Model comparison
DeepSeek V4 Pro vs Muse Spark 1.3
DeepSeek V4 Pro and Muse Spark 1.3 score almost the same on the Noometry Index (54.3 vs 54.8), so choose on price, context window or the category you care about most.
Last verified . 32 shared benchmarks.
DeepSeek
MetaSummary
- They share 32 benchmarks with published results for both. DeepSeek V4 Pro 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 knowledge, where DeepSeek V4 Pro leads 59.5 to 42.6.
- The biggest single-benchmark swing is FrontierMath Tier 4: 26.8% for DeepSeek V4 Pro and 46.3% for Muse Spark 1.3.
- DeepSeek V4 Pro is cheaper at $0.66 / $1.98 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 1M.
- DeepSeek V4 Pro has downloadable open weights; the other is API-only.
Side by side
| DeepSeek V4 Pro | Muse Spark 1.3 | |
|---|---|---|
| Provider | DeepSeek | Meta |
| Noometry Index | 54.3 | 54.8 |
| Released | 2026-04-24 | 2026-09-02 |
| Weights | Open | Proprietary |
| Context window | 1M | 1.05M |
| Max output | 393K | 131K |
| Input $ / M tokens | $0.66 | $1.25 |
| Output $ / M tokens | $1.98 | $4.25 |
| Results tracked | 48 | 37 |
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Category by category
Coding Muse Spark 1.3 leads
DeepSeek V4 Pro: 52.4 (#34), Muse Spark 1.3: 56.6 (#21)
| Benchmark | DeepSeek V4 Pro | Muse Spark 1.3 |
|---|---|---|
| LMArena WebDev | 1582 | 1657 |
| SciCode | 51% | 59.7% |
| LMArena Coding | 1470 | 1514 |
| SWE-bench Verified | 77.6% | — |
| FrontierCode | 28.6% | — |
| CursorBench | — | 41.6% |
| WeirdML | 66.2% | — |
| ALE-Bench | 1,403 | — |
Agentic & Tool Use Muse Spark 1.3 leads
DeepSeek V4 Pro: 32.8 (#58), Muse Spark 1.3: 38.6 (#30)
| Benchmark | DeepSeek V4 Pro | Muse Spark 1.3 |
|---|---|---|
| APEX-Agents | 47.3% | 57.8% |
| GDP.pdf | — | 27.6% |
| Vending-Bench 2 | 3,285 | — |
Reasoning DeepSeek V4 Pro leads
DeepSeek V4 Pro: 56.5 (#24), Muse Spark 1.3: 54.0 (#27)
| Benchmark | DeepSeek V4 Pro | Muse Spark 1.3 |
|---|---|---|
| NYT Connections (extended) | 91.3% | 85.1% |
| CritPt | 18% | 26% |
| Chess Puzzles | 47% | 38% |
| LMArena Hard Prompts | 1461 | 1503 |
| Mystery Game Puzzles | 43% | 25% |
| DTBench | 93.9% | 96.5% |
| LMCA | 45.5% | 53.9% |
| Epoch Capabilities Index | 155.31 | 156.75 |
| ARC-AGI-2 | 61.3% | — |
| Kagi LLM Benchmark | 53.5% | — |
| ARC-AGI-1 | 90.5% | — |
| Surface Evolver Bench | 40% | — |
| Bench to the Future 3 | — | 0.14 |
| ForecastBench | 56.1 | — |
Math Muse Spark 1.3 leads
DeepSeek V4 Pro: 64.8 (#30), Muse Spark 1.3: 73.1 (#21)
| Benchmark | DeepSeek V4 Pro | Muse Spark 1.3 |
|---|---|---|
| FrontierMath (Tiers 1-3) | 64.6% | 74.4% |
| FrontierMath Tier 4 | 26.8% | 46.3% |
| OTIS Mock AIME 2024-2025 | 98.6% | 99.2% |
| ProofBench | 50% | 58% |
| LMArena Math | 1455 | 1494 |
| MathArena Final-Answer Competitions | 76.6% | — |
Knowledge DeepSeek V4 Pro leads
DeepSeek V4 Pro: 59.5 (#31), Muse Spark 1.3: 42.6 (#95)
| Benchmark | DeepSeek V4 Pro | Muse Spark 1.3 |
|---|---|---|
| LMArena Expert | 1464 | 1516 |
| GPQA Diamond | 91.7% | — |
| SimpleQA Verified | 52.9% | — |
| Vectara Hallucination Rate | 8.6% | — |
Multimodal Not comparable
DeepSeek V4 Pro: —, Muse Spark 1.3: 43.7 (#22)
| Benchmark | DeepSeek V4 Pro | Muse Spark 1.3 |
|---|---|---|
| LMArena Vision | — | 1309 |
| LMArena Document | — | 1471 |
Multilingual Muse Spark 1.3 leads
DeepSeek V4 Pro: 54.4 (#45), Muse Spark 1.3: 57.4 (#8)
| Benchmark | DeepSeek V4 Pro | Muse Spark 1.3 |
|---|---|---|
| LMArena Non-English | 1439 | 1481 |
| LMArena Chinese | 1486 | 1529 |
| LMArena French | 1472 | 1524 |
| LMArena German | 1458 | 1515 |
| LMArena Japanese | 1445 | 1474 |
| LMArena Korean | 1447 | 1501 |
| LMArena Russian | 1453 | 1490 |
| LMArena Spanish | 1458 | 1490 |
Instruction Following Muse Spark 1.3 leads
DeepSeek V4 Pro: 76.1 (#47), Muse Spark 1.3: 77.5 (#22)
| Benchmark | DeepSeek V4 Pro | Muse Spark 1.3 |
|---|---|---|
| LMArena Instruction Following | 1448 | 1477 |
Long Context Too close to call
DeepSeek V4 Pro: 45.0 (#51), Muse Spark 1.3: 45.6 (#32)
| Benchmark | DeepSeek V4 Pro | Muse Spark 1.3 |
|---|---|---|
| LMArena Longer Query | 1458 | 1488 |
| CL-bench Life | 13.5% | — |
Writing & Preference Muse Spark 1.3 leads
DeepSeek V4 Pro: 65.5 (#46), Muse Spark 1.3: 73.6 (#9)
| Benchmark | DeepSeek V4 Pro | Muse Spark 1.3 |
|---|---|---|
| LMArena Text | 1451 | 1490 |
| LMArena Creative Writing | 1446 | 1455 |
| EQ-Bench Creative Writing | 1553 | 1906 |
| LMArena Multi-Turn | 1467 | 1482 |
| EQ-Bench 4 | 1166 | — |
Frequently asked questions
Is DeepSeek V4 Pro better than Muse Spark 1.3?
DeepSeek V4 Pro and Muse Spark 1.3 score almost the same on the Noometry Index (54.3 vs 54.8), so choose on price, context window or the category you care about most.
Which is cheaper, DeepSeek V4 Pro or Muse Spark 1.3?
DeepSeek V4 Pro is cheaper. It lists at $0.66 per million input tokens and $1.98 per million output tokens; Muse Spark 1.3 lists at $1.25 and $4.25.
Is DeepSeek V4 Pro or Muse Spark 1.3 better for coding?
Muse Spark 1.3 scores higher on coding benchmarks: 56.6 versus 52.4 in the Noometry coding category.
Which has the bigger context window?
Muse Spark 1.3 does, with 1.05M tokens against 1M.
How many benchmarks do DeepSeek V4 Pro and Muse Spark 1.3 share?
32 benchmarks have published results for both models. DeepSeek V4 Pro has 48 scored results on Noometry and Muse Spark 1.3 has 37.