Compare / Muse Wonder vs Kimi K3
Muse Wonder vs Kimi K3
Which is cheaper, which is more capable, and which does better at each kind of work, from 60 results on 4 sources that measured both models.
MetaMuse Wonder
Moonshot AIKimi K3
Muse Wonder wins 3215 too close to callKimi K3 wins 13
Of the 60 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 Meta vs Moonshot AI, every model from both labs.
Muse Wonder or Kimi K3? The short answer
Pick Muse Wonder: it scores higher (Spring Prompt overall 70 against 49) and costs less.
- Spring Prompt overall: Muse Wonder 70, Kimi K3 49 (out of 100; how the score works).
- Muse Wonder is cheaper to run: $0.036 against $0.047 per product listing on CatalogBench (23% less).
- Muse Wonder is newer: released 12 Oct 2026, against 16 Jul 2026 for Kimi K3.
- On our own benchmarks, on the results both models have: Muse Wonder does better for product listings; Kimi K3 for marketing planning.
Head to head
70
Spring Prompt overall
49
$8.00
Price per million tokens, blendedlower is better
$6.00
1,048,576
Context window, tokens
1,048,576
Every fact side by side
| Muse Wonder | Kimi K3 | |
|---|---|---|
| Price per million tokens | $4.00 in · $20.00 out | $3.00 in · $15.00 out |
| Blended (3 in : 1 out) | $8.00 | $6.00 |
| Cheapest host, blended | – | $3.70 (Morph, FP8) |
| Cost per product listing (CatalogBench) | $0.036 | $0.047 |
| Cached input, per million | $0.20 cached input | $0.30 cached input |
| Intelligence Index | – | 43.6 |
| Spring Prompt overall | 70 | 49 |
| Output speed, tokens a second | – | 45 |
| Context window | 1,048,576 tokens | 1,048,576 tokens |
| Released | 12 Oct 2026 (newer) | 16 Jul 2026 |
| Weights | Closed | Open |
| Developer based in | the US | China |
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.
Product listingsTurning a sparse product feed and photos into listings that can go live
4 of 21
14 of 21
Decks from an analysisTurning a finished analysis into a deck you could present as it is
8 of 20
10 of 20
User surveysPlanning a user survey and reading its results without being misled
2 of 9
not measured
Marketing planningPlanning a year of ad spend without overspending
10 of 20
8 of 20
Agents and tool useMulti-step tasks with tools: support desks, coding agents, function calls
not measured
34 of 164
Professional workReal tasks from banking, consulting and law, business documents and freelance projects
not measured
24 of 51
Reasoning and knowledgeHard questions across science, maths and general knowledge
not measured
15 of 198
WritingWhat people prefer in blind comparisons, and judged writing quality
not measured
11 of 188
Sticking to the factsSummarising without inventing things, and factual answers
not measured
28 of 152
Speed under pressureGood decisions against a real clock (fast chess)
12 of 24
21 of 24
Their slides, side by side
The same brief for both: Manchester store investment review for Hearthside Coffee, an invented company. First slide of each deck from DeckBench.
From our research
- Claude Opus 5.5 vs Muse Wonder Full: Eight Crafts, and Opus Still Wins Most 13 Oct 2026
- Muse Wonder review: good analysis, work that still needs checking 13 Oct 2026
- We Told 11 AI Models What Happened in 2026. Most of Them Thought We Were Making It Up. 12 Oct 2026
- We Tested the Anatomy Claude Code Skill on Everyday Objects and Seven AI Models 12 Oct 2026
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 | Muse Wonder | Kimi K3 |
|---|---|---|
| BulletBench measured by Spring Prompt · 3 results | ||
| Cost per game · Bullet 60sUS dollars, lower is better 28 · 27 of 29 measured | $0.0663minimal reasoning | $0.0540low reasoning |
| Ladder Elo · Bullet 60sladder Elo, higher is better 19 · 26 of 29 measured | 409minimal reasoning | 33low reasoning |
| Games lost on time · Bullet 60s% of games, lower is better 23 · 28 of 29 measured | 58.3%minimal reasoning | 91.7%low reasoning |
| CatalogBench measured by Spring Prompt · 3 results | ||
| Cost per productUS dollars, lower is better 17 · 20 of 21 measured | $0.0361 | $0.0469 |
| Reliably publish-ready% of products, higher is better 3 · 10 of 21 measured | 66.1% | 41.1% |
| Reliably publish-ready · sales brief% of products, higher is better 4 · 17 of 21 measured | 51.8% | 1.8% |
| DeckBench measured by Spring Prompt · 3 results | ||
| Cost per deckUS dollars, lower is better 14 · 18 of 20 measured | $0.18 | $0.45 |
| 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. 8 · 10 of 20 measured | 1,273 | 1,230 |
| Accurate decks% of tasks, higher is better 1 · 9 of 20 measured | 100.0% | 50.0% |
| ROASBench measured by Spring Prompt · 3 results | ||
| Overall scorescore out of 100, higher is better 10 · 8 of 20 measured | 38.9 | 44.1 |
| Audience scorescore out of 100, higher is better 12 · 8 of 20 measured | 54.7 | 65.1 |
| Business scorescore out of 100, higher is better 10 · 8 of 20 measured | 34.5 | 40.2 |
Show all 60 results (48 more)
| Measure | Muse Wonder | Kimi K3 |
|---|---|---|
| BulletBench measured by Spring Prompt · 2 results | ||
| Invalid moves · Bullet 60s% of moves, lower is better 1 · 1 of 29 measured | 0.0%minimal reasoning | 0.0%low reasoning |
| Median move time · Bullet 60smilliseconds, lower is better 25 · 27 of 29 measured | 1.9 sminimal reasoning | 2.6 slow reasoning |
| CatalogBench measured by Spring Prompt · 27 results | ||
| Channel compliance% of products, higher is better 14 · 14 of 21 measured | 99.4% | 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 1 · 10 of 21 measured | 0.0% | 0.6% |
| 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 1 · 1 of 21 measured | 0.0% | 0.0% |
| Conflicts caught% of conflicts, higher is better 1 · 19 of 21 measured | 100.0% | 97.0% |
| Content quality% of checks, higher is better 3 · 15 of 21 measured | 98.3% | 96.4% |
| Content quality · sales brief% of checks, higher is better 3 · 13 of 21 measured | 98.5% | 96.4% |
| Decision accuracy% of decisions, higher is better 3 · 12 of 21 measured | 98.3% | 96.1% |
| Failed outputs% of products, lower is better 1 · 1 of 21 measured | 0.0% | 0.0% |
| Failed outputs · sales brief% of products, lower is better 1 · 18 of 21 measured | 0.0% | 0.6% |
| Field accuracy% of missing fields, higher is better 1 · 4 of 21 measured | 96.1% | 94.4% |
| Invented values% of filled values, lower is better 11 · 20 of 21 measured | 1.4% | 5.7% |
| 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 · 7 of 21 measured | 3.6% | 4.2% |
| Not findable · sales brief% of products, lower is better 8 · 6 of 21 measured | 3.6% | 3.0% |
| Publish-ready listings% of products, higher is better 3 · 10 of 21 measured | 75.0% | 61.3% |
| Publish-ready listings · sales brief% of products, higher is better 5 · 15 of 21 measured | 67.3% | 14.9% |
| Unsupported claimsclaims per product, lower is better 4 · 14 of 21 measured | 0.21 | 0.98 |
| Unsupported claims% of products, lower is better 4 · 9 of 21 measured | 7.1% | 16.7% |
| Unsupported claims · sales briefclaims per product, lower is better 5 · 17 of 21 measured | 0.36 | 3.64 |
| Unsupported claims · sales brief% of products, lower is better 5 · 17 of 21 measured | 17.9% | 81.6% |
| Wrong attributes% of products, lower is better 2 · 18 of 21 measured | 13.1% | 24.4% |
| Wrong attributes · sales brief% of products, lower is better 4 · 17 of 21 measured | 14.3% | 20.8% |
| Wrong category or variant% of products, lower is better 10 · 13 of 21 measured | 2.4% | 3.0% |
| Wrong category or variant · sales brief% of products, lower is better 10 · 18 of 21 measured | 2.4% | 4.2% |
| DeckBench measured by Spring Prompt · 13 results | ||
| Caveat dropped% of tasks, lower is better 1 · 1 of 20 measured | 0.0% | 0.0% |
| Clean layout% of tasks, higher is better 18 · 13 of 20 measured | 16.7% | 33.3% |
| Design quality% of the maximum, higher is better 9 · 3 of 20 measured | 67.1% | 81.4% |
| Draft figure quoted% of tasks, lower is better 1 · 1 of 20 measured | 0.0% | 0.0% |
| Findings missing% of tasks, lower is better 1 · 15 of 20 measured | 0.0% | 16.7% |
| 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. 10 · 7 of 20 measured | 63.2% | 70.8% |
| Layout defects% of tasks, lower is better 18 · 13 of 20 measured | 83.3% | 66.7% |
| Misleading metric used% of tasks, lower is better 1 · 1 of 20 measured | 0.0% | 0.0% |
| Presentable decks% of tasks, higher is better 10 · 12 of 20 measured | 16.7% | 0.0% |
| Recommendation late or wrong% of tasks, lower is better 1 · 11 of 20 measured | 0.0% | 33.3% |
| Slides needing work% of tasks, lower is better 15 · 2 of 20 measured | 66.7% | 16.7% |
| Unsupported claims% of tasks, lower is better 1 · 14 of 20 measured | 0.0% | 33.3% |
| 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 12 · 10 of 20 measured | 25.9 | 28.3 |
| Contribution profitsimulated US dollars, higher is better 11 · 5 of 20 measured | $489,176 | $654,270 |
| Cost of a runUS dollars, lower is better 15 · 17 of 20 measured | $0.57 | $1.01 |
| Months over budgetmonths of 12, lower is better 1 · 1 of 20 measured | 0 | 0 |
| Planning scorescore out of 100, higher is better 4 · 13 of 20 measured | 55.0 | 54.7 |
| Return on ad spendprofit per $1 spent, higher is better 11 · 8 of 20 measured | $0.41 | $0.50 |
Compare them with other models
- Meta vs Moonshot AIEvery model from both labs, best against best
- 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
- Kimi K3 vs Claude Opus 5.5Most intelligent at $3–10 per million tokens
- Kimi K3 vs Claude Haiku 5.5Cheapest with similar intelligence
Quick answers
Is Muse Wonder better than Kimi K3?
Pick Muse Wonder: it scores higher (Spring Prompt overall 70 against 49) and costs less.
Which is cheaper, Muse Wonder or Kimi K3?
Muse Wonder is cheaper to run: $0.036 against $0.047 per product listing on CatalogBench (23% less).
Which is better for product listings, Muse Wonder or Kimi K3?
Muse Wonder ranks 4 of 21 models on sale for turning a sparse product feed and photos into listings that can go live, against 14 for Kimi K3.
Which is better for decks from an analysis, Muse Wonder or Kimi K3?
Muse Wonder ranks 8 of 20 models on sale for turning a finished analysis into a deck you could present as it is, against 10 for Kimi K3.
Which is better for marketing planning, Muse Wonder or Kimi K3?
Kimi K3 ranks 8 of 20 models on sale for planning a year of ad spend without overspending, against 10 for Muse Wonder.
Which is better for speed under pressure, Muse Wonder or Kimi K3?
Muse Wonder ranks 12 of 24 models on sale for good decisions against a real clock (fast chess), against 21 for Kimi K3.
Run Muse Wonder and Kimi K3 on your own prompt
Benchmarks aren't your data. Try both side by side in the playground, with the cost of every answer.