Compare / Muse Wonder vs Mistral Large 4
Muse Wonder vs Mistral Large 4
Which is cheaper, which is more capable, and which does better at each kind of work, from 79 results on 5 sources that measured both models.
MetaMuse Wonder
Mistral AIMistral Large 4
Muse Wonder wins 4910 too close to callMistral Large 4 wins 20
Of the 79 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 Mistral AI, every model from both labs.
Muse Wonder or Mistral Large 4? The short answer
Pick Muse Wonder if quality matters most: it scores higher (Spring Prompt overall 70 against 34). Pick Mistral Large 4 if cost matters more: it costs 11 times less per product listing on CatalogBench.
- Spring Prompt overall: Muse Wonder 70, Mistral Large 4 34 (out of 100; how the score works).
- Mistral Large 4 is cheaper to run: $0.003 against $0.036 per product listing on CatalogBench (11 times less).
- Muse Wonder is newer: released 12 Oct 2026, against 6 Oct 2026 for Mistral Large 4.
- On our own benchmarks, on the results both models have: Muse Wonder does better for product listings, decks from an analysis, user surveys and marketing planning.
Head to head
70
Spring Prompt overall
34
$8.00
Price per million tokens, blendedlower is better
$2.06
1,048,576
Context window, tokens
524,288
Every fact side by side
| Muse Wonder | Mistral Large 4 | |
|---|---|---|
| Price per million tokens | $4.00 in · $20.00 out | $1.36 in · $4.18 out |
| Blended (3 in : 1 out) | $8.00 | $2.06 |
| Cheapest host, blended | – | – |
| Cost per product listing (CatalogBench) | $0.036 | $0.003 |
| Cached input, per million | $0.20 cached input | – |
| Intelligence Index | – | 38.4 |
| Spring Prompt overall | 70 | 34 |
| Output speed, tokens a second | – | 106 |
| Context window | 1,048,576 tokens | 524,288 tokens |
| Released | 12 Oct 2026 (newer) | 6 Oct 2026 |
| Weights | Closed | Closed |
| Developer based in | the US | France |
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
19 of 21
Decks from an analysisTurning a finished analysis into a deck you could present as it is
8 of 20
20 of 20
User surveysPlanning a user survey and reading its results without being misled
2 of 9
5 of 9
Marketing planningPlanning a year of ad spend without overspending
10 of 20
11 of 20
Agents and tool useMulti-step tasks with tools: support desks, coding agents, function calls
not measured
21 of 164
Reasoning and knowledgeHard questions across science, maths and general knowledge
not measured
44 of 198
Speed under pressureGood decisions against a real clock (fast chess)
12 of 24
15 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
- AI Checkpoint: Sol’s new fast lane 9 Oct 2026
- Mistral Large 4 Is Fast, Cheap and Europe's Best Model. On Business Work, It Still Makes Things Up. 7 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 | Mistral Large 4 |
|---|---|---|
| BulletBench measured by Spring Prompt · 3 results | ||
| Cost per game · Blitz 3+2US dollars, lower is better 12 · 2 of 14 measured | $0.27 | $0.0082 |
| Games lost on time · Blitz 3+2% of games, lower is better 14 · 1 of 14 measured | 75.0% | 0.0% |
| Invalid moves · Blitz 3+2% of moves, lower is better 1 · 13 of 14 measured | 0.0% | 0.6% |
| CatalogBench measured by Spring Prompt · 3 results | ||
| Cost per productUS dollars, lower is better 17 · 4 of 21 measured | $0.0361 | $0.0033 |
| Reliably publish-ready% of products, higher is better 3 · 19 of 21 measured | 66.1% | 10.7%high reasoning |
| Reliably publish-ready · sales brief% of products, higher is better 4 · 17 of 21 measured | 51.8% | 1.8%high reasoning |
| DeckBench measured by Spring Prompt · 3 results | ||
| Cost per deckUS dollars, lower is better 14 · 4 of 20 measured | $0.18 | $0.0148 |
| 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 · 20 of 20 measured | 1,273 | 560 |
| Accurate decks% of tasks, higher is better 1 · 17 of 20 measured | 100.0% | 0.0% |
| ROASBench measured by Spring Prompt · 3 results | ||
| Overall scorescore out of 100, higher is better 10 · 11 of 20 measured | 38.9 | 37.0 |
| Audience scorescore out of 100, higher is better 12 · 11 of 20 measured | 54.7 | 55.8 |
| Business scorescore out of 100, higher is better 10 · 11 of 20 measured | 34.5 | 34.3 |
| SurveyBench measured by Spring Prompt · 3 results | ||
| Analysis ratingrating, higher is betterThe rating weighs each win by how strong the other analysis was; the win rate counts wins against whichever analyses a model happened to meet, so the two can disagree. 2 · 5 of 9 measured | 1,460 | 797 |
| Cost per taskUS dollars, lower is better 8 · 5 of 9 measured | $0.35 | $0.0924 |
| Analysis scorepoints of 100, higher is better 2 · 6 of 9 measured | 96.2 | 63.8 |
Show all 79 results (64 more)
| Measure | Muse Wonder | Mistral Large 4 |
|---|---|---|
| BulletBench measured by Spring Prompt · 2 results | ||
| Ladder Elo · Blitz 3+2ladder Elo, higher is better 13 · 10 of 14 measured | 358 | 555 |
| Median move time · Blitz 3+2milliseconds, lower is better 12 · 3 of 14 measured | 7.1 s | 0.8 s |
| CatalogBench measured by Spring Prompt · 27 results | ||
| Channel compliance% of products, higher is better 14 · 1 of 21 measured | 99.4% | 100.0% |
| Channel rules broken% of products, lower is better 11 · 20 of 21 measured | 0.6% | 3.0% |
| Channel rules broken · sales brief% of products, lower is better 1 · 16 of 21 measured | 0.0% | 1.2% |
| Claims to check% of products, lower is better 1 · 16 of 21 measured | 0.0% | 0.7%high reasoning |
| Claims to check · sales brief% of products, lower is better 1 · 15 of 21 measured | 0.0% | 3.3%high reasoning |
| Conflicts caught% of conflicts, higher is better 1 · 1 of 21 measured | 100.0% | 100.0% |
| Content quality% of checks, higher is better 3 · 13 of 21 measured | 98.3% | 96.5% |
| Content quality · sales brief% of checks, higher is better 3 · 16 of 21 measured | 98.5% | 95.9% |
| Decision accuracy% of decisions, higher is better 3 · 18 of 21 measured | 98.3% | 93.1% |
| 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 · 1 of 21 measured | 0.0% | 0.0% |
| 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 · 18 of 21 measured | 1.4% | 3.5% |
| 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%high reasoning |
| Publish-ready listings% of products, higher is better 3 · 17 of 21 measured | 75.0% | 39.9%high reasoning |
| Publish-ready listings · sales brief% of products, higher is better 5 · 17 of 21 measured | 67.3% | 10.7%high reasoning |
| Unsupported claimsclaims per product, lower is better 4 · 13 of 21 measured | 0.21 | 0.91high reasoning |
| Unsupported claims% of products, lower is better 4 · 15 of 21 measured | 7.1% | 33.3%high reasoning |
| Unsupported claims · sales brief% of products, lower is better 5 · 16 of 21 measured | 17.9% | 80.4%high reasoning |
| Unsupported claims · sales briefclaims per product, lower is better 5 · 18 of 21 measured | 0.36 | 3.69high reasoning |
| Wrong attributes% of products, lower is better 2 · 14 of 21 measured | 13.1% | 19.6%high reasoning |
| Wrong attributes · sales brief% of products, lower is better 4 · 10 of 21 measured | 14.3% | 17.3%high reasoning |
| Wrong category or variant% of products, lower is better 10 · 2 of 21 measured | 2.4% | 1.2% |
| Wrong category or variant · sales brief% of products, lower is better 10 · 4 of 21 measured | 2.4% | 1.8% |
| DeckBench measured by Spring Prompt · 13 results | ||
| Caveat dropped% of tasks, lower is better 1 · 11 of 20 measured | 0.0% | 16.7% |
| 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 · 20 of 20 measured | 67.1% | 30.5% |
| Draft figure quoted% of tasks, lower is better 1 · 9 of 20 measured | 0.0% | 16.7% |
| 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. 10 · 20 of 20 measured | 63.2% | 21.7% |
| 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 · 19 of 20 measured | 0.0% | 83.3% |
| Slides needing work% of tasks, lower is better 15 · 20 of 20 measured | 66.7% | 100.0% |
| Unsupported claims% of tasks, lower is better 1 · 18 of 20 measured | 0.0% | 50.0% |
| Unsupported numbers% of tasks, lower is better 1 · 20 of 20 measured | 0.0% | 33.3% |
| ROASBench measured by Spring Prompt · 6 results | ||
| Consistency scorescore out of 100, higher is better 12 · 15 of 20 measured | 25.9 | 16.7 |
| Contribution profitsimulated US dollars, higher is better 11 · 9 of 20 measured | $489,176 | $533,401 |
| Cost of a runUS dollars, lower is better 15 · 5 of 20 measured | $0.57 | $0.0657 |
| Months over budgetmonths of 12, lower is better 1 · 1 of 20 measured | 0 | 0high reasoning |
| Planning scorescore out of 100, higher is better 4 · 3 of 20 measured | 55.0 | 55.0high reasoning |
| Return on ad spendprofit per $1 spent, higher is better 11 · 9 of 20 measured | $0.41 | $0.45 |
| SurveyBench measured by Spring Prompt · 16 results | ||
| Head-to-head win rate% of comparisons, higher is betterThe rating weighs each win by how strong the other analysis was; the win rate counts wins against whichever analyses a model happened to meet, so the two can disagree. 3 · 5 of 9 measured | 67.9% | 39.9% |
| Leading or double-barrelled questions% of tasks, lower is better 5 · 3 of 9 measured | 44.4% | 33.3% |
| Mistaken next steps% of tasks, lower is better 1 · 8 of 9 measured | 0.0% | 66.7% |
| Numbers right% of answers, higher is better 1 · 6 of 9 measured | 100.0% | 98.9% |
| Objectives unanswerable% of tasks, lower is better 5 · 5 of 9 measured | 11.1% | 11.1% |
| Precision ignored% of tasks, lower is better 4 · 5 of 9 measured | 22.2% | 55.6% |
| Research scorepoints of 100, higher is better 3 · 6 of 9 measured | 93.6 | 71.2 |
| Sound research% of tasks, higher is better 3 · 5 of 9 measured | 33.3% | 0.0% |
| Stakeholder's view presumed% of tasks, lower is better 4 · 5 of 9 measured | 11.1% | 22.2% |
| Survey plan scorepoints of 100, higher is better 5 · 4 of 9 measured | 87.8 | 88.4 |
| Survey structure% of tasks, lower is better 8 · 2 of 9 measured | 33.3% | 11.1% |
| Traps handled% of traps, higher is better 1 · 5 of 9 measured | 100.0% | 78.7% |
| Traps missed% of tasks, lower is better 1 · 7 of 9 measured | 0.0% | 100.0% |
| Unsupported findings% of tasks, lower is better 2 · 7 of 9 measured | 11.1% | 100.0% |
| Wrong numbers% of tasks, lower is better 1 · 6 of 9 measured | 0.0% | 22.2% |
| Wrong recommendation% of tasks, lower is better 1 · 6 of 9 measured | 0.0% | 44.4% |
Compare them with other models
- Meta vs Mistral 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
- Mistral Large 4 vs Muse Spark 1.3Most intelligent at $1–3 per million tokens
- Mistral Large 4 vs DeepSeek-V4.1-FlashFastest with similar intelligence
- Mistral Large 4 vs Claude Opus 5.5Most intelligent available today
Quick answers
Is Muse Wonder better than Mistral Large 4?
Pick Muse Wonder if quality matters most: it scores higher (Spring Prompt overall 70 against 34). Pick Mistral Large 4 if cost matters more: it costs 11 times less per product listing on CatalogBench.
Which is cheaper, Muse Wonder or Mistral Large 4?
Mistral Large 4 is cheaper to run: $0.003 against $0.036 per product listing on CatalogBench (11 times less).
Which is better for product listings, Muse Wonder or Mistral Large 4?
Muse Wonder ranks 4 of 21 models on sale for turning a sparse product feed and photos into listings that can go live, against 19 for Mistral Large 4.
Which is better for decks from an analysis, Muse Wonder or Mistral Large 4?
Muse Wonder ranks 8 of 20 models on sale for turning a finished analysis into a deck you could present as it is, against 20 for Mistral Large 4.
Which is better for user surveys, Muse Wonder or Mistral Large 4?
Muse Wonder ranks 2 of 9 models on sale for planning a user survey and reading its results without being misled, against 5 for Mistral Large 4.
Which is better for marketing planning, Muse Wonder or Mistral Large 4?
Muse Wonder ranks 10 of 20 models on sale for planning a year of ad spend without overspending, against 11 for Mistral Large 4.
Which is better for speed under pressure, Muse Wonder or Mistral Large 4?
Muse Wonder ranks 12 of 24 models on sale for good decisions against a real clock (fast chess), against 15 for Mistral Large 4.
Which has the bigger context window, Muse Wonder or Mistral Large 4?
Muse Wonder: 1,048,576 tokens, against 524,288 for Mistral Large 4.
Run Muse Wonder and Mistral Large 4 on your own prompt
Benchmarks aren't your data. Try both side by side in the playground, with the cost of every answer.