Model comparison
gpt-oss-20b vs Muse Spark 1.3
Muse Spark 1.3 is the stronger model overall, scoring 54.8 to 32.5 on the Noometry Index. gpt-oss-20b costs 56× less per token, which makes it the better buy when Muse Spark 1.3's lead doesn't matter for your workload.
Last verified . 24 shared benchmarks.
MetaSummary
- They share 24 benchmarks with published results for both. gpt-oss-20b 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 writing & preference, where Muse Spark 1.3 leads 73.6 to 35.5.
- The biggest single-benchmark swing is LMCA: 14.5% for gpt-oss-20b and 53.9% for Muse Spark 1.3.
- gpt-oss-20b is cheaper at $0.018 / $0.09 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 131K.
- gpt-oss-20b has downloadable open weights; the other is API-only.
Side by side
| gpt-oss-20b | Muse Spark 1.3 | |
|---|---|---|
| Provider | OpenAI | Meta |
| Noometry Index | 32.5 | 54.8 |
| Released | 2025-08-05 | 2026-09-02 |
| Weights | Open | Proprietary |
| Context window | 131K | 1.05M |
| Max output | 16K | 131K |
| Input $ / M tokens | $0.018 | $1.25 |
| Output $ / M tokens | $0.09 | $4.25 |
| Results tracked | 34 | 37 |
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Category by category
Coding Muse Spark 1.3 leads
gpt-oss-20b: 37.6 (#192), Muse Spark 1.3: 56.6 (#21)
| Benchmark | gpt-oss-20b | Muse Spark 1.3 |
|---|---|---|
| SciCode | 34.4% | 59.7% |
| LMArena Coding | 1306 | 1514 |
| CursorBench | — | 41.6% |
| LMArena WebDev | — | 1657 |
| WeirdML | 40.9% | — |
| ALE-Bench | 566.05 | — |
Agentic & Tool Use Muse Spark 1.3 leads
gpt-oss-20b: 9.3 (#154), Muse Spark 1.3: 38.6 (#30)
| Benchmark | gpt-oss-20b | Muse Spark 1.3 |
|---|---|---|
| Terminal-Bench | 3.4% | — |
| APEX-Agents | — | 57.8% |
| GDP.pdf | — | 27.6% |
Reasoning Muse Spark 1.3 leads
gpt-oss-20b: 19.3 (#261), Muse Spark 1.3: 54.0 (#27)
| Benchmark | gpt-oss-20b | Muse Spark 1.3 |
|---|---|---|
| CritPt | 1.4% | 26% |
| Chess Puzzles | 4% | 38% |
| LMArena Hard Prompts | 1274 | 1503 |
| DTBench | 68% | 96.5% |
| LMCA | 14.5% | 53.9% |
| Epoch Capabilities Index | 137.82 | 156.75 |
| Kagi LLM Benchmark | 53.2% | — |
| NYT Connections (extended) | — | 85.1% |
| Mystery Game Puzzles | — | 25% |
| Bench to the Future 3 | — | 0.14 |
Math Muse Spark 1.3 leads
gpt-oss-20b: 39.4 (#103), Muse Spark 1.3: 73.1 (#21)
| Benchmark | gpt-oss-20b | Muse Spark 1.3 |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 65.3% | 99.2% |
| LMArena Math | 1317 | 1494 |
| FrontierMath (Tiers 1-3) | — | 74.4% |
| FrontierMath Tier 4 | — | 46.3% |
| ProofBench | — | 58% |
| Omni-MATH | 56.5% | — |
Knowledge Muse Spark 1.3 leads
gpt-oss-20b: 34.6 (#195), Muse Spark 1.3: 42.6 (#95)
| Benchmark | gpt-oss-20b | Muse Spark 1.3 |
|---|---|---|
| LMArena Expert | 1258 | 1516 |
| GPQA Diamond | 60.8% | — |
| MMLU-Pro | 74% | — |
| GPQA (HELM) | 59.4% | — |
Multimodal Not comparable
gpt-oss-20b: —, Muse Spark 1.3: 43.7 (#22)
| Benchmark | gpt-oss-20b | Muse Spark 1.3 |
|---|---|---|
| LMArena Vision | — | 1309 |
| LMArena Document | — | 1471 |
Multilingual Muse Spark 1.3 leads
gpt-oss-20b: 42.2 (#197), Muse Spark 1.3: 57.4 (#8)
| Benchmark | gpt-oss-20b | Muse Spark 1.3 |
|---|---|---|
| LMArena Non-English | 1268 | 1481 |
| LMArena Chinese | 1314 | 1529 |
| LMArena German | 1255 | 1515 |
| LMArena Japanese | 1244 | 1474 |
| LMArena Korean | 1236 | 1501 |
| LMArena Russian | 1278 | 1490 |
| LMArena Spanish | 1267 | 1490 |
| LMArena French | — | 1524 |
Instruction Following Muse Spark 1.3 leads
gpt-oss-20b: 61.8 (#240), Muse Spark 1.3: 77.5 (#22)
| Benchmark | gpt-oss-20b | Muse Spark 1.3 |
|---|---|---|
| LMArena Instruction Following | 1236 | 1477 |
| IFEval | 73.2% | — |
Long Context Muse Spark 1.3 leads
gpt-oss-20b: 37.9 (#209), Muse Spark 1.3: 45.6 (#32)
| Benchmark | gpt-oss-20b | Muse Spark 1.3 |
|---|---|---|
| LMArena Longer Query | 1250 | 1488 |
Writing & Preference Muse Spark 1.3 leads
gpt-oss-20b: 35.5 (#265), Muse Spark 1.3: 73.6 (#9)
| Benchmark | gpt-oss-20b | Muse Spark 1.3 |
|---|---|---|
| LMArena Text | 1287 | 1490 |
| LMArena Creative Writing | 1201 | 1455 |
| EQ-Bench Creative Writing | 666 | 1906 |
| LMArena Multi-Turn | 1268 | 1482 |
| WildBench | 73.7% | — |
Frequently asked questions
Is gpt-oss-20b better than Muse Spark 1.3?
Muse Spark 1.3 is the stronger model overall, scoring 54.8 to 32.5 on the Noometry Index. gpt-oss-20b costs 56× 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-oss-20b or Muse Spark 1.3?
gpt-oss-20b is cheaper. It lists at $0.018 per million input tokens and $0.09 per million output tokens; Muse Spark 1.3 lists at $1.25 and $4.25.
Is gpt-oss-20b or Muse Spark 1.3 better for coding?
Muse Spark 1.3 scores higher on coding benchmarks: 56.6 versus 37.6 in the Noometry coding category.
Which has the bigger context window?
Muse Spark 1.3 does, with 1.05M tokens against 131K.
How many benchmarks do gpt-oss-20b and Muse Spark 1.3 share?
24 benchmarks have published results for both models. gpt-oss-20b has 34 scored results on Noometry and Muse Spark 1.3 has 37.