Model comparison
Llama-3.3-70B-Instruct vs Muse Spark 1.2
Muse Spark 1.2 is the stronger model overall, scoring 49.8 to 30.3 on the Noometry Index. Llama-3.3-70B-Instruct costs 13× less per token, which makes it the better buy when Muse Spark 1.2's lead doesn't matter for your workload.
Last verified . 18 shared benchmarks.
MetaSummary
- They share 18 benchmarks with published results for both. Llama-3.3-70B-Instruct 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 reasoning, where Muse Spark 1.2 leads 50.4 to 13.7.
- The biggest single-benchmark swing is SimpleBench: 19.9% for Llama-3.3-70B-Instruct and 74.5% for Muse Spark 1.2.
- Llama-3.3-70B-Instruct is cheaper at $0.10 / $0.32 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-3.3-70B-Instruct has downloadable open weights; the other is API-only.
Side by side
| Llama-3.3-70B-Instruct | Muse Spark 1.2 | |
|---|---|---|
| Provider | Meta | Meta |
| Noometry Index | 30.3 | 49.8 |
| Released | 2024-12-06 | 2026-08-05 |
| Weights | Open | Proprietary |
| Context window | 128K | 1.05M |
| Max output | 4K | 131K |
| Input $ / M tokens | $0.10 | $1.25 |
| Output $ / M tokens | $0.32 | $4.25 |
| Results tracked | 43 | 28 |
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Category by category
Coding Muse Spark 1.2 leads
Llama-3.3-70B-Instruct: 30.6 (#284), Muse Spark 1.2: 48.6 (#49)
| Benchmark | Llama-3.3-70B-Instruct | Muse Spark 1.2 |
|---|---|---|
| SciCode | 26% | 56.4% |
| WeirdML | 14.4% | 60.3% |
| LMArena Coding | 1268 | 1497 |
| DeepSWE | — | 54.9% |
| LMArena WebDev | — | 1532 |
| FrontierSWE | — | 12% |
| BigCodeBench Instruct | 46.9% | — |
| LiveBench Coding | 36.6% | — |
| BigCodeBench Complete | 57.5% | — |
Agentic & Tool Use Muse Spark 1.2 leads
Llama-3.3-70B-Instruct: 25.6 (#100), Muse Spark 1.2: 29.5 (#77)
| Benchmark | Llama-3.3-70B-Instruct | Muse Spark 1.2 |
|---|---|---|
| APEX-Agents | — | 36.4% |
| Berkeley Function Calling Leaderboard | 31.9% | — |
| BALROG | 23% | — |
| GDP.pdf | — | 16% |
Reasoning Muse Spark 1.2 leads
Llama-3.3-70B-Instruct: 13.7 (#325), Muse Spark 1.2: 50.4 (#32)
| Benchmark | Llama-3.3-70B-Instruct | Muse Spark 1.2 |
|---|---|---|
| SimpleBench | 19.9% | 74.5% |
| CritPt | 0% | 17.7% |
| LMArena Hard Prompts | 1257 | 1490 |
| DTBench | 59.5% | 94.7% |
| LMCA | 17.5% | 48.4% |
| Epoch Capabilities Index | 127.32 | 155.18 |
| NYT Connections (extended) | — | 79.2% |
| LiveBench Reasoning | 50.8% | — |
| LiveBench Data Analysis | 49.5% | — |
| ForecastBench | 58.6 | — |
| LiveBench | 50.2% | — |
Math Muse Spark 1.2 leads
Llama-3.3-70B-Instruct: 14.3 (#292), Muse Spark 1.2: 43.8 (#68)
| Benchmark | Llama-3.3-70B-Instruct | Muse Spark 1.2 |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 5.1% | — |
| ProofBench | — | 43% |
| LiveBench Math | 42.2% | — |
| LMArena Math | 1268 | — |
| MATH Level 5 | 41.6% | — |
Knowledge Muse Spark 1.2 leads
Llama-3.3-70B-Instruct: 30.5 (#220), Muse Spark 1.2: 53.9 (#49)
| Benchmark | Llama-3.3-70B-Instruct | Muse Spark 1.2 |
|---|---|---|
| LMArena Expert | 1225 | 1485 |
| GPQA Diamond | 47.4% | — |
| SimpleQA Verified | — | 60.3% |
| Confabulations | 22.8% | — |
| Vectara Hallucination Rate | 4.1% | — |
| MMLU | 86.3% | — |
Multimodal Not comparable
Llama-3.3-70B-Instruct: —, Muse Spark 1.2: 42.6 (#24)
| Benchmark | Llama-3.3-70B-Instruct | Muse Spark 1.2 |
|---|---|---|
| LMArena Vision | — | 1304 |
Multilingual Muse Spark 1.2 leads
Llama-3.3-70B-Instruct: 40.1 (#215), Muse Spark 1.2: 57.4 (#10)
| Benchmark | Llama-3.3-70B-Instruct | Muse Spark 1.2 |
|---|---|---|
| LMArena Non-English | 1236 | 1479 |
| LMArena Chinese | 1217 | 1519 |
| LMArena Russian | 1252 | 1494 |
| LMArena French | 1280 | — |
| LMArena German | 1251 | — |
| LMArena Japanese | 1149 | — |
| LMArena Korean | 1142 | — |
| LMArena Spanish | 1269 | — |
Instruction Following Muse Spark 1.2 leads
Llama-3.3-70B-Instruct: 71.3 (#152), Muse Spark 1.2: 77.1 (#30)
| Benchmark | Llama-3.3-70B-Instruct | Muse Spark 1.2 |
|---|---|---|
| LMArena Instruction Following | 1242 | 1466 |
| LiveBench Instruction Following | 82.7% | — |
Long Context Muse Spark 1.2 leads
Llama-3.3-70B-Instruct: 26.4 (#290), Muse Spark 1.2: 45.4 (#35)
| Benchmark | Llama-3.3-70B-Instruct | Muse Spark 1.2 |
|---|---|---|
| LMArena Longer Query | 1256 | 1483 |
| Fiction.LiveBench | 33.3% | — |
Writing & Preference Muse Spark 1.2 leads
Llama-3.3-70B-Instruct: 47.8 (#203), Muse Spark 1.2: 72.6 (#14)
| Benchmark | Llama-3.3-70B-Instruct | Muse Spark 1.2 |
|---|---|---|
| LMArena Text | 1274 | 1486 |
| LMArena Creative Writing | 1250 | 1444 |
| LMArena Multi-Turn | 1279 | 1504 |
| EQ-Bench Creative Writing | — | 1840 |
| LiveBench Language | 39.2% | — |
Frequently asked questions
Is Llama-3.3-70B-Instruct better than Muse Spark 1.2?
Muse Spark 1.2 is the stronger model overall, scoring 49.8 to 30.3 on the Noometry Index. Llama-3.3-70B-Instruct costs 13× 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-3.3-70B-Instruct or Muse Spark 1.2?
Llama-3.3-70B-Instruct is cheaper. It lists at $0.10 per million input tokens and $0.32 per million output tokens; Muse Spark 1.2 lists at $1.25 and $4.25.
Is Llama-3.3-70B-Instruct or Muse Spark 1.2 better for coding?
Muse Spark 1.2 scores higher on coding benchmarks: 48.6 versus 30.6 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-3.3-70B-Instruct and Muse Spark 1.2 share?
18 benchmarks have published results for both models. Llama-3.3-70B-Instruct has 43 scored results on Noometry and Muse Spark 1.2 has 28.