GPT-5.2-Codex vs Muse Wonder
Muse Wonder leads GPT-5.2-Codex on coding, 71.9 to 45.4 on the Vibe Coding Index, and GPT-5.2-Codex costs 1.4x less per output token ($14.00 vs $20.00 per million). Updated October 21, 2026; 6 benchmarks have a result for both.
GPT-5.2-Codex or Muse Wonder: which to pick
Pick Muse Wonder for coding: it scores 71.9 on the Vibe Coding Index against 45.4 for GPT-5.2-Codex.
Pick GPT-5.2-Codex for output tokens 1.4x cheaper ($14.00 vs $20.00 per million) and work measured by Long context reasoning (AA-LCR), where it leads.
- Shared results
- 6
- GPT-5.2-Codex only
- 16
- Muse Wonder only
- 54
- Like-for-like categories
- 3 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
Muse Wonder
Muse Wonder scores 71.9 against 45.4.
3 benchmarks behind each score
Intelligence
AA Intelligence Index
No call
Only GPT-5.2-Codex has a AA Intelligence Index result, 28.5.
Directional only
Web design
Design Arena: websites
No call
Only Muse Wonder has a Design Arena: websites result, 1362.
Directional only
General
Vals Index
No call
Only Muse Wonder has a Vals Index result, 64.9%.
Directional only
Long documents
Listed context window
Muse Wonder
Muse Wonder takes 1.05M tokens against 400K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GPT-5.2-Codex
GPT-5.2-Codex $0.0088, Muse Wonder $0.014 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only GPT-5.2-Codex has the number, $0.43.
Directional only
At a glance
- Coding index
- GPT-5.2-Codex 45.4 from 3 benchmarks, Muse Wonder 71.9 from 3 benchmarks.
- Widest category
- Coding, 44.2 points apart on average over 4 shared benchmarks.
- Widest single gap
- Vibe Code Bench (Vals AI): GPT-5.2-Codex 37.9% against Muse Wonder 83.1%, 45.2 points apart on a 0 to 100 scale.
- Output price
- GPT-5.2-Codex $14.00 against Muse Wonder $20.00 per million output tokens, 1.4x apart.
- Context window
- GPT-5.2-Codex takes 400K tokens, Muse Wonder 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-Codex | Bars | Muse Wonder | Ties |
|---|---|---|---|---|
| Coding | 0 | 4 | ||
| Reasoning | 0 | 1 | ||
| General | 1 | 0 | ||
| All shared boards | 1 | 5 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index04
| Benchmark | GPT-5.2-Codex | Muse Wonder | Leader |
|---|---|---|---|
| SWE-bench Verified | 72.8% | Not measured | Only GPT-5.2-Codex measured |
| SWE-bench Multilingual | 66.3% | Not measured | Only GPT-5.2-Codex measured |
| LMArena WebDev | 1339 | 1652 | Muse Wonder |
| LiveCodeBench (Vals AI) | 88.0% | Not measured | Only GPT-5.2-Codex measured |
| SWE-bench Verified (Vals AI) | 72.4% | Not measured | Only GPT-5.2-Codex measured |
| Terminal-Bench 4 (Vals AI) | Not measured | 49.5% | Only Muse Wonder measured |
| Vibe Code Bench (Vals AI) | 37.9% | 83.1% | Muse Wonder |
| Code migration (Vals AI) | Not measured | 62.5% | Only Muse Wonder measured |
| IOI (Vals AI) | Not measured | 66.9% | Only Muse Wonder measured |
| SWE-bench Multimodal (Vals AI) | Not measured | 45.6% | Only Muse Wonder measured |
| Vibe Code Bench 1-100 (Vals AI) | Not measured | 26.6% | Only Muse Wonder measured |
| CyberBench (Vals AI) | Not measured | 80.4% | Only Muse Wonder measured |
| SciCode | Not measured | 60.3% | Only Muse Wonder measured |
| Terminal-Bench Hard (Artificial Analysis run) | 37.1% | Not measured | Only GPT-5.2-Codex measured |
| Terminal-Bench | 66.5% | Not measured | Only GPT-5.2-Codex measured |
| ALE-Bench | 1299.9 | Not measured | Only GPT-5.2-Codex measured |
| LMArena Text: coding | Not measured | 1522 | Only Muse Wonder measured |
| LMArena Text: software and IT | Not measured | 1515 | Only Muse Wonder measured |
| LMArena WebDev: React | 1329 | 1641 | Muse Wonder |
| LMArena WebDev: HTML | 1348 | 1694 | Muse Wonder |
| Neither model has a result on the other 19 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | GPT-5.2-Codex | Muse Wonder | Leader |
|---|---|---|---|
| AA Intelligence Index | 28.5 | Not measured | Only GPT-5.2-Codex measured |
| Epoch Capabilities Index | Not measured | 161.8 | Only Muse Wonder measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index17 boards, none shared
| Benchmark | GPT-5.2-Codex | Muse Wonder | Leader |
|---|---|---|---|
| Design Arena: agents building web apps | 1051 | Not measured | Only GPT-5.2-Codex measured |
| Design Arena: agents building full-stack apps | 993 | Not measured | Only GPT-5.2-Codex measured |
| Design Arena: agents building mobile apps | 1090 | Not measured | Only GPT-5.2-Codex measured |
| Design Arena: agents building Android apps | 1176 | Not measured | Only GPT-5.2-Codex measured |
| Design Arena: agents building Godot games | 1142 | Not measured | Only GPT-5.2-Codex measured |
| Design Arena: spreadsheets | Not measured | 1227 | Only Muse Wonder measured |
| Finance Agent (Vals AI) | Not measured | 56.6% | Only Muse Wonder measured |
| Tax Agent Bench (Vals AI) | Not measured | 36.3% | Only Muse Wonder measured |
| Legal Research Bench (Vals AI) | Not measured | 50.5% | Only Muse Wonder measured |
| Harvey's Legal Agent Benchmark (Vals AI) | Not measured | 23.8% | Only Muse Wonder measured |
| Excel Modeling Benchmark (Vals AI) | Not measured | 67.1% | Only Muse Wonder measured |
| BioMysteryBench (Vals AI) | Not measured | 67.0% | Only Muse Wonder measured |
| Terminal-Bench-Science (Vals AI) | Not measured | 22.9% | Only Muse Wonder measured |
| Vals Science Index | Not measured | 39.1% | Only Muse Wonder measured |
| CUA-bench (Vals AI) | Not measured | 7.2% | Only Muse Wonder measured |
| τ²-Bench Telecom | 92.1% | Not measured | Only GPT-5.2-Codex measured |
| Vending-Bench 2 | Not measured | $9,432 | Only Muse Wonder measured |
| Neither model has a result on the other 24 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GPT-5.2-Codex | Muse Wonder | Leader |
|---|---|---|---|
| MysteryMechanism (Vals AI) | Not measured | 27.5% | Only Muse Wonder measured |
| GPQA Diamond (Artificial Analysis run) | 89.9% | Not measured | Only GPT-5.2-Codex measured |
| Humanity's Last Exam (Artificial Analysis run) | 35.7% | 50.1% | Muse Wonder |
| LMArena Text: hard prompts | Not measured | 1506 | Only Muse Wonder measured |
| LMArena Text: expert | Not measured | 1513 | Only Muse Wonder measured |
| LMArena Text: hard prompts in English | Not measured | 1506 | Only Muse Wonder measured |
| Neither model has a result on the other 16 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index3 boards, none shared
| Benchmark | GPT-5.2-Codex | Muse Wonder | Leader |
|---|---|---|---|
| ProofBench | Not measured | 84.0% | Only Muse Wonder measured |
| LMArena Text: math | Not measured | 1486 | Only Muse Wonder measured |
| LMArena Text: mathematical work | Not measured | 1514 | Only Muse Wonder measured |
| Neither model has a result on the other 10 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index4 boards, none shared
| Benchmark | GPT-5.2-Codex | Muse Wonder | Leader |
|---|---|---|---|
| LMArena Text: science | Not measured | 1506 | Only Muse Wonder measured |
| LMArena Text: medicine and healthcare | Not measured | 1512 | Only Muse Wonder measured |
| LMArena Text: legal and government | Not measured | 1505 | Only Muse Wonder measured |
| LMArena Text: business and finance | Not measured | 1483 | Only Muse Wonder measured |
| Neither model has a result on the other 14 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
| Benchmark | GPT-5.2-Codex | Muse Wonder | Leader |
|---|---|---|---|
| Design Arena: websites | Not measured | 1362 | Only Muse Wonder measured |
| Design Arena: UI components | Not measured | 1374 | Only Muse Wonder measured |
| Design Arena: game dev | Not measured | 1377 | Only Muse Wonder measured |
| Design Arena: data viz | Not measured | 1321 | Only Muse Wonder measured |
| Design Arena: 3D | Not measured | 1426 | Only Muse Wonder measured |
| Design Arena: all code categories | Not measured | 1371 | Only Muse Wonder measured |
| Neither model has a result on the other 3 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GPT-5.2-Codex | Muse Wonder | Leader |
|---|---|---|---|
| Vals Index | Not measured | 64.9% | Only Muse Wonder measured |
| Public Benefits Bench (Vals AI) | Not measured | 70.2% | Only Muse Wonder measured |
| IFBench | 77.6% | Not measured | Only GPT-5.2-Codex measured |
| Long context reasoning (AA-LCR) | 82.3% | 80.7% | GPT-5.2-Codex |
| DTBench | Not measured | 95.2% | Only Muse Wonder measured |
| LMCA | Not measured | 56.8% | Only Muse Wonder measured |
| LMArena Text | Not measured | 1502 | Only Muse Wonder measured |
| LMArena Text: instruction following | Not measured | 1491 | Only Muse Wonder measured |
| LMArena Text: creative writing | Not measured | 1491 | Only Muse Wonder measured |
| LMArena Text: English | Not measured | 1507 | Only Muse Wonder measured |
| LMArena Text: non-English | Not measured | 1492 | Only Muse Wonder measured |
| LMArena Text: Chinese | Not measured | 1537 | Only Muse Wonder measured |
| LMArena Text: French | Not measured | 1523 | Only Muse Wonder measured |
| LMArena Text: Russian | Not measured | 1522 | Only Muse Wonder measured |
| LMArena Text: multi-turn | Not measured | 1510 | Only Muse Wonder measured |
| LMArena Text: longer queries | Not measured | 1493 | Only Muse Wonder measured |
| LMArena Text: writing and language | Not measured | 1496 | Only Muse Wonder measured |
| LMArena Text: entertainment and media | Not measured | 1482 | Only Muse Wonder measured |
| Neither model has a result on the other 28 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-5.2-Codex | Muse Wonder | 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
GPT-5.2-Codex costs less
Repository review
50K input + 3K output tokens
GPT-5.2-Codex costs less
Agent loop
200K input + 10K output tokens
GPT-5.2-Codex 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-Codex | Meta Muse Wonder |
|---|---|---|
| Maker | OpenAI | Meta |
| Weights | No public weights listed | No public weights listed |
| Released | January 14, 2026 | October 12, 2026 |
| Context window | 400K | 1.05M |
| Input $/Mtok | $1.75 | $4.00 |
| Output $/Mtok | $14.00 | $20.00 |
| Coding index | 45.4 | 71.9 |
| Independent measurements behind it | 3 | 3 |
| Cost per solved task | $0.43 | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GPT-5.2-Codex better than Muse Wonder for coding?
Pick Muse Wonder for coding: it scores 71.9 on the Vibe Coding Index against 45.4 for GPT-5.2-Codex. Pick GPT-5.2-Codex for output tokens 1.4x cheaper ($14.00 vs $20.00 per million) and work measured by Long context reasoning (AA-LCR), where it leads.
Which is cheaper, GPT-5.2-Codex or Muse Wonder?
GPT-5.2-Codex costs $1.75 per million input tokens and $14.00 per million output tokens; Muse Wonder costs $4.00 and $20.00. Cost per solved task is known for GPT-5.2-Codex only.
Which has the bigger context window, GPT-5.2-Codex or Muse Wonder?
Muse Wonder, with 1.05M tokens against 400K.
How many benchmarks is the GPT-5.2-Codex vs Muse Wonder comparison based on?
6 benchmarks have a result for both models as of October 21, 2026. GPT-5.2-Codex's coding index rests on 3 independent benchmarks and Muse Wonder's on 3. Where only one model has a result, the page says so instead of naming a winner.