GPT-5.2 vs Muse Spark Open
GPT-5.2 leads Muse Spark Open on coding, 55.9 to 47.4 on the Vibe Coding Index, and Muse Spark Open costs 3.3x less per output token ($4.25 vs $14.00 per million). Updated October 22, 2026; 10 benchmarks have a result for both.
GPT-5.2 or Muse Spark Open: which to pick
Pick GPT-5.2 for coding: it scores 55.9 on the Vibe Coding Index against 47.4 for Muse Spark Open, though on 3 benchmarks against 4.
Pick Muse Spark Open for output tokens 3.3x cheaper ($4.25 vs $14.00 per million) and a larger context window (1.05M against 400K tokens).
- Shared results
- 10
- GPT-5.2 only
- 104
- Muse Spark Open only
- 18
- Like-for-like categories
- 5 of 9
Only benchmarks that tested both models count toward a pick; a result only one model has is shown but not compared.
Which one for your work
Each card is decided by one number both models carry. Where only one has it, the card says so and makes no call.
Coding
Vibe Coding Index, six public coding benchmarks
GPT-5.2
GPT-5.2 scores 55.9 against 47.4.
3 and 4 benchmarks behind the scores
Intelligence
AA Intelligence Index
No call
Only GPT-5.2 has a AA Intelligence Index result, 30.4.
Directional only
Reasoning
GPQA Diamond
No call
Only GPT-5.2 has a GPQA Diamond result, 91.7%.
Directional only
Math
AIME
No call
Only GPT-5.2 has a AIME result, 96.9%.
Directional only
Knowledge
MMLU Pro
No call
Only GPT-5.2 has a MMLU Pro result, 86.2%.
Directional only
Web design
Design Arena: websites
Muse Spark Open
Muse Spark Open leads, 1310 against 1195.
Same board, both measured
General
Vals Index
No call
Only Muse Spark Open has a Vals Index result, 48.6%.
Directional only
Long documents
Listed context window
Muse Spark Open
Muse Spark Open takes 1.05M tokens against 400K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Muse Spark Open
GPT-5.2 $0.0088, Muse Spark Open $0.0034 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only GPT-5.2 has the number, $1.91.
Directional only
At a glance
- Coding index
- GPT-5.2 55.9 from 3 benchmarks, Muse Spark Open 47.4 from 4 benchmarks.
- Widest category
- Coding, 15.0 points apart on average over 4 shared benchmarks.
- Widest single gap
- Vibe Code Bench (Vals AI): GPT-5.2 53.5% against Muse Spark Open 76.2%, 22.7 points apart on a 0 to 100 scale.
- Output price
- GPT-5.2 $14.00 against Muse Spark Open $4.25 per million output tokens, 3.3x apart.
- Context window
- GPT-5.2 takes 400K tokens, Muse Spark Open 1.05M.
Benchmark by benchmark
First the score: boards each model leads, per category, where both have a result. Then every board, a category to a fold; bars are each result on a 0 to 100 scale, so compare the two in a row, not one row with another.
| Category | GPT-5.2 | Bars | Muse Spark Open | Ties |
|---|---|---|---|---|
| Coding | 0 | 4 | ||
| Intelligence | 0 | 1 | ||
| Reasoning | 1 | 1 | ||
| Web design | 0 | 2 | ||
| General | 1 | 0 | ||
| All shared boards | 2 | 8 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index04
| Benchmark | GPT-5.2 | Muse Spark Open | Leader |
|---|---|---|---|
| SWE-bench Verified | 72.8% | Not measured | Only GPT-5.2 measured |
| SWE-bench Multilingual | 66.7% | Not measured | Only GPT-5.2 measured |
| LiveBench coding | Not measured | 73.6% | Only Muse Spark Open measured |
| LMArena WebDev | 1417 | 1528 | Muse Spark Open |
| LiveCodeBench (Vals AI) | 85.4% | Not measured | Only GPT-5.2 measured |
| SWE-bench Verified (Vals AI) | 75.8% | Not measured | Only GPT-5.2 measured |
| Terminal-Bench 4 (Vals AI) | Not measured | 6.6% | Only Muse Spark Open measured |
| Vibe Code Bench (Vals AI) | 53.5% | 76.2% | Muse Spark Open |
| Code migration (Vals AI) | Not measured | 21.2% | Only Muse Spark Open measured |
| LiveCodeBench (Artificial Analysis run) | 89.4% | Not measured | Only GPT-5.2 measured |
| SciCode | Not measured | 58.4% | Only Muse Spark Open measured |
| Terminal-Bench Hard (Artificial Analysis run) | 47.0% | Not measured | Only GPT-5.2 measured |
| Terminal-Bench | 64.9% | Not measured | Only GPT-5.2 measured |
| GSO-Bench | 27.5% | Not measured | Only GPT-5.2 measured |
| WeirdML | 72.2% | Not measured | Only GPT-5.2 measured |
| ALE-Bench | 1293.5 | 1590.1 | Muse Spark Open |
| LMArena Text: coding | 1442 | Not measured | Only GPT-5.2 measured |
| LMArena Text: software and IT | 1434 | Not measured | Only GPT-5.2 measured |
| LMArena WebDev: React | Not measured | 1529 | Only Muse Spark Open measured |
| LMArena WebDev: HTML | 1427 | 1495 | Muse Spark Open |
| Neither model has a result on the other 19 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GPT-5.2 | Muse Spark Open | Leader |
|---|---|---|---|
| AA Intelligence Index | 30.4 | Not measured | Only GPT-5.2 measured |
| Epoch Capabilities Index | 153.7 | 155.1 | Muse Spark Open |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index21 boards, none shared
| Benchmark | GPT-5.2 | Muse Spark Open | Leader |
|---|---|---|---|
| Design Arena: agents building web apps | 1086 | Not measured | Only GPT-5.2 measured |
| Design Arena: agents building full-stack apps | 1043 | Not measured | Only GPT-5.2 measured |
| Design Arena: agents building mobile apps | 1093 | Not measured | Only GPT-5.2 measured |
| Design Arena: agents building Android apps | 1059 | Not measured | Only GPT-5.2 measured |
| Design Arena: agents building Godot games | 1142 | Not measured | Only GPT-5.2 measured |
| LiveBench research | Not measured | 80.3% | Only Muse Spark Open measured |
| LiveBench browser | Not measured | 36.0% | Only Muse Spark Open measured |
| LiveBench long agentic | Not measured | 18.9% | Only Muse Spark Open measured |
| Finance Agent (Vals AI) | Not measured | 61.3% | Only Muse Spark Open measured |
| Tax Agent Bench (Vals AI) | Not measured | 26.3% | Only Muse Spark Open measured |
| Legal Research Bench (Vals AI) | Not measured | 38.5% | Only Muse Spark Open measured |
| Harvey's Legal Agent Benchmark (Vals AI) | Not measured | 25.0% | Only Muse Spark Open measured |
| Excel Modeling Benchmark (Vals AI) | Not measured | 53.7% | Only Muse Spark Open measured |
| τ²-Bench Telecom | 84.8% | Not measured | Only GPT-5.2 measured |
| APEX-Agents | Not measured | 33.4% | Only Muse Spark Open measured |
| METR time horizons | 75.3% | Not measured | Only GPT-5.2 measured |
| Remote Labor Index | 2.5% | Not measured | Only GPT-5.2 measured |
| GDPval | 49.7% | Not measured | Only GPT-5.2 measured |
| DeepResearch Bench | 41.1% | Not measured | Only GPT-5.2 measured |
| Vending-Bench 2 | $3,591 | Not measured | Only GPT-5.2 measured |
| Berkeley Function Calling Leaderboard (BFCL) | 55.9% | Not measured | Only GPT-5.2 measured |
| Neither model has a result on the other 20 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index11
| Benchmark | GPT-5.2 | Muse Spark Open | Leader |
|---|---|---|---|
| GPQA Diamond | 91.7% | Not measured | Only GPT-5.2 measured |
| LiveBench reasoning | Not measured | 73.7% | Only Muse Spark Open measured |
| MMMU Pro (Vals AI) | 86.7% | Not measured | Only GPT-5.2 measured |
| GPQA Diamond (Artificial Analysis run) | 90.3% | 89.0% | GPT-5.2 |
| Humanity's Last Exam (Artificial Analysis run) | 37.7% | 43.9% | Muse Spark Open |
| GPQA Diamond (Epoch AI run) | 91.4% | Not measured | Only GPT-5.2 measured |
| ARC-AGI | 86.2% | Not measured | Only GPT-5.2 measured |
| ARC-AGI-2 | 52.9% | Not measured | Only GPT-5.2 measured |
| SimpleBench | 45.8% | Not measured | Only GPT-5.2 measured |
| Chess puzzles | 48.0% | Not measured | Only GPT-5.2 measured |
| Mystery game puzzles | 23.0% | Not measured | Only GPT-5.2 measured |
| EBR-bench | 23.0% | Not measured | Only GPT-5.2 measured |
| VPCT | 84.0% | Not measured | Only GPT-5.2 measured |
| LMArena Text: hard prompts | 1428 | Not measured | Only GPT-5.2 measured |
| LMArena Text: expert | 1445 | Not measured | Only GPT-5.2 measured |
| LMArena Text: hard prompts in English | 1431 | Not measured | Only GPT-5.2 measured |
| LMArena Vision: diagrams | 1265 | Not measured | Only GPT-5.2 measured |
| LMArena Vision: homework | 1276 | Not measured | Only GPT-5.2 measured |
| Kagi LLM Benchmark | 73.3% | Not measured | Only GPT-5.2 measured |
| Neither model has a result on the other 3 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index10 boards, none shared
| Benchmark | GPT-5.2 | Muse Spark Open | Leader |
|---|---|---|---|
| AIME | 96.9% | Not measured | Only GPT-5.2 measured |
| MGSM (Vals AI) | 94.0% | Not measured | Only GPT-5.2 measured |
| AA Math Index | 99.0 | Not measured | Only GPT-5.2 measured |
| AIME 2025 (Artificial Analysis run) | 99.0% | Not measured | Only GPT-5.2 measured |
| OTIS Mock AIME | 96.1% | Not measured | Only GPT-5.2 measured |
| FrontierMath, tiers 1 to 3 | 67.4% | Not measured | Only GPT-5.2 measured |
| FrontierMath, tier 4 | 31.7% | Not measured | Only GPT-5.2 measured |
| ProofBench | 15.0% | Not measured | Only GPT-5.2 measured |
| LMArena Text: math | 1440 | Not measured | Only GPT-5.2 measured |
| LMArena Text: mathematical work | 1442 | Not measured | Only GPT-5.2 measured |
| Neither model has a result on the other 3 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index15 boards, none shared
| Benchmark | GPT-5.2 | Muse Spark Open | Leader |
|---|---|---|---|
| MMLU Pro | 86.2% | Not measured | Only GPT-5.2 measured |
| LegalBench (Vals AI) | 82.8% | Not measured | Only GPT-5.2 measured |
| CaseLaw (Vals AI) | 66.0% | Not measured | Only GPT-5.2 measured |
| CorpFin (Vals AI) | 65.9% | Not measured | Only GPT-5.2 measured |
| TaxEval (Vals AI) | 75.8% | Not measured | Only GPT-5.2 measured |
| MortgageTax (Vals AI) | 67.1% | Not measured | Only GPT-5.2 measured |
| MedQA (Vals AI) | 94.1% | Not measured | Only GPT-5.2 measured |
| MedCode (Vals AI) | 49.8% | Not measured | Only GPT-5.2 measured |
| MMLU Pro (Artificial Analysis run) | 87.4% | Not measured | Only GPT-5.2 measured |
| SimpleQA Verified | 37.1% | Not measured | Only GPT-5.2 measured |
| LMArena Text: science | 1425 | Not measured | Only GPT-5.2 measured |
| LMArena Text: medicine and healthcare | 1425 | Not measured | Only GPT-5.2 measured |
| LMArena Text: legal and government | 1427 | Not measured | Only GPT-5.2 measured |
| LMArena Text: business and finance | 1414 | Not measured | Only GPT-5.2 measured |
| FACTS Benchmark Suite | 48.5% | Not measured | Only GPT-5.2 measured |
| Neither model has a result on the other 3 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | GPT-5.2 | Muse Spark Open | Leader |
|---|---|---|---|
| Design Arena: websites | 1195 | 1310 | Muse Spark Open |
| Design Arena: UI components | 1190 | Not measured | Only GPT-5.2 measured |
| Design Arena: game dev | 1195 | 1282 | Muse Spark Open |
| Design Arena: data viz | 1202 | Not measured | Only GPT-5.2 measured |
| Design Arena: 3D | 1079 | Not measured | Only GPT-5.2 measured |
| Design Arena: all code categories | 1170 | Not measured | Only GPT-5.2 measured |
| Design Arena: SVG | 1139 | Not measured | Only GPT-5.2 measured |
| Design Arena: ASCII art | 1152 | Not measured | Only GPT-5.2 measured |
| LiveBench design | Not measured | 60.3% | Only Muse Spark Open measured |
GeneralLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GPT-5.2 | Muse Spark Open | Leader |
|---|---|---|---|
| Vals Index | Not measured | 48.6% | Only Muse Spark Open measured |
| LiveBench data analysis | Not measured | 60.2% | Only Muse Spark Open measured |
| SAGE (Vals AI) | 49.3% | Not measured | Only GPT-5.2 measured |
| MedScribe (Vals AI) | 84.4% | Not measured | Only GPT-5.2 measured |
| IFBench | 75.4% | Not measured | Only GPT-5.2 measured |
| Long context reasoning (AA-LCR) | 82.7% | 78.3% | GPT-5.2 |
| DTBench | 90.9% | Not measured | Only GPT-5.2 measured |
| LMCA | 43.9% | Not measured | Only GPT-5.2 measured |
| CL-bench | 18.2% | Not measured | Only GPT-5.2 measured |
| Furniture assembly | 38.3% | Not measured | Only GPT-5.2 measured |
| LMArena Text | 1416 | Not measured | Only GPT-5.2 measured |
| LMArena Text: instruction following | 1409 | Not measured | Only GPT-5.2 measured |
| LMArena Text: creative writing | 1376 | Not measured | Only GPT-5.2 measured |
| LMArena Vision | 1246 | Not measured | Only GPT-5.2 measured |
| LMArena Text: English | 1422 | Not measured | Only GPT-5.2 measured |
| LMArena Text: non-English | 1405 | Not measured | Only GPT-5.2 measured |
| LMArena Text: Chinese | 1447 | Not measured | Only GPT-5.2 measured |
| LMArena Text: German | 1444 | Not measured | Only GPT-5.2 measured |
| LMArena Text: Japanese | 1420 | Not measured | Only GPT-5.2 measured |
| LMArena Text: Korean | 1374 | Not measured | Only GPT-5.2 measured |
| LMArena Text: French | 1454 | Not measured | Only GPT-5.2 measured |
| LMArena Text: Spanish | 1413 | Not measured | Only GPT-5.2 measured |
| LMArena Text: Russian | 1415 | Not measured | Only GPT-5.2 measured |
| LMArena Text: Polish | 1426 | Not measured | Only GPT-5.2 measured |
| LMArena Text: multi-turn | 1419 | Not measured | Only GPT-5.2 measured |
| LMArena Text: longer queries | 1413 | Not measured | Only GPT-5.2 measured |
| LMArena Text: writing and language | 1392 | Not measured | Only GPT-5.2 measured |
| LMArena Text: entertainment and media | 1378 | Not measured | Only GPT-5.2 measured |
| LMArena Vision: English | 1243 | Not measured | Only GPT-5.2 measured |
| LMArena Vision: Chinese | 1305 | Not measured | Only GPT-5.2 measured |
| LMArena Vision: reading text | 1257 | Not measured | Only GPT-5.2 measured |
| LMArena Vision: humor | 1227 | Not measured | Only GPT-5.2 measured |
| LMArena Vision: captioning | 1267 | Not measured | Only GPT-5.2 measured |
| LMArena Vision: recognizing things | 1232 | Not measured | Only GPT-5.2 measured |
| LMArena Vision: creative writing | 1225 | Not measured | Only GPT-5.2 measured |
| LMArena Document | 1405 | Not measured | Only GPT-5.2 measured |
| Neither model has a result on the other 10 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-5.2 | Muse Spark Open | Leader |
|---|---|---|---|
| Neither model has a result on 5 boards here | |||
What each workload costs
Three fixed token mixes at each model's listed price, so per-token rates become a bill.
Chat turn
1K input + 500 output tokens
Muse Spark Open costs less
Repository review
50K input + 3K output tokens
Muse Spark Open costs less
Agent loop
200K input + 10K output tokens
Muse Spark Open costs less
Listed OpenRouter rates, without cache or batch discounts. A workload that does not fit a model's context window would need splitting.
Specification differences
The leading value in a row is marked; a row with a missing value names no leader. Our two rows are marked in red.
| Specification | OpenAI GPT-5.2 | Meta Muse Spark Open |
|---|---|---|
| Maker | OpenAI | Meta |
| Weights | No public weights listed | Open weights |
| Released | December 10, 2025 | October 15, 2026 |
| Context window | 400K | 1.05M |
| Input $/Mtok | $1.75 | $1.25 |
| Output $/Mtok | $14.00 | $4.25 |
| Coding index | 55.9 | 47.4 |
| Independent measurements behind it | 3 | 4 |
| Cost per solved task | $1.91 | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GPT-5.2 better than Muse Spark Open for coding?
Pick GPT-5.2 for coding: it scores 55.9 on the Vibe Coding Index against 47.4 for Muse Spark Open, though on 3 benchmarks against 4. Pick Muse Spark Open for output tokens 3.3x cheaper ($4.25 vs $14.00 per million) and a larger context window (1.05M against 400K tokens).
Which is cheaper, GPT-5.2 or Muse Spark Open?
GPT-5.2 costs $1.75 per million input tokens and $14.00 per million output tokens; Muse Spark Open costs $1.25 and $4.25. Cost per solved task is known for GPT-5.2 only.
Which has the bigger context window, GPT-5.2 or Muse Spark Open?
Muse Spark Open, with 1.05M tokens against 400K.
How many benchmarks is the GPT-5.2 vs Muse Spark Open comparison based on?
10 benchmarks have a result for both models as of October 22, 2026. GPT-5.2's coding index rests on 3 independent benchmarks and Muse Spark Open's on 4. Where only one model has a result, the page says so instead of naming a winner.