Model comparison
Llama 3.1-70B vs Muse Spark 1.2
Muse Spark 1.2 is the stronger model overall, scoring 49.8 to 29.4 on the Noometry Index. Llama 3.1-70B costs 5.0× less per token, which makes it the better buy when Muse Spark 1.2's lead doesn't matter for your workload.
Last verified . 16 shared benchmarks.
MetaSummary
- They share 16 benchmarks with published results for both. Llama 3.1-70B scores higher in 0 categories and Muse Spark 1.2 in 9 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in writing & preference, where Muse Spark 1.2 leads 72.6 to 35.6.
- The biggest single-benchmark swing is WeirdML: 9% for Llama 3.1-70B and 60.3% for Muse Spark 1.2.
- Llama 3.1-70B is cheaper at $0.40 / $0.40 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 128K.
- Llama 3.1-70B has downloadable open weights; the other is API-only.
Side by side
| Llama 3.1-70B | Muse Spark 1.2 | |
|---|---|---|
| Provider | Meta | Meta |
| Noometry Index | 29.4 | 49.8 |
| Released | 2024-07-23 | 2026-08-05 |
| Weights | Open | Proprietary |
| Context window | 128K | 1.05M |
| Max output | 4K | 131K |
| Input $ / M tokens | $0.40 | $1.25 |
| Output $ / M tokens | $0.40 | $4.25 |
| Results tracked | 35 | 28 |
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Category by category
Coding Muse Spark 1.2 leads
Llama 3.1-70B: 29.8 (#292), Muse Spark 1.2: 48.6 (#49)
| Benchmark | Llama 3.1-70B | Muse Spark 1.2 |
|---|---|---|
| WeirdML | 9% | 60.3% |
| LMArena Coding | 1260 | 1497 |
| DeepSWE | — | 54.9% |
| LMArena WebDev | — | 1532 |
| FrontierSWE | — | 12% |
| SciCode | — | 56.4% |
| BigCodeBench Instruct | 46.1% | — |
| BigCodeBench Complete | 54.8% | — |
Agentic & Tool Use Muse Spark 1.2 leads
Llama 3.1-70B: 24.9 (#104), Muse Spark 1.2: 29.5 (#77)
| Benchmark | Llama 3.1-70B | Muse Spark 1.2 |
|---|---|---|
| APEX-Agents | — | 36.4% |
| TheAgentCompany | 6.9% | — |
| BALROG | 27.9% | — |
| GDP.pdf | — | 16% |
Reasoning Muse Spark 1.2 leads
Llama 3.1-70B: 20.8 (#215), Muse Spark 1.2: 50.4 (#32)
| Benchmark | Llama 3.1-70B | Muse Spark 1.2 |
|---|---|---|
| LMArena Hard Prompts | 1241 | 1490 |
| DTBench | 60% | 94.7% |
| LMCA | 14.8% | 48.4% |
| Epoch Capabilities Index | 125.91 | 155.18 |
| SimpleBench | — | 74.5% |
| NYT Connections (extended) | — | 79.2% |
| CritPt | — | 17.7% |
Math Muse Spark 1.2 leads
Llama 3.1-70B: 12.7 (#298), Muse Spark 1.2: 43.8 (#68)
| Benchmark | Llama 3.1-70B | Muse Spark 1.2 |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 3.6% | — |
| ProofBench | — | 43% |
| Omni-MATH | 21% | — |
| LMArena Math | 1252 | — |
| MATH Level 5 | 36.7% | — |
Knowledge Muse Spark 1.2 leads
Llama 3.1-70B: 24.1 (#264), Muse Spark 1.2: 53.9 (#49)
| Benchmark | Llama 3.1-70B | Muse Spark 1.2 |
|---|---|---|
| LMArena Expert | 1209 | 1485 |
| GPQA Diamond | 44.2% | — |
| SimpleQA Verified | — | 60.3% |
| MMLU-Pro | 65.3% | — |
| GPQA (HELM) | 42.6% | — |
| MMLU | 80.1% | — |
Multimodal Not comparable
Llama 3.1-70B: —, Muse Spark 1.2: 42.6 (#24)
| Benchmark | Llama 3.1-70B | Muse Spark 1.2 |
|---|---|---|
| LMArena Vision | — | 1304 |
Multilingual Muse Spark 1.2 leads
Llama 3.1-70B: 38.9 (#220), Muse Spark 1.2: 57.4 (#10)
| Benchmark | Llama 3.1-70B | Muse Spark 1.2 |
|---|---|---|
| LMArena Non-English | 1219 | 1479 |
| LMArena Chinese | 1215 | 1519 |
| LMArena Russian | 1234 | 1494 |
| LMArena French | 1260 | — |
| LMArena German | 1222 | — |
| LMArena Japanese | 1132 | — |
| LMArena Korean | 1140 | — |
| LMArena Spanish | 1253 | — |
Instruction Following Muse Spark 1.2 leads
Llama 3.1-70B: 65.5 (#218), Muse Spark 1.2: 77.1 (#30)
| Benchmark | Llama 3.1-70B | Muse Spark 1.2 |
|---|---|---|
| LMArena Instruction Following | 1231 | 1466 |
| IFEval | 82.1% | — |
Long Context Muse Spark 1.2 leads
Llama 3.1-70B: 37.6 (#208), Muse Spark 1.2: 45.4 (#35)
| Benchmark | Llama 3.1-70B | Muse Spark 1.2 |
|---|---|---|
| LMArena Longer Query | 1241 | 1483 |
Writing & Preference Muse Spark 1.2 leads
Llama 3.1-70B: 35.6 (#262), Muse Spark 1.2: 72.6 (#14)
| Benchmark | Llama 3.1-70B | Muse Spark 1.2 |
|---|---|---|
| LMArena Text | 1261 | 1486 |
| LMArena Creative Writing | 1232 | 1444 |
| EQ-Bench Creative Writing | 784 | 1840 |
| LMArena Multi-Turn | 1256 | 1504 |
| WildBench | 75.8% | — |
Frequently asked questions
Is Llama 3.1-70B better than Muse Spark 1.2?
Muse Spark 1.2 is the stronger model overall, scoring 49.8 to 29.4 on the Noometry Index. Llama 3.1-70B costs 5.0× 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, Llama 3.1-70B or Muse Spark 1.2?
Llama 3.1-70B is cheaper. It lists at $0.40 per million input tokens and $0.40 per million output tokens; Muse Spark 1.2 lists at $1.25 and $4.25.
Is Llama 3.1-70B or Muse Spark 1.2 better for coding?
Muse Spark 1.2 scores higher on coding benchmarks: 48.6 versus 29.8 in the Noometry coding category.
Which has the bigger context window?
Muse Spark 1.2 does, with 1.05M tokens against 128K.
How many benchmarks do Llama 3.1-70B and Muse Spark 1.2 share?
16 benchmarks have published results for both models. Llama 3.1-70B has 35 scored results on Noometry and Muse Spark 1.2 has 28.