Llama 3.3 70B Instruct vs Muse Wonder
Llama 3.3 70B Instruct costs 27.8x less per output token than Muse Wonder ($0.72 vs $20.00 per million). Updated October 22, 2026; 28 benchmarks have a result for both.
Llama 3.3 70B Instruct or Muse Wonder: which to pick
Only Muse Wonder has a coding score, 71.9 on the Vibe Coding Index; Llama 3.3 70B Instruct has none, so the two cannot be ranked on coding.
Pick Llama 3.3 70B Instruct for output tokens 27.8x cheaper ($0.72 vs $20.00 per million).
- Shared results
- 28
- Llama 3.3 70B Instruct only
- 24
- Muse Wonder only
- 32
- Like-for-like categories
- 6 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
No call
Only Muse Wonder has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
No call
Only Llama 3.3 70B Instruct has a AA Intelligence Index result, 7.7.
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 131K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Llama 3.3 70B Instruct
Llama 3.3 70B Instruct $0.0008, Muse Wonder $0.014 per turn.
List prices
At a glance
- Coding index
- Llama 3.3 70B Instruct not benchmarked, Muse Wonder 71.9 from 3 benchmarks.
- Widest category
- Reasoning, 43.5 points apart on average over 4 shared benchmarks.
- Widest single gap
- Long context reasoning (AA-LCR): Llama 3.3 70B Instruct 15.7% against Muse Wonder 80.7%, 65.0 points apart on a 0 to 100 scale.
- Output price
- Llama 3.3 70B Instruct $0.72 against Muse Wonder $20.00 per million output tokens, 27.8x apart.
- Context window
- Llama 3.3 70B Instruct takes 131K 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 | Llama 3.3 70B Instruct | Bars | Muse Wonder | Ties |
|---|---|---|---|---|
| Coding | 0 | 2 | ||
| Intelligence | 0 | 1 | ||
| Reasoning | 0 | 4 | ||
| Math | 0 | 2 | ||
| Knowledge | 0 | 4 | ||
| General | 0 | 15 | ||
| All shared boards | 0 | 28 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index02
| Benchmark | Llama 3.3 70B Instruct | Muse Wonder | Leader |
|---|---|---|---|
| LMArena WebDev | Not measured | 1652 | Only Muse Wonder measured |
| AA Coding Index | 11.9 | Not measured | Only Llama 3.3 70B Instruct 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 |
| LiveCodeBench (Artificial Analysis run) | 28.8% | Not measured | Only Llama 3.3 70B Instruct measured |
| SciCode | Not measured | 60.3% | Only Muse Wonder measured |
| Terminal-Bench Hard (Artificial Analysis run) | 3.0% | Not measured | Only Llama 3.3 70B Instruct measured |
| WeirdML | 14.4% | Not measured | Only Llama 3.3 70B Instruct measured |
| LMArena Text: coding | 1268 | 1522 | Muse Wonder |
| LMArena Text: software and IT | 1273 | 1515 | Muse Wonder |
| 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 22 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Llama 3.3 70B Instruct | Muse Wonder | Leader |
|---|---|---|---|
| AA Intelligence Index | 7.7 | Not measured | Only Llama 3.3 70B Instruct measured |
| Epoch Capabilities Index | 127.3 | 161.8 | Muse Wonder |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index14 boards, none shared
| Benchmark | Llama 3.3 70B Instruct | Muse Wonder | Leader |
|---|---|---|---|
| Design Arena: spreadsheets | Not measured | 1243 | 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 | 26.6% | Not measured | Only Llama 3.3 70B Instruct measured |
| BALROG | 23.0% | Not measured | Only Llama 3.3 70B Instruct measured |
| Vending-Bench 2 | Not measured | $9,432 | Only Muse Wonder measured |
| Berkeley Function Calling Leaderboard (BFCL) | 31.9% | Not measured | Only Llama 3.3 70B Instruct measured |
| Neither model has a result on the other 27 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index04
| Benchmark | Llama 3.3 70B Instruct | Muse Wonder | Leader |
|---|---|---|---|
| MysteryMechanism (Vals AI) | Not measured | 27.5% | Only Muse Wonder measured |
| GPQA Diamond (Artificial Analysis run) | 49.8% | Not measured | Only Llama 3.3 70B Instruct measured |
| Humanity's Last Exam (Artificial Analysis run) | 3.6% | 50.1% | Muse Wonder |
| GPQA Diamond (Epoch AI run) | 47.4% | Not measured | Only Llama 3.3 70B Instruct measured |
| SimpleBench | 19.9% | Not measured | Only Llama 3.3 70B Instruct measured |
| LMArena Text: hard prompts | 1257 | 1506 | Muse Wonder |
| LMArena Text: expert | 1225 | 1513 | Muse Wonder |
| LMArena Text: hard prompts in English | 1277 | 1506 | Muse Wonder |
| Neither model has a result on the other 14 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | Llama 3.3 70B Instruct | Muse Wonder | Leader |
|---|---|---|---|
| AA Math Index | 7.7 | Not measured | Only Llama 3.3 70B Instruct measured |
| AIME 2025 (Artificial Analysis run) | 7.7% | Not measured | Only Llama 3.3 70B Instruct measured |
| OTIS Mock AIME | 5.1% | Not measured | Only Llama 3.3 70B Instruct measured |
| ProofBench | Not measured | 84.0% | Only Muse Wonder measured |
| MATH level 5 | 41.6% | Not measured | Only Llama 3.3 70B Instruct measured |
| LMArena Text: math | 1267 | 1486 | Muse Wonder |
| LMArena Text: mathematical work | 1267 | 1514 | Muse Wonder |
| Neither model has a result on the other 6 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index04
| Benchmark | Llama 3.3 70B Instruct | Muse Wonder | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | 71.3% | Not measured | Only Llama 3.3 70B Instruct measured |
| MMLU | 86.3% | Not measured | Only Llama 3.3 70B Instruct measured |
| LMArena Text: science | 1284 | 1506 | Muse Wonder |
| LMArena Text: medicine and healthcare | 1275 | 1512 | Muse Wonder |
| LMArena Text: legal and government | 1284 | 1505 | Muse Wonder |
| LMArena Text: business and finance | 1243 | 1483 | Muse Wonder |
| Neither model has a result on the other 12 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
| Benchmark | Llama 3.3 70B Instruct | Muse Wonder | Leader |
|---|---|---|---|
| Design Arena: websites | Not measured | 1362 | Only Muse Wonder measured |
| Design Arena: UI components | Not measured | 1379 | Only Muse Wonder measured |
| Design Arena: game dev | Not measured | 1376 | Only Muse Wonder measured |
| Design Arena: data viz | Not measured | 1317 | Only Muse Wonder measured |
| Design Arena: 3D | Not measured | 1427 | 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 index015
| Benchmark | Llama 3.3 70B 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 |
| IFBench | 47.1% | Not measured | Only Llama 3.3 70B Instruct measured |
| Long context reasoning (AA-LCR) | 15.7% | 80.7% | Muse Wonder |
| DTBench | 59.5% | 95.2% | Muse Wonder |
| LMCA | 17.5% | 56.8% | Muse Wonder |
| Fiction.LiveBench | 33.3% | Not measured | Only Llama 3.3 70B Instruct measured |
| LMArena Text | 1274 | 1502 | Muse Wonder |
| LMArena Text: instruction following | 1242 | 1491 | Muse Wonder |
| LMArena Text: creative writing | 1250 | 1491 | Muse Wonder |
| LMArena Text: English | 1303 | 1507 | Muse Wonder |
| LMArena Text: non-English | 1236 | 1492 | Muse Wonder |
| LMArena Text: Chinese | 1217 | 1537 | Muse Wonder |
| LMArena Text: German | 1251 | Not measured | Only Llama 3.3 70B Instruct measured |
| LMArena Text: Japanese | 1150 | Not measured | Only Llama 3.3 70B Instruct measured |
| LMArena Text: Korean | 1142 | Not measured | Only Llama 3.3 70B Instruct measured |
| LMArena Text: French | 1282 | 1523 | Muse Wonder |
| LMArena Text: Spanish | 1269 | Not measured | Only Llama 3.3 70B Instruct measured |
| LMArena Text: Russian | 1252 | 1522 | Muse Wonder |
| LMArena Text: Polish | 1259 | Not measured | Only Llama 3.3 70B Instruct measured |
| LMArena Text: multi-turn | 1280 | 1510 | Muse Wonder |
| LMArena Text: longer queries | 1255 | 1493 | Muse Wonder |
| LMArena Text: writing and language | 1255 | 1496 | Muse Wonder |
| LMArena Text: entertainment and media | 1241 | 1482 | Muse Wonder |
| Neither model has a result on the other 22 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Llama 3.3 70B Instruct | 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
Llama 3.3 70B Instruct costs less
Repository review
50K input + 3K output tokens
Llama 3.3 70B Instruct costs less
Agent loop
200K input + 10K output tokens
Llama 3.3 70B 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 | Meta Llama 3.3 70B Instruct | Meta Muse Wonder |
|---|---|---|
| Maker | Meta Llama | Meta |
| Weights | Open weights | No public weights listed |
| Released | December 6, 2024 | October 12, 2026 |
| Context window | 131K | 1.05M |
| Input $/Mtok | $0.45 | $4.00 |
| Output $/Mtok | $0.72 | $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 Llama 3.3 70B Instruct better than Muse Wonder for coding?
Only Muse Wonder has a coding score, 71.9 on the Vibe Coding Index; Llama 3.3 70B Instruct has none, so the two cannot be ranked on coding. Pick Llama 3.3 70B Instruct for output tokens 27.8x cheaper ($0.72 vs $20.00 per million).
Which is cheaper, Llama 3.3 70B Instruct or Muse Wonder?
Llama 3.3 70B Instruct costs $0.45 per million input tokens and $0.72 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, Llama 3.3 70B Instruct or Muse Wonder?
Muse Wonder, with 1.05M tokens against 131K.
How many benchmarks is the Llama 3.3 70B Instruct vs Muse Wonder comparison based on?
28 benchmarks have a result for both models as of October 22, 2026. Llama 3.3 70B Instruct's coding index rests on 0 independent benchmarks and Muse Wonder's on 3. Where only one model has a result, the page says so instead of naming a winner.