# Llama-3.3-70B-Instruct vs Muse Spark 1.3

> Muse Spark 1.3 is the stronger model overall, scoring 54.8 to 30.6 on the Noometry Index. Llama-3.3-70B-Instruct costs 13× less per token, which makes it the better buy when Muse Spark 1.3's lead doesn't matter for your workload.

- Canonical page: https://noometry.com/compare/llama-3-3-70b-instruct-vs-muse-spark-1-3
- Last updated: 2026-10-12
- Shared benchmarks: 23

## Summary

- They share 23 benchmarks with published results for both. Llama-3.3-70B-Instruct scores higher in 0 categories and Muse Spark 1.3 in 9 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in math, where Muse Spark 1.3 leads 73.1 to 15.3.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 5.1% for Llama-3.3-70B-Instruct and 99.2% for Muse Spark 1.3.
- 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 1.3.
- Muse Spark 1.3 accepts more context: 1.05M tokens versus 128K.
- Llama-3.3-70B-Instruct has downloadable open weights; the other is API-only.

## Snapshot

| | Llama-3.3-70B-Instruct | Muse Spark 1.3 |
|---|---|---|
| Provider | Meta | Meta |
| Noometry Index | 30.6 | 54.8 |
| Rank | 291 | 27 |
| Context | 128K | 1.05M |
| Input $/M | $0.10 | $1.25 |
| Output $/M | $0.32 | $4.25 |
| Weights | Open | Proprietary |

## Coding

- Llama-3.3-70B-Instruct: 31.0 (#290)
- Muse Spark 1.3: 56.6 (#21)

| Benchmark | Llama-3.3-70B-Instruct | Muse Spark 1.3 |
|---|---|---|
| SciCode | 26% | 59.7% |
| LMArena Coding | 1268 | 1514 |
| CursorBench | — | 41.6% |
| LMArena WebDev | — | 1657 |
| WeirdML | 14.4% | — |
| BigCodeBench Instruct | 46.9% | — |
| LiveBench Coding | 36.6% | — |
| BigCodeBench Complete | 57.5% | — |

## Agentic & Tool Use

- Llama-3.3-70B-Instruct: 25.8 (#105)
- Muse Spark 1.3: 38.6 (#30)

| Benchmark | Llama-3.3-70B-Instruct | Muse Spark 1.3 |
|---|---|---|
| APEX-Agents | — | 57.8% |
| Berkeley Function Calling Leaderboard | 31.9% | — |
| BALROG | 23% | — |
| GDP.pdf | — | 27.6% |

## Reasoning

- Llama-3.3-70B-Instruct: 14.1 (#327)
- Muse Spark 1.3: 54.0 (#27)

| Benchmark | Llama-3.3-70B-Instruct | Muse Spark 1.3 |
|---|---|---|
| CritPt | 0% | 26% |
| LMArena Hard Prompts | 1257 | 1503 |
| DTBench | 59.5% | 96.5% |
| LMCA | 17.5% | 53.9% |
| Epoch Capabilities Index | 127.33 | 156.75 |
| SimpleBench | 19.9% | — |
| NYT Connections (extended) | — | 85.1% |
| Chess Puzzles | — | 38% |
| LiveBench Reasoning | 50.8% | — |
| Mystery Game Puzzles | — | 25% |
| LiveBench Data Analysis | 49.5% | — |
| Bench to the Future 3 | — | 0.14 |
| ForecastBench | 58.6 | — |
| LiveBench | 50.2% | — |

## Math

- Llama-3.3-70B-Instruct: 15.3 (#298)
- Muse Spark 1.3: 73.1 (#21)

| Benchmark | Llama-3.3-70B-Instruct | Muse Spark 1.3 |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 5.1% | 99.2% |
| LMArena Math | 1267 | 1494 |
| FrontierMath (Tiers 1-3) | — | 74.4% |
| FrontierMath Tier 4 | — | 46.3% |
| ProofBench | — | 58% |
| LiveBench Math | 42.2% | — |
| MATH Level 5 | 41.6% | — |

## Knowledge

- Llama-3.3-70B-Instruct: 30.6 (#226)
- Muse Spark 1.3: 42.6 (#95)

| Benchmark | Llama-3.3-70B-Instruct | Muse Spark 1.3 |
|---|---|---|
| LMArena Expert | 1225 | 1516 |
| GPQA Diamond | 47.4% | — |
| Confabulations | 22.8% | — |
| Vectara Hallucination Rate | 4.1% | — |
| MMLU | 86.3% | — |

## Multimodal

- Llama-3.3-70B-Instruct: —
- Muse Spark 1.3: 43.7 (#22)

| Benchmark | Llama-3.3-70B-Instruct | Muse Spark 1.3 |
|---|---|---|
| LMArena Vision | — | 1309 |
| LMArena Document | — | 1471 |

## Multilingual

- Llama-3.3-70B-Instruct: 39.9 (#220)
- Muse Spark 1.3: 57.4 (#8)

| Benchmark | Llama-3.3-70B-Instruct | Muse Spark 1.3 |
|---|---|---|
| LMArena Non-English | 1236 | 1481 |
| LMArena Chinese | 1217 | 1529 |
| LMArena French | 1281 | 1524 |
| LMArena German | 1251 | 1515 |
| LMArena Japanese | 1150 | 1474 |
| LMArena Korean | 1143 | 1501 |
| LMArena Russian | 1252 | 1490 |
| LMArena Spanish | 1270 | 1490 |

## Instruction Following

- Llama-3.3-70B-Instruct: 71.1 (#157)
- Muse Spark 1.3: 77.5 (#22)

| Benchmark | Llama-3.3-70B-Instruct | Muse Spark 1.3 |
|---|---|---|
| LMArena Instruction Following | 1242 | 1477 |
| LiveBench Instruction Following | 82.7% | — |

## Long Context

- Llama-3.3-70B-Instruct: 26.4 (#295)
- Muse Spark 1.3: 45.6 (#32)

| Benchmark | Llama-3.3-70B-Instruct | Muse Spark 1.3 |
|---|---|---|
| LMArena Longer Query | 1256 | 1488 |
| Fiction.LiveBench | 33.3% | — |

## Writing & Preference

- Llama-3.3-70B-Instruct: 47.6 (#207)
- Muse Spark 1.3: 73.6 (#9)

| Benchmark | Llama-3.3-70B-Instruct | Muse Spark 1.3 |
|---|---|---|
| LMArena Text | 1274 | 1490 |
| LMArena Creative Writing | 1250 | 1455 |
| LMArena Multi-Turn | 1280 | 1482 |
| EQ-Bench Creative Writing | — | 1906 |
| LiveBench Language | 39.2% | — |

## FAQ

### Is Llama-3.3-70B-Instruct better than Muse Spark 1.3?

Muse Spark 1.3 is the stronger model overall, scoring 54.8 to 30.6 on the Noometry Index. Llama-3.3-70B-Instruct costs 13× 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, Llama-3.3-70B-Instruct or Muse Spark 1.3?

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 1.3 lists at $1.25 and $4.25.

### Is Llama-3.3-70B-Instruct or Muse Spark 1.3 better for coding?

Muse Spark 1.3 scores higher on coding benchmarks: 56.6 versus 31.0 in the Noometry coding category.

### Which has the bigger context window?

Muse Spark 1.3 does, with 1.05M tokens against 128K.

### How many benchmarks do Llama-3.3-70B-Instruct and Muse Spark 1.3 share?

23 benchmarks have published results for both models. Llama-3.3-70B-Instruct has 43 scored results on Noometry and Muse Spark 1.3 has 37.