Model comparison
DeepSeek-V3 vs Muse Spark 1.3
Muse Spark 1.3 is the stronger model overall, scoring 54.4 to 39.2 on the Noometry Index. DeepSeek-V3 costs 4.9× less per token, which makes it the better buy when Muse Spark 1.3's lead doesn't matter for your workload.
Last verified . 22 shared benchmarks.
DeepSeek
MetaSummary
- They share 22 benchmarks with published results for both. DeepSeek-V3 scores higher in 0 categories and Muse Spark 1.3 in 8 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in math, where Muse Spark 1.3 leads 72.4 to 30.4.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 37.8% for DeepSeek-V3 and 99.2% for Muse Spark 1.3.
- DeepSeek-V3 is cheaper at $0.24 / $0.90 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 has downloadable open weights; the other is API-only.
Side by side
| DeepSeek-V3 | Muse Spark 1.3 | |
|---|---|---|
| Provider | DeepSeek | Meta |
| Noometry Index | 39.2 | 54.4 |
| Released | 2024-12-26 | 2026-09-02 |
| Weights | Open | Proprietary |
| Context window | 164K | 1.05M |
| Max output | 164K | 131K |
| Input $ / M tokens | $0.24 | $1.25 |
| Output $ / M tokens | $0.90 | $4.25 |
| Results tracked | 60 | 34 |
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Category by category
Coding Muse Spark 1.3 leads
DeepSeek-V3: 41.9 (#99), Muse Spark 1.3: 56.1 (#18)
| Benchmark | DeepSeek-V3 | Muse Spark 1.3 |
|---|---|---|
| SciCode | 35.8% | 59.7% |
| LMArena Coding | 1368 | 1516 |
| Aider Polyglot | 55.1% | — |
| CursorBench | — | 41.6% |
| LMArena WebDev | — | 1655 |
| WeirdML | 36.1% | — |
| BigCodeBench Instruct | 50% | — |
| LiveBench Coding | 70.9% | — |
| BigCodeBench Complete | 62.2% | — |
| HumanEval+ | 86.6% | — |
| MBPP+ | 73% | — |
Agentic & Tool Use Not comparable
DeepSeek-V3: —, Muse Spark 1.3: 38.6 (#28)
| Benchmark | DeepSeek-V3 | Muse Spark 1.3 |
|---|---|---|
| APEX-Agents | — | 57.8% |
| GDP.pdf | — | 27.6% |
| METR Time Horizons | 49.6% | — |
Reasoning Muse Spark 1.3 leads
DeepSeek-V3: 20.0 (#226), Muse Spark 1.3: 52.9 (#27)
| Benchmark | DeepSeek-V3 | Muse Spark 1.3 |
|---|---|---|
| CritPt | 0% | 24.9% |
| LMArena Hard Prompts | 1365 | 1503 |
| DTBench | 64.8% | 96.5% |
| LMCA | 15.5% | 53.9% |
| Epoch Capabilities Index | 135.95 | 156.89 |
| SimpleBench | 27.2% | — |
| Kagi LLM Benchmark | 52.3% | — |
| NYT Connections (extended) | — | 85.1% |
| Chess Puzzles | — | 38% |
| LiveBench Reasoning | 65.8% | — |
| Mystery Game Puzzles | — | 25% |
| LiveBench Data Analysis | 60.9% | — |
| Bench to the Future 3 | — | 0.14 |
| BIG-Bench Hard | 87.5% | — |
| ForecastBench | 59.1 | — |
| HellaSwag | 88.9% | — |
| LiveBench | 66.9% | — |
| PIQA | 84.7% | — |
| WinoGrande | 85.2% | — |
Math Muse Spark 1.3 leads
DeepSeek-V3: 30.4 (#208), Muse Spark 1.3: 72.4 (#16)
| Benchmark | DeepSeek-V3 | Muse Spark 1.3 |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 37.8% | 99.2% |
| LMArena Math | 1373 | 1497 |
| FrontierMath (Tiers 1-3) | — | 74.4% |
| FrontierMath Tier 4 | — | 46.3% |
| Omni-MATH | 40.3% | — |
| LiveBench Math | 73.5% | — |
| MATH Level 5 | 75.5% | — |
| FrontierMath (Feb 2025 set) | 1.7% | — |
Knowledge Muse Spark 1.3 leads
DeepSeek-V3: 37.5 (#147), Muse Spark 1.3: 42.0 (#92)
| Benchmark | DeepSeek-V3 | Muse Spark 1.3 |
|---|---|---|
| LMArena Expert | 1352 | 1516 |
| GPQA Diamond | 67.6% | — |
| MMLU-Pro | 72.3% | — |
| Confabulations | 26.1% | — |
| Vectara Hallucination Rate | 6.1% | — |
| GPQA (HELM) | 53.8% | — |
| ARC (AI2) Challenge | 95.3% | — |
| MMLU | 87.2% | — |
| TriviaQA | 82.9% | — |
Multimodal Not comparable
DeepSeek-V3: —, Muse Spark 1.3: 43.0 (#21)
| Benchmark | DeepSeek-V3 | Muse Spark 1.3 |
|---|---|---|
| LMArena Vision | — | 1310 |
| LMArena Document | — | 1471 |
Multilingual Muse Spark 1.3 leads
DeepSeek-V3: 48.8 (#138), Muse Spark 1.3: 57.8 (#6)
| Benchmark | DeepSeek-V3 | Muse Spark 1.3 |
|---|---|---|
| LMArena Non-English | 1358 | 1485 |
| LMArena Chinese | 1390 | 1527 |
| LMArena French | 1386 | 1534 |
| LMArena Korean | 1321 | 1513 |
| LMArena Russian | 1372 | 1491 |
| LMArena Spanish | 1358 | 1485 |
| LMArena German | 1374 | — |
| LMArena Japanese | 1333 | — |
Instruction Following Muse Spark 1.3 leads
DeepSeek-V3: 73.0 (#125), Muse Spark 1.3: 77.8 (#14)
| Benchmark | DeepSeek-V3 | Muse Spark 1.3 |
|---|---|---|
| LMArena Instruction Following | 1345 | 1481 |
| LiveBench Instruction Following | 81.5% | — |
| IFEval | 83.2% | — |
Long Context Muse Spark 1.3 leads
DeepSeek-V3: 34.0 (#248), Muse Spark 1.3: 45.7 (#29)
| Benchmark | DeepSeek-V3 | Muse Spark 1.3 |
|---|---|---|
| LMArena Longer Query | 1353 | 1491 |
| Fiction.LiveBench | 50% | — |
Writing & Preference Muse Spark 1.3 leads
DeepSeek-V3: 57.6 (#124), Muse Spark 1.3: 73.7 (#10)
| Benchmark | DeepSeek-V3 | Muse Spark 1.3 |
|---|---|---|
| LMArena Text | 1375 | 1490 |
| LMArena Creative Writing | 1364 | 1450 |
| EQ-Bench Creative Writing | 1472 | 1906 |
| LMArena Multi-Turn | 1389 | 1486 |
| Short-Story Creative Writing | 77% | — |
| WildBench | 83% | — |
| LiveBench Language | 49.1% | — |
Frequently asked questions
Is DeepSeek-V3 better than Muse Spark 1.3?
Muse Spark 1.3 is the stronger model overall, scoring 54.4 to 39.2 on the Noometry Index. DeepSeek-V3 costs 4.9× 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 or Muse Spark 1.3?
DeepSeek-V3 is cheaper. It lists at $0.24 per million input tokens and $0.90 per million output tokens; Muse Spark 1.3 lists at $1.25 and $4.25.
Is DeepSeek-V3 or Muse Spark 1.3 better for coding?
Muse Spark 1.3 scores higher on coding benchmarks: 56.1 versus 41.9 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 and Muse Spark 1.3 share?
22 benchmarks have published results for both models. DeepSeek-V3 has 60 scored results on Noometry and Muse Spark 1.3 has 34.