Model comparison
DeepSeek-V3.2-Exp vs Muse Spark 1.3
Muse Spark 1.3 is the stronger model overall, scoring 54.4 to 44.9 on the Noometry Index. DeepSeek-V3.2-Exp costs 6.9× less per token, which makes it the better buy when Muse Spark 1.3's lead doesn't matter for your workload.
Last verified . 25 shared benchmarks.
DeepSeek
MetaSummary
- They share 25 benchmarks with published results for both. DeepSeek-V3.2-Exp scores higher in 2 categories 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 72.4 to 40.8.
- The biggest single-benchmark swing is NYT Connections (extended): 36.7% for DeepSeek-V3.2-Exp and 85.1% for Muse Spark 1.3.
- DeepSeek-V3.2-Exp is cheaper at $0.26 / $0.38 per million input/output tokens, against $1.25 / $4.25 for Muse Spark 1.3.
- Muse Spark 1.3 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 1.3 | |
|---|---|---|
| Provider | DeepSeek | Meta |
| Noometry Index | 44.9 | 54.4 |
| Released | 2025-09-29 | 2026-09-02 |
| 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 | 48 | 34 |
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Category by category
Coding Muse Spark 1.3 leads
DeepSeek-V3.2-Exp: 46.4 (#61), Muse Spark 1.3: 56.1 (#18)
| Benchmark | DeepSeek-V3.2-Exp | Muse Spark 1.3 |
|---|---|---|
| LMArena WebDev | 1362 | 1655 |
| SciCode | 38.9% | 59.7% |
| LMArena Coding | 1454 | 1516 |
| SWE-bench Verified (bash only) | 70% | — |
| Aider Polyglot | 74.2% | — |
| CursorBench | — | 41.6% |
| SWE-bench Multilingual | 59% | — |
| WeirdML | 39.5% | — |
Agentic & Tool Use Too close to call
DeepSeek-V3.2-Exp: 37.9 (#31), Muse Spark 1.3: 38.6 (#28)
| Benchmark | DeepSeek-V3.2-Exp | Muse Spark 1.3 |
|---|---|---|
| Terminal-Bench | 39.6% | — |
| APEX-Agents | — | 57.8% |
| Berkeley Function Calling Leaderboard | 56.7% | — |
| TheAgentCompany | 42.9% | — |
| GDP.pdf | — | 27.6% |
| Vending-Bench 2 | 1,034 | — |
Reasoning Muse Spark 1.3 leads
DeepSeek-V3.2-Exp: 22.0 (#185), Muse Spark 1.3: 52.9 (#27)
| Benchmark | DeepSeek-V3.2-Exp | Muse Spark 1.3 |
|---|---|---|
| NYT Connections (extended) | 36.7% | 85.1% |
| CritPt | 2.9% | 24.9% |
| Chess Puzzles | 14% | 38% |
| LMArena Hard Prompts | 1433 | 1503 |
| DTBench | 87.7% | 96.5% |
| LMCA | 29.1% | 53.9% |
| Epoch Capabilities Index | 146.28 | 156.89 |
| ARC-AGI-2 | 4% | — |
| Kagi LLM Benchmark | 52.2% | — |
| ARC-AGI-1 | 57% | — |
| Thematic Generalization | 65% | — |
| Mystery Game Puzzles | — | 25% |
| Bench to the Future 3 | — | 0.14 |
Math Muse Spark 1.3 leads
DeepSeek-V3.2-Exp: 40.8 (#75), Muse Spark 1.3: 72.4 (#16)
| Benchmark | DeepSeek-V3.2-Exp | Muse Spark 1.3 |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 87.8% | 99.2% |
| LMArena Math | 1435 | 1497 |
| FrontierMath (Tiers 1-3) | — | 74.4% |
| FrontierMath Tier 4 | — | 46.3% |
| MathArena Final-Answer Competitions | 57.7% | — |
| ProofBench | 8% | — |
| FrontierMath (Feb 2025 set) | 22.1% | — |
| FrontierMath Tier 4 (v1) | 2.1% | — |
Knowledge DeepSeek-V3.2-Exp leads
DeepSeek-V3.2-Exp: 51.6 (#61), Muse Spark 1.3: 42.0 (#92)
| Benchmark | DeepSeek-V3.2-Exp | Muse Spark 1.3 |
|---|---|---|
| LMArena Expert | 1436 | 1516 |
| GPQA Diamond | 83.4% | — |
| Vectara Hallucination Rate | 5.3% | — |
Multimodal Not comparable
DeepSeek-V3.2-Exp: —, Muse Spark 1.3: 43.0 (#21)
| Benchmark | DeepSeek-V3.2-Exp | Muse Spark 1.3 |
|---|---|---|
| LMArena Vision | — | 1310 |
| LMArena Document | — | 1471 |
Multilingual Muse Spark 1.3 leads
DeepSeek-V3.2-Exp: 52.4 (#85), Muse Spark 1.3: 57.8 (#6)
| Benchmark | DeepSeek-V3.2-Exp | Muse Spark 1.3 |
|---|---|---|
| LMArena Non-English | 1409 | 1485 |
| LMArena Chinese | 1461 | 1527 |
| LMArena French | 1435 | 1534 |
| LMArena Korean | 1372 | 1513 |
| LMArena Russian | 1423 | 1491 |
| LMArena Spanish | 1441 | 1485 |
| LMArena German | 1440 | — |
| LMArena Japanese | 1377 | — |
Instruction Following Muse Spark 1.3 leads
DeepSeek-V3.2-Exp: 74.7 (#89), Muse Spark 1.3: 77.8 (#14)
| Benchmark | DeepSeek-V3.2-Exp | Muse Spark 1.3 |
|---|---|---|
| LMArena Instruction Following | 1413 | 1481 |
Long Context DeepSeek-V3.2-Exp leads
DeepSeek-V3.2-Exp: 47.6 (#15), Muse Spark 1.3: 45.7 (#29)
| Benchmark | DeepSeek-V3.2-Exp | Muse Spark 1.3 |
|---|---|---|
| LMArena Longer Query | 1428 | 1491 |
| Fiction.LiveBench | 83.3% | — |
| CL-bench | 13.2% | — |
| CL-bench Life | 9.5% | — |
Writing & Preference Muse Spark 1.3 leads
DeepSeek-V3.2-Exp: 62.5 (#73), Muse Spark 1.3: 73.7 (#10)
| Benchmark | DeepSeek-V3.2-Exp | Muse Spark 1.3 |
|---|---|---|
| LMArena Text | 1425 | 1490 |
| LMArena Creative Writing | 1403 | 1450 |
| EQ-Bench Creative Writing | 1515 | 1906 |
| LMArena Multi-Turn | 1427 | 1486 |
Frequently asked questions
Is DeepSeek-V3.2-Exp better than Muse Spark 1.3?
Muse Spark 1.3 is the stronger model overall, scoring 54.4 to 44.9 on the Noometry Index. DeepSeek-V3.2-Exp costs 6.9× 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-Exp or Muse Spark 1.3?
DeepSeek-V3.2-Exp is cheaper. It lists at $0.26 per million input tokens and $0.38 per million output tokens; Muse Spark 1.3 lists at $1.25 and $4.25.
Is DeepSeek-V3.2-Exp or Muse Spark 1.3 better for coding?
Muse Spark 1.3 scores higher on coding benchmarks: 56.1 versus 46.4 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-V3.2-Exp and Muse Spark 1.3 share?
25 benchmarks have published results for both models. DeepSeek-V3.2-Exp has 48 scored results on Noometry and Muse Spark 1.3 has 34.