Model comparison
Llama-3.3-70B-Instruct vs Muse Spark 1.1
Muse Spark 1.1 is the stronger model overall, scoring 49.9 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 1.1's lead doesn't matter for your workload.
Last verified . 22 shared benchmarks.
MetaSummary
- They share 22 benchmarks with published results for both. Llama-3.3-70B-Instruct scores higher in 0 categories and Muse Spark 1.1 in 9 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where Muse Spark 1.1 leads 45.9 to 13.7.
- The biggest single-benchmark swing is DTBench: 59.5% for Llama-3.3-70B-Instruct and 94.4% for Muse Spark 1.1.
- 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 1.1.
- Muse Spark 1.1 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 1.1 | |
|---|---|---|
| Provider | Meta | Meta |
| Noometry Index | 30.3 | 49.9 |
| Released | 2024-12-06 | 2026-04-08 |
| 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 | 36 |
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Category by category
Coding Muse Spark 1.1 leads
Llama-3.3-70B-Instruct: 30.6 (#284), Muse Spark 1.1: 50.8 (#38)
| Benchmark | Llama-3.3-70B-Instruct | Muse Spark 1.1 |
|---|---|---|
| SciCode | 26% | 58.8% |
| LMArena Coding | 1268 | 1497 |
| DeepSWE | — | 53.3% |
| LMArena WebDev | — | 1542 |
| WeirdML | 14.4% | — |
| BigCodeBench Instruct | 46.9% | — |
| LiveBench Coding | 36.6% | — |
| BigCodeBench Complete | 57.5% | — |
Agentic & Tool Use Muse Spark 1.1 leads
Llama-3.3-70B-Instruct: 25.6 (#100), Muse Spark 1.1: 33.9 (#45)
| Benchmark | Llama-3.3-70B-Instruct | Muse Spark 1.1 |
|---|---|---|
| Berkeley Function Calling Leaderboard | 31.9% | — |
| τ²-bench Banking | — | 40.5% |
| BALROG | 23% | — |
| GBAEval | — | 7.9% |
| GDP.pdf | — | 15% |
| Vending-Bench 2 | — | 6,520 |
Reasoning Muse Spark 1.1 leads
Llama-3.3-70B-Instruct: 13.7 (#325), Muse Spark 1.1: 45.9 (#41)
| Benchmark | Llama-3.3-70B-Instruct | Muse Spark 1.1 |
|---|---|---|
| CritPt | 0% | 15.1% |
| LMArena Hard Prompts | 1257 | 1486 |
| DTBench | 59.5% | 94.4% |
| LMCA | 17.5% | 49.9% |
| Epoch Capabilities Index | 127.32 | 154.32 |
| SimpleBench | 19.9% | — |
| NYT Connections (extended) | — | 84.9% |
| LiveBench Reasoning | 50.8% | — |
| LiveBench Data Analysis | 49.5% | — |
| Surface Evolver Bench | — | 52.5% |
| ForecastBench | 58.6 | — |
| LiveBench | 50.2% | — |
Math Muse Spark 1.1 leads
Llama-3.3-70B-Instruct: 14.3 (#292), Muse Spark 1.1: 44.0 (#67)
| Benchmark | Llama-3.3-70B-Instruct | Muse Spark 1.1 |
|---|---|---|
| LMArena Math | 1268 | 1477 |
| OTIS Mock AIME 2024-2025 | 5.1% | — |
| ProofBench | — | 39% |
| LiveBench Math | 42.2% | — |
| MATH Level 5 | 41.6% | — |
Knowledge Muse Spark 1.1 leads
Llama-3.3-70B-Instruct: 30.5 (#220), Muse Spark 1.1: 52.8 (#55)
| Benchmark | Llama-3.3-70B-Instruct | Muse Spark 1.1 |
|---|---|---|
| LMArena Expert | 1225 | 1480 |
| GPQA Diamond | 47.4% | — |
| SimpleQA Verified | — | 57.8% |
| Confabulations | 22.8% | — |
| Vectara Hallucination Rate | 4.1% | — |
| MMLU | 86.3% | — |
Multimodal Not comparable
Llama-3.3-70B-Instruct: —, Muse Spark 1.1: 41.9 (#28)
| Benchmark | Llama-3.3-70B-Instruct | Muse Spark 1.1 |
|---|---|---|
| LMArena Vision | — | 1292 |
| LMArena Document | — | 1465 |
Multilingual Muse Spark 1.1 leads
Llama-3.3-70B-Instruct: 40.1 (#215), Muse Spark 1.1: 56.8 (#16)
| Benchmark | Llama-3.3-70B-Instruct | Muse Spark 1.1 |
|---|---|---|
| LMArena Non-English | 1236 | 1471 |
| LMArena Chinese | 1217 | 1518 |
| LMArena French | 1280 | 1494 |
| LMArena German | 1251 | 1466 |
| LMArena Japanese | 1149 | 1445 |
| LMArena Korean | 1142 | 1459 |
| LMArena Russian | 1252 | 1482 |
| LMArena Spanish | 1269 | 1471 |
Instruction Following Muse Spark 1.1 leads
Llama-3.3-70B-Instruct: 71.3 (#152), Muse Spark 1.1: 76.8 (#36)
| Benchmark | Llama-3.3-70B-Instruct | Muse Spark 1.1 |
|---|---|---|
| LMArena Instruction Following | 1242 | 1457 |
| LiveBench Instruction Following | 82.7% | — |
Long Context Muse Spark 1.1 leads
Llama-3.3-70B-Instruct: 26.4 (#290), Muse Spark 1.1: 44.8 (#55)
| Benchmark | Llama-3.3-70B-Instruct | Muse Spark 1.1 |
|---|---|---|
| LMArena Longer Query | 1256 | 1463 |
| Fiction.LiveBench | 33.3% | — |
Writing & Preference Muse Spark 1.1 leads
Llama-3.3-70B-Instruct: 47.8 (#203), Muse Spark 1.1: 73.6 (#11)
| Benchmark | Llama-3.3-70B-Instruct | Muse Spark 1.1 |
|---|---|---|
| LMArena Text | 1274 | 1479 |
| LMArena Creative Writing | 1250 | 1440 |
| LMArena Multi-Turn | 1279 | 1484 |
| EQ-Bench Creative Writing | — | 1927 |
| EQ-Bench 4 | — | 1260 |
| LiveBench Language | 39.2% | — |
Frequently asked questions
Is Llama-3.3-70B-Instruct better than Muse Spark 1.1?
Muse Spark 1.1 is the stronger model overall, scoring 49.9 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 1.1's lead doesn't matter for your workload.
Which is cheaper, Llama-3.3-70B-Instruct or Muse Spark 1.1?
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 1.1 lists at $1.25 and $4.25.
Is Llama-3.3-70B-Instruct or Muse Spark 1.1 better for coding?
Muse Spark 1.1 scores higher on coding benchmarks: 50.8 versus 30.6 in the Noometry coding category.
Which has the bigger context window?
Muse Spark 1.1 does, with 1.05M tokens against 128K.
How many benchmarks do Llama-3.3-70B-Instruct and Muse Spark 1.1 share?
22 benchmarks have published results for both models. Llama-3.3-70B-Instruct has 43 scored results on Noometry and Muse Spark 1.1 has 36.