Model comparison
GPT-3.5-turbo vs Muse Spark 1.3
Muse Spark 1.3 is the stronger model overall, scoring 54.8 to 23.2 on the Noometry Index. GPT-3.5-turbo costs 2.7× less per token, which makes it the better buy when Muse Spark 1.3's lead doesn't matter for your workload.
Last verified . 25 shared benchmarks.
MetaSummary
- They share 25 benchmarks with published results for both. GPT-3.5-turbo scores higher in 0 categories and Muse Spark 1.3 in 8 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in math, where Muse Spark 1.3 leads 73.1 to 6.3.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 2.2% for GPT-3.5-turbo and 99.2% for Muse Spark 1.3.
- GPT-3.5-turbo is cheaper at $0.50 / $1.50 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 16K.
Side by side
| GPT-3.5-turbo | Muse Spark 1.3 | |
|---|---|---|
| Provider | OpenAI | Meta |
| Noometry Index | 23.2 | 54.8 |
| Released | 2023-03-01 | 2026-09-02 |
| Weights | Proprietary | Proprietary |
| Context window | 16K | 1.05M |
| Max output | 4K | 131K |
| Input $ / M tokens | $0.50 | $1.25 |
| Output $ / M tokens | $1.50 | $4.25 |
| Results tracked | 44 | 37 |
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Category by category
Coding Muse Spark 1.3 leads
GPT-3.5-turbo: 23.9 (#331), Muse Spark 1.3: 56.6 (#21)
| Benchmark | GPT-3.5-turbo | Muse Spark 1.3 |
|---|---|---|
| LMArena Coding | 1136 | 1514 |
| CursorBench | — | 41.6% |
| LMArena WebDev | — | 1657 |
| SciCode | — | 59.7% |
| WeirdML | 3.5% | — |
| BigCodeBench Instruct | 39.1% | — |
| BigCodeBench Complete | 50.6% | — |
| HumanEval+ | 70.7% | — |
| MBPP+ | 69.7% | — |
Agentic & Tool Use Not comparable
GPT-3.5-turbo: —, Muse Spark 1.3: 38.6 (#30)
| Benchmark | GPT-3.5-turbo | Muse Spark 1.3 |
|---|---|---|
| APEX-Agents | — | 57.8% |
| GDP.pdf | — | 27.6% |
| METR Time Horizons | 21.5% | — |
Reasoning Muse Spark 1.3 leads
GPT-3.5-turbo: 13.8 (#332), Muse Spark 1.3: 54.0 (#27)
| Benchmark | GPT-3.5-turbo | Muse Spark 1.3 |
|---|---|---|
| Chess Puzzles | 0% | 38% |
| LMArena Hard Prompts | 1108 | 1503 |
| Mystery Game Puzzles | 3% | 25% |
| DTBench | 48.5% | 96.5% |
| LMCA | 9.7% | 53.9% |
| Epoch Capabilities Index | 118.55 | 156.75 |
| NYT Connections (extended) | — | 85.1% |
| CritPt | — | 26% |
| Adversarial NLI | 58.1% | — |
| Bench to the Future 3 | — | 0.14 |
| BIG-Bench Hard | 61.6% | — |
| CommonsenseQA 2.0 | 57% | — |
| ForecastBench | 50.4 | — |
| WinoGrande | 81.6% | — |
Math Muse Spark 1.3 leads
GPT-3.5-turbo: 6.3 (#327), Muse Spark 1.3: 73.1 (#21)
| Benchmark | GPT-3.5-turbo | Muse Spark 1.3 |
|---|---|---|
| FrontierMath (Tiers 1-3) | 0% | 74.4% |
| OTIS Mock AIME 2024-2025 | 2.2% | 99.2% |
| LMArena Math | 1142 | 1494 |
| FrontierMath Tier 4 | — | 46.3% |
| ProofBench | — | 58% |
| MATH Level 5 | 15.9% | — |
| GSM8K | 57.8% | — |
Knowledge Muse Spark 1.3 leads
GPT-3.5-turbo: 10.0 (#303), Muse Spark 1.3: 42.6 (#95)
| Benchmark | GPT-3.5-turbo | Muse Spark 1.3 |
|---|---|---|
| LMArena Expert | 1070 | 1516 |
| GPQA Diamond | 28% | — |
| ARC (AI2) Challenge | 87.4% | — |
| BoolQ | 87% | — |
| MMLU | 71.4% | — |
| OpenBookQA | 86% | — |
| TriviaQA | 85.8% | — |
Multimodal Not comparable
GPT-3.5-turbo: —, Muse Spark 1.3: 43.7 (#22)
| Benchmark | GPT-3.5-turbo | Muse Spark 1.3 |
|---|---|---|
| LMArena Vision | — | 1309 |
| LMArena Document | — | 1471 |
Multilingual Muse Spark 1.3 leads
GPT-3.5-turbo: 31.5 (#258), Muse Spark 1.3: 57.4 (#8)
| Benchmark | GPT-3.5-turbo | Muse Spark 1.3 |
|---|---|---|
| LMArena Non-English | 1108 | 1481 |
| LMArena Chinese | 1075 | 1529 |
| LMArena French | 1118 | 1524 |
| LMArena German | 1090 | 1515 |
| LMArena Japanese | 1043 | 1474 |
| LMArena Korean | 1019 | 1501 |
| LMArena Russian | 1123 | 1490 |
| LMArena Spanish | 1121 | 1490 |
Instruction Following Muse Spark 1.3 leads
GPT-3.5-turbo: 57.9 (#262), Muse Spark 1.3: 77.5 (#22)
| Benchmark | GPT-3.5-turbo | Muse Spark 1.3 |
|---|---|---|
| LMArena Instruction Following | 1119 | 1477 |
Long Context Muse Spark 1.3 leads
GPT-3.5-turbo: 34.0 (#254), Muse Spark 1.3: 45.6 (#32)
| Benchmark | GPT-3.5-turbo | Muse Spark 1.3 |
|---|---|---|
| LMArena Longer Query | 1121 | 1488 |
Writing & Preference Muse Spark 1.3 leads
GPT-3.5-turbo: 25.3 (#305), Muse Spark 1.3: 73.6 (#9)
| Benchmark | GPT-3.5-turbo | Muse Spark 1.3 |
|---|---|---|
| LMArena Text | 1125 | 1490 |
| LMArena Creative Writing | 1092 | 1455 |
| EQ-Bench Creative Writing | 451 | 1906 |
| LMArena Multi-Turn | 1117 | 1482 |
Frequently asked questions
Is GPT-3.5-turbo better than Muse Spark 1.3?
Muse Spark 1.3 is the stronger model overall, scoring 54.8 to 23.2 on the Noometry Index. GPT-3.5-turbo costs 2.7× 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, GPT-3.5-turbo or Muse Spark 1.3?
GPT-3.5-turbo is cheaper. It lists at $0.50 per million input tokens and $1.50 per million output tokens; Muse Spark 1.3 lists at $1.25 and $4.25.
Is GPT-3.5-turbo or Muse Spark 1.3 better for coding?
Muse Spark 1.3 scores higher on coding benchmarks: 56.6 versus 23.9 in the Noometry coding category.
Which has the bigger context window?
Muse Spark 1.3 does, with 1.05M tokens against 16K.
How many benchmarks do GPT-3.5-turbo and Muse Spark 1.3 share?
25 benchmarks have published results for both models. GPT-3.5-turbo has 44 scored results on Noometry and Muse Spark 1.3 has 37.