Model comparison
gpt-oss-120b vs Muse Spark 1.1
Muse Spark 1.1 is the stronger model overall, scoring 49.9 to 36.3 on the Noometry Index. gpt-oss-120b costs 28× less per token, which makes it the better buy when Muse Spark 1.1's lead doesn't matter for your workload.
Last verified . 26 shared benchmarks.
MetaSummary
- They share 26 benchmarks with published results for both. gpt-oss-120b scores higher in 1 category and Muse Spark 1.1 in 8 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where Muse Spark 1.1 leads 47.1 to 20.0.
- The biggest single-benchmark swing is LMCA: 22.1% for gpt-oss-120b and 49.9% for Muse Spark 1.1.
- gpt-oss-120b is cheaper at $0.037 / $0.17 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 131K.
- gpt-oss-120b has downloadable open weights; the other is API-only.
Side by side
| gpt-oss-120b | Muse Spark 1.1 | |
|---|---|---|
| Provider | OpenAI | Meta |
| Noometry Index | 36.3 | 49.9 |
| Released | 2025-08-05 | 2026-04-08 |
| Weights | Open | Proprietary |
| Context window | 131K | 1.05M |
| Max output | 41K | 131K |
| Input $ / M tokens | $0.037 | $1.25 |
| Output $ / M tokens | $0.17 | $4.25 |
| Results tracked | 48 | 37 |
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Category by category
Coding Muse Spark 1.1 leads
gpt-oss-120b: 33.5 (#256), Muse Spark 1.1: 51.3 (#40)
| Benchmark | gpt-oss-120b | Muse Spark 1.1 |
|---|---|---|
| SciCode | 36% | 58.8% |
| LMArena Coding | 1380 | 1498 |
| DeepSWE | — | 53.3% |
| SWE-bench Verified (bash only) | 26% | — |
| Aider Polyglot | 41.8% | — |
| LMArena WebDev | — | 1542 |
| WeirdML | 48.2% | — |
| ALE-Bench | 575.62 | — |
| AlgoTune | 1.41 | — |
Agentic & Tool Use Muse Spark 1.1 leads
gpt-oss-120b: 12.2 (#153), Muse Spark 1.1: 30.8 (#73)
| Benchmark | gpt-oss-120b | Muse Spark 1.1 |
|---|---|---|
| APEX-Agents | 4.4% | 31.8% |
| Vending-Bench 2 | -21.53 | 6,520 |
| Terminal-Bench | 18.7% | — |
| τ²-bench Banking | — | 40.5% |
| GBAEval | — | 7.9% |
| GDP.pdf | — | 15% |
| METR Time Horizons | 56.6% | — |
Reasoning Muse Spark 1.1 leads
gpt-oss-120b: 20.0 (#245), Muse Spark 1.1: 47.1 (#44)
| Benchmark | gpt-oss-120b | Muse Spark 1.1 |
|---|---|---|
| CritPt | 1.1% | 15.1% |
| LMArena Hard Prompts | 1364 | 1486 |
| DTBench | 76.3% | 94.4% |
| LMCA | 22.1% | 49.9% |
| Surface Evolver Bench | 25% | 52.5% |
| Epoch Capabilities Index | 139.93 | 154.21 |
| SimpleBench | 22.1% | — |
| Kagi LLM Benchmark | 58.6% | — |
| NYT Connections (extended) | — | 84.9% |
| Chess Puzzles | 20% | — |
| Mystery Game Puzzles | 2% | — |
Math gpt-oss-120b leads
gpt-oss-120b: 52.5 (#50), Muse Spark 1.1: 45.5 (#76)
| Benchmark | gpt-oss-120b | Muse Spark 1.1 |
|---|---|---|
| LMArena Math | 1389 | 1483 |
| OTIS Mock AIME 2024-2025 | 88.9% | — |
| ProofBench | — | 39% |
| Omni-MATH | 68.8% | — |
Knowledge Muse Spark 1.1 leads
gpt-oss-120b: 42.4 (#96), Muse Spark 1.1: 53.1 (#59)
| Benchmark | gpt-oss-120b | Muse Spark 1.1 |
|---|---|---|
| LMArena Expert | 1356 | 1478 |
| GPQA Diamond | 75.8% | — |
| SimpleQA Verified | — | 57.8% |
| MMLU-Pro | 79.5% | — |
| Confabulations | 15.7% | — |
| Vectara Hallucination Rate | 14.2% | — |
| GPQA (HELM) | 68.4% | — |
Multimodal Not comparable
gpt-oss-120b: —, Muse Spark 1.1: 42.6 (#29)
| Benchmark | gpt-oss-120b | Muse Spark 1.1 |
|---|---|---|
| LMArena Vision | — | 1293 |
| LMArena Document | — | 1465 |
Multilingual Muse Spark 1.1 leads
gpt-oss-120b: 48.0 (#147), Muse Spark 1.1: 56.7 (#17)
| Benchmark | gpt-oss-120b | Muse Spark 1.1 |
|---|---|---|
| LMArena Non-English | 1351 | 1472 |
| LMArena Chinese | 1385 | 1518 |
| LMArena French | 1369 | 1494 |
| LMArena German | 1353 | 1466 |
| LMArena Japanese | 1331 | 1451 |
| LMArena Korean | 1282 | 1458 |
| LMArena Russian | 1343 | 1483 |
| LMArena Spanish | 1389 | 1464 |
Instruction Following Muse Spark 1.1 leads
gpt-oss-120b: 69.3 (#173), Muse Spark 1.1: 76.5 (#39)
| Benchmark | gpt-oss-120b | Muse Spark 1.1 |
|---|---|---|
| LMArena Instruction Following | 1318 | 1457 |
| IFEval | 83.6% | — |
Long Context Muse Spark 1.1 leads
gpt-oss-120b: 31.4 (#278), Muse Spark 1.1: 44.8 (#58)
| Benchmark | gpt-oss-120b | Muse Spark 1.1 |
|---|---|---|
| LMArena Longer Query | 1319 | 1462 |
| Fiction.LiveBench | 44.4% | — |
Writing & Preference Muse Spark 1.1 leads
gpt-oss-120b: 46.5 (#217), Muse Spark 1.1: 73.4 (#11)
| Benchmark | gpt-oss-120b | Muse Spark 1.1 |
|---|---|---|
| LMArena Text | 1365 | 1479 |
| LMArena Creative Writing | 1275 | 1437 |
| EQ-Bench Creative Writing | 961 | 1927 |
| LMArena Multi-Turn | 1340 | 1485 |
| Short-Story Creative Writing | 77.1% | — |
| WildBench | 84.5% | — |
| EQ-Bench 4 | — | 1260 |
Frequently asked questions
Is gpt-oss-120b better than Muse Spark 1.1?
Muse Spark 1.1 is the stronger model overall, scoring 49.9 to 36.3 on the Noometry Index. gpt-oss-120b costs 28× 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, gpt-oss-120b or Muse Spark 1.1?
gpt-oss-120b is cheaper. It lists at $0.037 per million input tokens and $0.17 per million output tokens; Muse Spark 1.1 lists at $1.25 and $4.25.
Is gpt-oss-120b or Muse Spark 1.1 better for coding?
Muse Spark 1.1 scores higher on coding benchmarks: 51.3 versus 33.5 in the Noometry coding category.
Which has the bigger context window?
Muse Spark 1.1 does, with 1.05M tokens against 131K.
How many benchmarks do gpt-oss-120b and Muse Spark 1.1 share?
26 benchmarks have published results for both models. gpt-oss-120b has 48 scored results on Noometry and Muse Spark 1.1 has 37.