Compare / Qwen3.8-Max (0902) vs Muse Wonder
Qwen3.8-Max (0902) vs Muse Wonder
Which is cheaper, which is more capable, and which does better at each kind of work, from 55 results on 3 sources that measured both models.
Of the 55 results both models have, the better value on each. Results whose reported ranges overlap are too close to call.
Results as of 13 October 2026 (catalogue release 2026-10-13-2e2420e3c5cc). See also Alibaba vs Meta, every model from both labs.
Qwen3.8-Max (0902) or Muse Wonder? The short answer
Pick Muse Wonder if quality matters most: it scores higher (Spring Prompt overall 70 against 47). Pick Qwen3.8-Max (0902) if cost matters more: it costs 29% less per product listing on CatalogBench.
- Spring Prompt overall: Qwen3.8-Max (0902) 47, Muse Wonder 70 (out of 100; how the score works).
- Qwen3.8-Max (0902) is cheaper to run: $0.026 against $0.036 per product listing on CatalogBench (29% less).
- Muse Wonder is newer: released 12 Oct 2026, against 3 Aug 2026 for Qwen3.8-Max (0902).
- On our own benchmarks, on the results both models have: Muse Wonder does better for product listings and marketing planning.
Head to head
Every fact side by side
| Qwen3.8-Max (0902) | Muse Wonder | |
|---|---|---|
| Price per million tokens | $2.00 in · $6.00 out | $4.00 in · $20.00 out |
| Blended (3 in : 1 out) | $3.00 | $8.00 |
| Cheapest host, blended | – | – |
| Cost per product listing (CatalogBench) | $0.026 | $0.036 |
| Cached input, per million | – | $0.20 cached input |
| Intelligence Index | 45.4 | – |
| Spring Prompt overall | 47 | 70 |
| Output speed, tokens a second | 37 | – |
| Context window | 1,000,000 tokens | 1,048,576 tokens |
| Released | 3 Aug 2026 | 12 Oct 2026 (newer) |
| Weights | Closed | Closed |
| Developer based in | China | the US |
Prices are the developer's list price where we have read it, otherwise the typical host on OpenRouter; see every model's price. Intelligence and speed from Artificial Analysis.
Which is better for what
Each model's rank among the models on sale today, for each kind of work, from our own benchmarks and licensed sources. Further right is better; the better rank is in green.
From our research
Every shared result
Each model's best result on every measure both have, grouped by benchmark, with the setting that produced it. Under each measure, every model that benchmark has measured is a grey tick, best to the right, so you can see where the two sit in the field. The headline measures of each benchmark come first; the rest are under "Show all". Green marks the better value; where a source reports ranges that overlap, neither is marked.
| Measure | Qwen3.8-Max (0902) | Muse Wonder |
|---|---|---|
| CatalogBench measured by Spring Prompt · 3 results | ||
| Cost per productUS dollars, lower is better 15 · 17 of 21 measured | $0.0256 | $0.0361 |
| Reliably publish-ready% of products, higher is better 14 · 3 of 21 measured | 28.6% | 66.1% |
| Reliably publish-ready · sales brief% of products, higher is better 19 · 4 of 21 measured | 0.0% | 51.8% |
| DeckBench measured by Spring Prompt · 3 results | ||
| Cost per deckUS dollars, lower is better 11 · 14 of 20 measured | $0.15 | $0.18 |
| Deck ratingrating, higher is betterThe rating weighs each win by how strong the other deck was; the win rate counts wins against whichever decks a model happened to meet, so the two can disagree. 12 · 8 of 20 measured | 1,076 | 1,273 |
| Accurate decks% of tasks, higher is better 9 · 1 of 20 measured | 50.0% | 100.0% |
| ROASBench measured by Spring Prompt · 3 results | ||
| Overall scorescore out of 100, higher is better 15 · 10 of 20 measured | 25.4 | 38.9 |
| Audience scorescore out of 100, higher is better 18 · 12 of 20 measured | 39.7 | 54.7 |
| Business scorescore out of 100, higher is better 15 · 10 of 20 measured | 15.6 | 34.5 |
Show all 55 results (46 more)
| Measure | Qwen3.8-Max (0902) | Muse Wonder |
|---|---|---|
| CatalogBench measured by Spring Prompt · 27 results | ||
| Channel compliance% of products, higher is better 1 · 14 of 21 measured | 100.0% | 99.4% |
| Channel rules broken% of products, lower is better 11 · 11 of 21 measured | 0.6% | 0.6% |
| Channel rules broken · sales brief% of products, lower is better 20 · 1 of 21 measured | 3.0% | 0.0% |
| Claims to check% of products, lower is better 1 · 1 of 21 measured | 0.0% | 0.0% |
| Claims to check · sales brief% of products, lower is better 14 · 1 of 21 measured | 1.3% | 0.0% |
| Conflicts caught% of conflicts, higher is better 1 · 1 of 21 measured | 100.0% | 100.0% |
| Content quality% of checks, higher is better 19 · 3 of 21 measured | 94.2% | 98.3% |
| Content quality · sales brief% of checks, higher is better 20 · 3 of 21 measured | 93.6% | 98.5% |
| Decision accuracy% of decisions, higher is better 17 · 3 of 21 measured | 93.3% | 98.3% |
| Failed outputs% of products, lower is better 20 · 1 of 21 measured | 3.6% | 0.0% |
| Failed outputs · sales brief% of products, lower is better 20 · 1 of 21 measured | 3.6% | 0.0% |
| Field accuracy% of missing fields, higher is better 20 · 1 of 21 measured | 90.3% | 96.1% |
| Invented values% of filled values, lower is better 16 · 11 of 21 measured | 2.7% | 1.4% |
| Missing UK information% of products, lower is better 1 · 1 of 21 measured | 0.0% | 0.0% |
| Missing UK information · sales brief% of products, lower is better 1 · 1 of 21 measured | 0.0% | 0.0% |
| Not findable% of products, lower is better 4 · 4 of 21 measured | 3.6% | 3.6% |
| Not findable · sales brief% of products, lower is better 2 · 8 of 21 measured | 1.8% | 3.6% |
| Publish-ready listings% of products, higher is better 13 · 3 of 21 measured | 50.0% | 75.0% |
| Publish-ready listings · sales brief% of products, higher is better 19 · 5 of 21 measured | 7.7% | 67.3% |
| Unsupported claimsclaims per product, lower is better 11 · 4 of 21 measured | 0.69 | 0.21 |
| Unsupported claims% of products, lower is better 11 · 4 of 21 measured | 26.8% | 7.1% |
| Unsupported claims · sales briefclaims per product, lower is better 20 · 5 of 21 measured | 4.52 | 0.36 |
| Unsupported claims · sales brief% of products, lower is better 20 · 5 of 21 measured | 84.5% | 17.9% |
| Wrong attributes% of products, lower is better 17 · 2 of 21 measured | 21.4% | 13.1% |
| Wrong attributes · sales brief% of products, lower is better 14 · 4 of 21 measured | 19.6% | 14.3% |
| Wrong category or variant% of products, lower is better 4 · 10 of 21 measured | 1.8% | 2.4% |
| Wrong category or variant · sales brief% of products, lower is better 10 · 10 of 21 measured | 2.4% | 2.4% |
| DeckBench measured by Spring Prompt · 13 results | ||
| Caveat dropped% of tasks, lower is better 11 · 1 of 20 measured | 16.7% | 0.0% |
| Clean layout% of tasks, higher is better 6 · 18 of 20 measured | 66.7% | 16.7% |
| Design quality% of the maximum, higher is better 10 · 9 of 20 measured | 67.0% | 67.1% |
| Draft figure quoted% of tasks, lower is better 12 · 1 of 20 measured | 33.3% | 0.0% |
| Findings missing% of tasks, lower is better 1 · 1 of 20 measured | 0.0% | 0.0% |
| Head-to-head win rate% of comparisons, higher is betterThe rating weighs each win by how strong the other deck was; the win rate counts wins against whichever decks a model happened to meet, so the two can disagree. 11 · 10 of 20 measured | 61.3% | 63.2% |
| Layout defects% of tasks, lower is better 6 · 18 of 20 measured | 33.3% | 83.3% |
| Misleading metric used% of tasks, lower is better 1 · 1 of 20 measured | 0.0% | 0.0% |
| Presentable decks% of tasks, higher is better 5 · 10 of 20 measured | 33.3% | 16.7% |
| Recommendation late or wrong% of tasks, lower is better 1 · 1 of 20 measured | 0.0% | 0.0% |
| Slides needing work% of tasks, lower is better 10 · 15 of 20 measured | 50.0% | 66.7% |
| Unsupported claims% of tasks, lower is better 11 · 1 of 20 measured | 16.7% | 0.0% |
| Unsupported numbers% of tasks, lower is better 1 · 1 of 20 measured | 0.0% | 0.0% |
| ROASBench measured by Spring Prompt · 6 results | ||
| Consistency scorescore out of 100, higher is better 14 · 12 of 20 measured | 19.3 | 25.9 |
| Contribution profitsimulated US dollars, higher is better 15 · 11 of 20 measured | $99,775 | $489,176 |
| Cost of a runUS dollars, lower is better 16 · 15 of 20 measured | $0.73 | $0.57 |
| Months over budgetmonths of 12, lower is better 18 · 1 of 20 measured | 2 | 0 |
| Planning scorescore out of 100, higher is better 10 · 4 of 20 measured | 54.8 | 55.0 |
| Return on ad spendprofit per $1 spent, higher is better 15 · 11 of 20 measured | $0.09 | $0.41 |
Compare them with other models
- Alibaba vs MetaEvery model from both labs, best against best
- Qwen3.8-Max (0902) vs Claude Opus 5.5Most intelligent at $3–10 per million tokens
- Qwen3.8-Max (0902) vs Claude Haiku 5.5Cheapest with similar intelligence
- Muse Wonder vs Claude Opus 5.5Highest-scoring overall at $3–10 per million tokens
- Muse Wonder vs GPT-6 AstraJust above overall
- Muse Wonder vs GPT-6 SolJust below overall
- Muse Wonder vs Muse Spark 1.3Meta's best other model
Quick answers
Is Qwen3.8-Max (0902) better than Muse Wonder?
Pick Muse Wonder if quality matters most: it scores higher (Spring Prompt overall 70 against 47). Pick Qwen3.8-Max (0902) if cost matters more: it costs 29% less per product listing on CatalogBench.
Which is cheaper, Qwen3.8-Max (0902) or Muse Wonder?
Qwen3.8-Max (0902) is cheaper to run: $0.026 against $0.036 per product listing on CatalogBench (29% less).
Which is better for product listings, Qwen3.8-Max (0902) or Muse Wonder?
Muse Wonder ranks 4 of 21 models on sale for turning a sparse product feed and photos into listings that can go live, against 18 for Qwen3.8-Max (0902).
Which is better for decks from an analysis, Qwen3.8-Max (0902) or Muse Wonder?
Muse Wonder ranks 8 of 20 models on sale for turning a finished analysis into a deck you could present as it is, against 12 for Qwen3.8-Max (0902).
Which is better for marketing planning, Qwen3.8-Max (0902) or Muse Wonder?
Muse Wonder ranks 10 of 20 models on sale for planning a year of ad spend without overspending, against 15 for Qwen3.8-Max (0902).
Which is better for speed under pressure, Qwen3.8-Max (0902) or Muse Wonder?
Muse Wonder ranks 12 of 25 models on sale for good decisions against a real clock (fast chess), against 25 for Qwen3.8-Max (0902).
Which has the bigger context window, Qwen3.8-Max (0902) or Muse Wonder?
Muse Wonder: 1,048,576 tokens, against 1,000,000 for Qwen3.8-Max (0902).
Run Qwen3.8-Max (0902) and Muse Wonder on your own prompt
Benchmarks aren't your data. Try both side by side in the playground, with the cost of every answer.