Model comparison
GPT-5.5 vs Muse Spark 1.3
GPT-5.5 is the stronger model overall, scoring 63.4 to 54.4 on the Noometry Index. Muse Spark 1.3 costs 5.6× less per token, which makes it the better buy when GPT-5.5's lead doesn't matter for your workload.
Last verified . 33 shared benchmarks.
MetaSummary
- They share 33 benchmarks with published results for both. GPT-5.5 scores higher in 7 categories and Muse Spark 1.3 in 3 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where GPT-5.5 leads 64.4 to 42.0.
- The biggest single-benchmark swing is Mystery Game Puzzles: 56% for GPT-5.5 and 25% for Muse Spark 1.3.
- Muse Spark 1.3 is cheaper at $1.25 / $4.25 per million input/output tokens, against $5 / $30 for GPT-5.5.
- GPT-5.5 accepts more context: 1.05M tokens versus 1.05M.
Side by side
| GPT-5.5 | Muse Spark 1.3 | |
|---|---|---|
| Provider | OpenAI | Meta |
| Noometry Index | 63.4 | 54.4 |
| Released | 2026-04-23 | 2026-09-02 |
| Weights | Proprietary | Proprietary |
| Context window | 1.05M | 1.05M |
| Max output | 128K | 131K |
| Input $ / M tokens | $5 | $1.25 |
| Output $ / M tokens | $30 | $4.25 |
| Results tracked | 71 | 34 |
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Category by category
Coding GPT-5.5 leads
GPT-5.5: 58.3 (#13), Muse Spark 1.3: 56.1 (#18)
| Benchmark | GPT-5.5 | Muse Spark 1.3 |
|---|---|---|
| LMArena WebDev | 1512 | 1655 |
| SciCode | 56.1% | 59.7% |
| LMArena Coding | 1494 | 1516 |
| SWE-bench Verified | 80.6% | — |
| DeepSWE | 67% | — |
| FrontierCode | 43% | — |
| CursorBench | — | 41.6% |
| GSO | 40.2% | — |
| WeirdML | 84.9% | — |
| MirrorCode | 10% | — |
| ALE-Bench | 1,943 | — |
Agentic & Tool Use GPT-5.5 leads
GPT-5.5: 50.7 (#6), Muse Spark 1.3: 38.6 (#28)
| Benchmark | GPT-5.5 | Muse Spark 1.3 |
|---|---|---|
| APEX-Agents | 55.1% | 57.8% |
| GDP.pdf | 26% | 27.6% |
| Terminal-Bench | 84.7% | — |
| OSWorld 2.0 | 13% | — |
| Remote Labor Index | 6.3% | — |
| τ²-bench Banking | 44.6% | — |
| DeepResearch Bench | 54% | — |
| PostTrainBench | 27.2% | — |
| ExploitBench | 47.4% | — |
| GBAEval | 53.2% | — |
| LMArena Search | 1242 | — |
| Vending-Bench 2 | 7,524 | — |
Reasoning GPT-5.5 leads
GPT-5.5: 72.7 (#8), Muse Spark 1.3: 52.9 (#27)
| Benchmark | GPT-5.5 | Muse Spark 1.3 |
|---|---|---|
| NYT Connections (extended) | 96.2% | 85.1% |
| CritPt | 27.1% | 24.9% |
| Chess Puzzles | 54% | 38% |
| LMArena Hard Prompts | 1489 | 1503 |
| Mystery Game Puzzles | 56% | 25% |
| DTBench | 96% | 96.5% |
| LMCA | 54.3% | 53.9% |
| Bench to the Future 3 | 0.14 | 0.14 |
| Epoch Capabilities Index | 159.25 | 156.89 |
| ARC-AGI-2 | 85% | — |
| SimpleBench | 69% | — |
| Kagi LLM Benchmark | 88.8% | — |
| ARC-AGI-1 | 95% | — |
| EBR-Bench | 34.3% | — |
| Surface Evolver Bench | 88.1% | — |
| ForecastBench | 60.6 | — |
Math GPT-5.5 leads
GPT-5.5: 81.5 (#9), Muse Spark 1.3: 72.4 (#16)
| Benchmark | GPT-5.5 | Muse Spark 1.3 |
|---|---|---|
| FrontierMath (Tiers 1-3) | 85.3% | 74.4% |
| FrontierMath Tier 4 | 72.5% | 46.3% |
| OTIS Mock AIME 2024-2025 | 100% | 99.2% |
| LMArena Math | 1486 | 1497 |
| MathArena Final-Answer Competitions | 94.3% | — |
| ProofBench | 50% | — |
| FrontierMath (Feb 2025 set) | 51.7% | — |
| FrontierMath Erdős | 0% | — |
| FrontierMath Tier 4 (v1) | 35.4% | — |
Knowledge GPT-5.5 leads
GPT-5.5: 64.4 (#14), Muse Spark 1.3: 42.0 (#92)
| Benchmark | GPT-5.5 | Muse Spark 1.3 |
|---|---|---|
| LMArena Expert | 1508 | 1516 |
| GPQA Diamond | 94% | — |
| SimpleQA Verified | 63% | — |
| Vectara Hallucination Rate | 9.3% | — |
Multimodal GPT-5.5 leads
GPT-5.5: 47.1 (#11), Muse Spark 1.3: 43.0 (#21)
| Benchmark | GPT-5.5 | Muse Spark 1.3 |
|---|---|---|
| LMArena Vision | 1297 | 1310 |
| LMArena Document | 1486 | 1471 |
| Blueprint-Bench 2 | 36.2% | — |
| Furniture Assembly | 44.2% | — |
Multilingual Muse Spark 1.3 leads
GPT-5.5: 56.5 (#19), Muse Spark 1.3: 57.8 (#6)
| Benchmark | GPT-5.5 | Muse Spark 1.3 |
|---|---|---|
| LMArena Non-English | 1467 | 1485 |
| LMArena Chinese | 1535 | 1527 |
| LMArena French | 1486 | 1534 |
| LMArena Korean | 1461 | 1513 |
| LMArena Russian | 1473 | 1491 |
| LMArena Spanish | 1469 | 1485 |
| LMArena German | 1478 | — |
| LMArena Japanese | 1498 | — |
Instruction Following Too close to call
GPT-5.5: 77.7 (#18), Muse Spark 1.3: 77.8 (#14)
| Benchmark | GPT-5.5 | Muse Spark 1.3 |
|---|---|---|
| LMArena Instruction Following | 1479 | 1481 |
Long Context GPT-5.5 leads
GPT-5.5: 48.3 (#12), Muse Spark 1.3: 45.7 (#29)
| Benchmark | GPT-5.5 | Muse Spark 1.3 |
|---|---|---|
| LMArena Longer Query | 1484 | 1491 |
| CL-bench Life | 22.2% | — |
Writing & Preference Too close to call
GPT-5.5: 72.8 (#13), Muse Spark 1.3: 73.7 (#10)
| Benchmark | GPT-5.5 | Muse Spark 1.3 |
|---|---|---|
| LMArena Text | 1471 | 1490 |
| LMArena Creative Writing | 1455 | 1450 |
| EQ-Bench Creative Writing | 1844 | 1906 |
| LMArena Multi-Turn | 1477 | 1486 |
| EQ-Bench 4 | 1315 | — |
Frequently asked questions
Is GPT-5.5 better than Muse Spark 1.3?
GPT-5.5 is the stronger model overall, scoring 63.4 to 54.4 on the Noometry Index. Muse Spark 1.3 costs 5.6× less per token, which makes it the better buy when GPT-5.5's lead doesn't matter for your workload.
Which is cheaper, GPT-5.5 or Muse Spark 1.3?
Muse Spark 1.3 is cheaper. It lists at $1.25 per million input tokens and $4.25 per million output tokens; GPT-5.5 lists at $5 and $30.
Is GPT-5.5 or Muse Spark 1.3 better for coding?
GPT-5.5 scores higher on coding benchmarks: 58.3 versus 56.1 in the Noometry coding category.
Which has the bigger context window?
GPT-5.5 does, with 1.05M tokens against 1.05M.
How many benchmarks do GPT-5.5 and Muse Spark 1.3 share?
33 benchmarks have published results for both models. GPT-5.5 has 71 scored results on Noometry and Muse Spark 1.3 has 34.