash only) | 70% | — |
| Aider Polyglot | 74.2% | — |
| LMArena WebDev | 1362 | — |
| SWE-bench Multilingual | 59% | — |
| WeirdML | 39.5% | — |
Agentic & Tool Use Not comparable
DeepSeek-V3.2-Exp: 37.9 (#31), Muse Spark: —
| Benchmark | DeepSeek-V3.2-Exp | Muse Spark |
|---|---|---|
| Terminal-Bench | 39.6% | — |
| Berkeley Function Calling Leaderboard | 56.7% | — |
| TheAgentCompany | 42.9% | — |
| Vending-Bench 2 | 1,034 | — |
Reasoning Muse Spark leads
DeepSeek-V3.2-Exp: 22.0 (#185), Muse Spark: 34.9 (#61)
| Benchmark | DeepSeek-V3.2-Exp | Muse Spark |
|---|---|---|
| CritPt | 2.9% | 11.3% |
| LMArena Hard Prompts | 1433 | 1475 |
| Epoch Capabilities Index | 146.28 | 152.09 |
| ARC-AGI-2 | 4% | — |
| Kagi LLM Benchmark | 52.2% | — |
| NYT Connections (extended) | 36.7% | — |
| ARC-AGI-1 | 57% | — |
| Chess Puzzles | 14% | — |
| Thematic Generalization | 65% | — |
| DTBench | 87.7% | — |
| LMCA | 29.1% | — |
Math Muse Spark leads
DeepSeek-V3.2-Exp: 40.8 (#75), Muse Spark: 46.8 (#57)
| Benchmark | DeepSeek-V3.2-Exp | Muse Spark |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 87.8% | 88.9% |
| ProofBench | 8% | 17% |
| LMArena Math | 1435 | 1455 |
| FrontierMath (Feb 2025 set) | 22.1% | 39% |
| FrontierMath Tier 4 (v1) | 2.1% | 14.6% |
| MathArena Final-Answer Competitions | 57.7% | — |
Knowledge Muse Spark leads
DeepSeek-V3.2-Exp: 51.6 (#61), Muse Spark: 65.7 (#12)
| Benchmark | DeepSeek-V3.2-Exp | Muse Spark |
|---|---|---|
| GPQA Diamond | 83.4% | 89.8% |
| LMArena Expert | 1436 | 1458 |
| Humanity's Last Exam | — | 40.6% |
| Vectara Hallucination Rate | 5.3% | — |
Multimodal Not comparable
DeepSeek-V3.2-Exp: —, Muse Spark: 42.7 (#23)
| Benchmark | DeepSeek-V3.2-Exp | Muse Spark |
|---|---|---|
| LMArena Vision | — | 1306 |
| LMArena Document | — | 1444 |
Multilingual Muse Spark leads
DeepSeek-V3.2-Exp: 52.4 (#85), Muse Spark: 56.3 (#23)
| Benchmark | DeepSeek-V3.2-Exp | Muse Spark |
|---|---|---|
| LMArena Non-English | 1409 | 1464 |
| LMArena Chinese | 1461 | 1507 |
| LMArena French | 1435 | 1497 |
| LMArena German | 1440 | 1497 |
| LMArena Korean | 1372 | 1457 |
| LMArena Russian | 1423 | 1467 |
| LMArena Spanish | 1441 | 1471 |
| LMArena Japanese | 1377 | — |
Instruction Following Muse Spark leads
DeepSeek-V3.2-Exp: 74.7 (#89), Muse Spark: 76.1 (#47)
| Benchmark | DeepSeek-V3.2-Exp | Muse Spark |
|---|---|---|
| LMArena Instruction Following | 1413 | 1443 |
Long Context DeepSeek-V3.2-Exp leads
DeepSeek-V3.2-Exp: 47.6 (#15), Muse Spark: 44.4 (#65)
| Benchmark | DeepSeek-V3.2-Exp | Muse Spark |
|---|---|---|
| LMArena Longer Query | 1428 | 1451 |
| Fiction.LiveBench | 83.3% | — |
| CL-bench | 13.2% | — |
| CL-bench Life | 9.5% | — |
Writing & Preference Muse Spark leads
DeepSeek-V3.2-Exp: 62.5 (#73), Muse Spark: 66.2 (#37)
| Benchmark | DeepSeek-V3.2-Exp | Muse Spark |
|---|---|---|
| LMArena Text | 1425 | 1474 |
| LMArena Creative Writing | 1403 | 1460 |
| LMArena Multi-Turn | 1427 | 1477 |
| EQ-Bench Creative Writing | 1515 | — |
Frequently asked questions
Is DeepSeek-V3.2-Exp better than Muse Spark?
Muse Spark is the stronger model overall, scoring 50.3 to 44.9 on the Noometry Index.
Is DeepSeek-V3.2-Exp or Muse Spark better for coding?
They score almost the same on coding (46.4 vs 45.6); test both on your own repository before choosing.
How many benchmarks do DeepSeek-V3.2-Exp and Muse Spark share?
24 benchmarks have published results for both models. DeepSeek-V3.2-Exp has 48 scored results on Noometry and Muse Spark has 27.