Model comparison
DeepSeek-R1 vs Muse Spark 1.2
Muse Spark 1.2 is the stronger model overall, scoring 50.3 to 42.3 on the Noometry Index. DeepSeek-R1 costs 2.2× less per token, which makes it the better buy when Muse Spark 1.2's lead doesn't matter for your workload.
Last verified . 20 shared benchmarks.
DeepSeek
MetaSummary
- They share 20 benchmarks with published results for both. DeepSeek-R1 scores higher in 2 categories and Muse Spark 1.2 in 7 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where Muse Spark 1.2 leads 51.3 to 18.6.
- The biggest single-benchmark swing is SimpleBench: 40.8% for DeepSeek-R1 and 74.5% for Muse Spark 1.2.
- DeepSeek-R1 is cheaper at $0.50 / $2.15 per million input/output tokens, against $1.25 / $4.25 for Muse Spark 1.2.
- Muse Spark 1.2 accepts more context: 1.05M tokens versus 164K.
Side by side
| DeepSeek-R1 | Muse Spark 1.2 | |
|---|---|---|
| Provider | DeepSeek | Meta |
| Noometry Index | 42.3 | 50.3 |
| Released | 2025-01-20 | 2026-08-05 |
| Weights | Proprietary | Proprietary |
| Context window | 164K | 1.05M |
| Max output | 64K | 131K |
| Input $ / M tokens | $0.50 | $1.25 |
| Output $ / M tokens | $2.15 | $4.25 |
| Results tracked | 52 | 31 |
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Category by category
Coding Muse Spark 1.2 leads
DeepSeek-R1: 46.3 (#68), Muse Spark 1.2: 49.2 (#51)
| Benchmark | DeepSeek-R1 | Muse Spark 1.2 |
|---|---|---|
| SciCode | 35.7% | 56.4% |
| WeirdML | 41.6% | 60.3% |
| LMArena Coding | 1427 | 1495 |
| DeepSWE | — | 54.9% |
| Aider Polyglot | 71.4% | — |
| LMArena WebDev | — | 1533 |
| FrontierSWE | — | 12% |
| LiveBench Coding | 66.7% | — |
| ALE-Bench | 804.12 | — |
| AlgoTune | 1.7 | — |
Agentic & Tool Use DeepSeek-R1 leads
DeepSeek-R1: 30.7 (#75), Muse Spark 1.2: 29.4 (#87)
| Benchmark | DeepSeek-R1 | Muse Spark 1.2 |
|---|---|---|
| APEX-Agents | — | 36.4% |
| DeepResearch Bench | 35.1% | — |
| BALROG | 34.9% | — |
| GDP.pdf | — | 16% |
| METR Time Horizons | 53.8% | — |
Reasoning Muse Spark 1.2 leads
DeepSeek-R1: 18.6 (#278), Muse Spark 1.2: 51.3 (#34)
| Benchmark | DeepSeek-R1 | Muse Spark 1.2 |
|---|---|---|
| SimpleBench | 40.8% | 74.5% |
| CritPt | 1.1% | 17.7% |
| LMArena Hard Prompts | 1416 | 1486 |
| Epoch Capabilities Index | 141.29 | 154.87 |
| ARC-AGI-2 | 1.3% | — |
| Kagi LLM Benchmark | 69.4% | — |
| NYT Connections (extended) | — | 79.2% |
| ARC-AGI-1 | 21.2% | — |
| LiveBench Reasoning | 83.2% | — |
| DTBench | — | 94.7% |
| LiveBench Data Analysis | 69.8% | — |
| LMCA | — | 48.4% |
| ForecastBench | 60 | — |
| LiveBench | 71.6% | — |
Math Muse Spark 1.2 leads
DeepSeek-R1: 43.8 (#79), Muse Spark 1.2: 46.4 (#70)
| Benchmark | DeepSeek-R1 | Muse Spark 1.2 |
|---|---|---|
| LMArena Math | 1400 | 1471 |
| OTIS Mock AIME 2024-2025 | 66.4% | — |
| ProofBench | — | 43% |
| Omni-MATH | 42.4% | — |
| LiveBench Math | 80.7% | — |
| MATH Level 5 | 96.6% | — |
Knowledge Muse Spark 1.2 leads
DeepSeek-R1: 44.5 (#87), Muse Spark 1.2: 54.1 (#53)
| Benchmark | DeepSeek-R1 | Muse Spark 1.2 |
|---|---|---|
| LMArena Expert | 1394 | 1480 |
| GPQA Diamond | 76.3% | — |
| SimpleQA Verified | — | 60.3% |
| MMLU-Pro | 79.3% | — |
| Confabulations | 12.7% | — |
| Vectara Hallucination Rate | 11.3% | — |
| GPQA (HELM) | 66.6% | — |
Multimodal Not comparable
DeepSeek-R1: —, Muse Spark 1.2: 43.4 (#25)
| Benchmark | DeepSeek-R1 | Muse Spark 1.2 |
|---|---|---|
| LMArena Vision | — | 1305 |
Multilingual Muse Spark 1.2 leads
DeepSeek-R1: 52.4 (#85), Muse Spark 1.2: 57.1 (#11)
| Benchmark | DeepSeek-R1 | Muse Spark 1.2 |
|---|---|---|
| LMArena Non-English | 1412 | 1478 |
| LMArena Chinese | 1442 | 1511 |
| LMArena French | 1417 | 1513 |
| LMArena Russian | 1423 | 1487 |
| LMArena Spanish | 1411 | 1498 |
| LMArena German | 1404 | — |
| LMArena Japanese | 1391 | — |
| LMArena Korean | 1360 | — |
Instruction Following Muse Spark 1.2 leads
DeepSeek-R1: 72.0 (#143), Muse Spark 1.2: 76.7 (#36)
| Benchmark | DeepSeek-R1 | Muse Spark 1.2 |
|---|---|---|
| LMArena Instruction Following | 1382 | 1461 |
| LiveBench Instruction Following | 80.5% | — |
| IFEval | 78.4% | — |
Long Context Too close to call
DeepSeek-R1: 45.4 (#36), Muse Spark 1.2: 45.2 (#48)
| Benchmark | DeepSeek-R1 | Muse Spark 1.2 |
|---|---|---|
| LMArena Longer Query | 1391 | 1475 |
| Fiction.LiveBench | 75% | — |
Writing & Preference Muse Spark 1.2 leads
DeepSeek-R1: 61.4 (#88), Muse Spark 1.2: 72.3 (#14)
| Benchmark | DeepSeek-R1 | Muse Spark 1.2 |
|---|---|---|
| LMArena Text | 1428 | 1482 |
| LMArena Creative Writing | 1405 | 1449 |
| EQ-Bench Creative Writing | 1500 | 1840 |
| LMArena Multi-Turn | 1405 | 1494 |
| Short-Story Creative Writing | 83% | — |
| WildBench | 82.8% | — |
| LiveBench Language | 48.5% | — |
Frequently asked questions
Is DeepSeek-R1 better than Muse Spark 1.2?
Muse Spark 1.2 is the stronger model overall, scoring 50.3 to 42.3 on the Noometry Index. DeepSeek-R1 costs 2.2× less per token, which makes it the better buy when Muse Spark 1.2's lead doesn't matter for your workload.
Which is cheaper, DeepSeek-R1 or Muse Spark 1.2?
DeepSeek-R1 is cheaper. It lists at $0.50 per million input tokens and $2.15 per million output tokens; Muse Spark 1.2 lists at $1.25 and $4.25.
Is DeepSeek-R1 or Muse Spark 1.2 better for coding?
Muse Spark 1.2 scores higher on coding benchmarks: 49.2 versus 46.3 in the Noometry coding category.
Which has the bigger context window?
Muse Spark 1.2 does, with 1.05M tokens against 164K.
How many benchmarks do DeepSeek-R1 and Muse Spark 1.2 share?
20 benchmarks have published results for both models. DeepSeek-R1 has 52 scored results on Noometry and Muse Spark 1.2 has 31.