GPT-3.5 Turbo Instruct vs Muse Wonder
GPT-3.5 Turbo Instruct costs 10.0x less per output token than Muse Wonder ($2.00 vs $20.00 per million). Updated October 22, 2026; no benchmark has tested both models yet.
- Coding
- No data
- Overall
- No data
- Output $/M
- $2.00
- Coding
- 71.9
- Overall
- No data
- Output $/M
- $20.00
GPT-3.5 Turbo Instruct or Muse Wonder: which to pick
Only Muse Wonder has a coding score, 71.9 on the Vibe Coding Index; GPT-3.5 Turbo Instruct has none, so the two cannot be ranked on coding.
Pick GPT-3.5 Turbo Instruct for output tokens 10.0x cheaper ($2.00 vs $20.00 per million).
No benchmark has tested both yet. GPT-3.5 Turbo Instruct has results on METR time horizons; Muse Wonder on LMArena WebDev, Terminal-Bench 4 (Vals AI) and Vibe Code Bench (Vals AI). Compare them on price and context instead.
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
No call
Only Muse Wonder has a coding score, so no call is made.
One side not measured
Web design
Design Arena: websites
No call
Only Muse Wonder has a Design Arena: websites result, 1363.
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 4K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GPT-3.5 Turbo Instruct
GPT-3.5 Turbo Instruct $0.0025, Muse Wonder $0.014 per turn.
List prices
At a glance
- Coding index
- GPT-3.5 Turbo Instruct not benchmarked, Muse Wonder 71.9 from 3 benchmarks.
- Widest category
- None yet: no category has been tested on both models.
- Widest single gap
- None yet: no benchmark has tested both models.
- Output price
- GPT-3.5 Turbo Instruct $2.00 against Muse Wonder $20.00 per million output tokens, 10.0x apart.
- Context window
- GPT-3.5 Turbo Instruct takes 4K tokens, Muse Wonder 1.05M.
Benchmark by benchmark
Every board either model has a result on, a category to a fold; bars are each result on a 0 to 100 scale. Only one model is measured on each, so no row names a leader.
CodingDirectional onlySix of these boards feed the Vibe Coding Index13 boards measured
| Benchmark | GPT-3.5 Turbo Instruct | Muse Wonder | Leader |
|---|---|---|---|
| LMArena WebDev | Not measured | 1652 | Only Muse Wonder measured |
| Terminal-Bench 4 (Vals AI) | Not measured | 49.5% | Only Muse Wonder measured |
| Vibe Code Bench (Vals AI) | Not measured | 83.1% | Only Muse Wonder measured |
| 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 |
| 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 | Not measured | 1641 | Only Muse Wonder measured |
| LMArena WebDev: HTML | Not measured | 1694 | Only Muse Wonder measured |
| Neither model has a result on the other 30 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index1 board measured
| Benchmark | GPT-3.5 Turbo Instruct | Muse Wonder | Leader |
|---|---|---|---|
| Epoch Capabilities Index | Not measured | 161.8 | Only Muse Wonder measured |
| Neither model has a result on the other 1 board here | |||
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index12 boards measured
| Benchmark | GPT-3.5 Turbo Instruct | Muse Wonder | Leader |
|---|---|---|---|
| Design Arena: spreadsheets | Not measured | 1258 | 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 |
| METR time horizons | 21.5% | Not measured | Only GPT-3.5 Turbo Instruct measured |
| Vending-Bench 2 | Not measured | $9,432 | Only Muse Wonder measured |
| Neither model has a result on the other 30 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index5 boards measured
| Benchmark | GPT-3.5 Turbo Instruct | Muse Wonder | Leader |
|---|---|---|---|
| MysteryMechanism (Vals AI) | Not measured | 27.5% | Only Muse Wonder measured |
| Humanity's Last Exam (Artificial Analysis run) | Not measured | 50.1% | Only Muse Wonder measured |
| 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 18 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index3 boards measured
| Benchmark | GPT-3.5 Turbo Instruct | 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 11 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index4 boards measured
| Benchmark | GPT-3.5 Turbo Instruct | 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 16 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index6 boards measured
| Benchmark | GPT-3.5 Turbo Instruct | Muse Wonder | Leader |
|---|---|---|---|
| Design Arena: websites | Not measured | 1363 | Only Muse Wonder measured |
| Design Arena: UI components | Not measured | 1380 | Only Muse Wonder measured |
| Design Arena: game dev | Not measured | 1379 | Only Muse Wonder measured |
| Design Arena: data viz | Not measured | 1312 | Only Muse Wonder measured |
| Design Arena: 3D | Not measured | 1428 | Only Muse Wonder measured |
| Design Arena: all code categories | Not measured | 1372 | Only Muse Wonder measured |
| Neither model has a result on the other 3 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index17 boards measured
| Benchmark | GPT-3.5 Turbo Instruct | 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 |
| Long context reasoning (AA-LCR) | Not measured | 80.7% | Only Muse Wonder measured |
| 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 31 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-3.5 Turbo Instruct costs less
Repository review
50K input + 3K output tokens
GPT-3.5 Turbo Instruct costs less
Agent loop
200K input + 10K output tokens
GPT-3.5 Turbo Instruct 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-3.5 Turbo Instruct | Meta Muse Wonder |
|---|---|---|
| Maker | OpenAI | Meta |
| Weights | No public weights listed | No public weights listed |
| Released | September 28, 2023 | October 12, 2026 |
| Context window | 4K | 1.05M |
| Input $/Mtok | $1.50 | $4.00 |
| Output $/Mtok | $2.00 | $20.00 |
| Coding index | Not benchmarked | 71.9 |
| Independent measurements behind it | 0 | 3 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GPT-3.5 Turbo Instruct better than Muse Wonder for coding?
Only Muse Wonder has a coding score, 71.9 on the Vibe Coding Index; GPT-3.5 Turbo Instruct has none, so the two cannot be ranked on coding. Pick GPT-3.5 Turbo Instruct for output tokens 10.0x cheaper ($2.00 vs $20.00 per million).
Which is cheaper, GPT-3.5 Turbo Instruct or Muse Wonder?
GPT-3.5 Turbo Instruct costs $1.50 per million input tokens and $2.00 per million output tokens; Muse Wonder costs $4.00 and $20.00. Cost per solved task is known for neither of them.
Which has the bigger context window, GPT-3.5 Turbo Instruct or Muse Wonder?
Muse Wonder, with 1.05M tokens against 4K.
Has any benchmark tested both GPT-3.5 Turbo Instruct and Muse Wonder?
No benchmark has tested both yet. GPT-3.5 Turbo Instruct has results on METR time horizons; Muse Wonder on LMArena WebDev, Terminal-Bench 4 (Vals AI) and Vibe Code Bench (Vals AI). Compare them on price and context instead. Data as of October 22, 2026.