Compare / Muse Ember vs Kimi K3.1 Pro
Muse Ember vs Kimi K3.1 Pro
Which is cheaper, which is more capable, and which does better at each kind of work, from 83 results on 5 sources that measured both models.
MetaMuse Ember
Moonshot AIKimi K3.1 Pro
Muse Ember wins 3221 too close to callKimi K3.1 Pro wins 30
Of the 83 results both models have, the better value on each. Results whose reported ranges overlap are too close to call.
Results as of 13 November 2026 (catalogue release 2026-11-13-a1171a342f96). See also Meta vs Moonshot AI, every model from both labs.
Muse Ember or Kimi K3.1 Pro? The short answer
They score about the same, so pick Muse Ember: it costs 22 times less per product listing on CatalogBench.
- Spring Prompt overall: Muse Ember 65, Kimi K3.1 Pro 68 (out of 100; how the score works).
- Muse Ember is cheaper to run: $0.002 against $0.045 per product listing on CatalogBench (22 times less).
- Muse Ember is newer: released 9 Nov 2026, against 23 Oct 2026 for Kimi K3.1 Pro.
- On our own benchmarks, on the results both models have: Muse Ember does better for user surveys; Kimi K3.1 Pro for marketing planning.
Head to head
65
Spring Prompt overall
68
$0.20
Price per million tokens, blendedlower is better
$3.81
1,048,576
Context window, tokens
1,048,576
Every fact side by side
| Muse Ember | Kimi K3.1 Pro | |
|---|---|---|
| Price per million tokens | $0.10 in · $0.50 out | $1.75 in · $10.00 out |
| Blended (3 in : 1 out) | $0.20 | $3.81 |
| Cheapest host, blended | – | $2.38 (Relace, FP4) |
| Cost per product listing (CatalogBench) | $0.002 | $0.045 |
| Cached input, per million | $0.004 cached input | – |
| Intelligence Index | – | – |
| Spring Prompt overall | 65 | 68 |
| Output speed, tokens a second | – | – |
| Context window | 1,048,576 tokens | 1,048,576 tokens |
| Released | 9 Nov 2026 (newer) | 23 Oct 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
10 of 31
not measured
Decks from an analysisTurning a finished analysis into a deck you could present as it is
15 of 31
14 of 31
User surveysPlanning a user survey and reading its results without being misled
5 of 22
19 of 22
Marketing planningPlanning a year of ad spend without overspending
8 of 31
4 of 31
Agents and tool useMulti-step tasks with tools: support desks, coding agents, function calls
not measured
1 of 159
Reasoning and knowledgeHard questions across science, maths and general knowledge
not measured
9 of 187
Speed under pressureGood decisions against a real clock (fast chess)
21 of 34
26 of 34
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
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 Ember | Kimi K3.1 Pro |
|---|---|---|
| BulletBench measured by Spring Prompt · 3 results | ||
| Cost per game · Bullet 60sUS dollars, lower is better 30 · 47 of 58 measured | $0.0038minimal reasoning | $0.0175low reasoning |
| Ladder Elo · Bullet 60sladder Elo, higher is better 47 · 51 of 58 measured | 310minimal reasoning | 147low reasoning |
| Games lost on time · Bullet 60s% of games, lower is better 45 · 54 of 58 measured | 62.5%minimal reasoning | 87.5%low reasoning |
| CatalogBench measured by Spring Prompt · 3 results | ||
| Cost per productUS dollars, lower is better 6 · 29 of 32 measured | $0.0020 | $0.0454 |
| Channel compliance% of products, higher is better 1 · 1 of 32 measured | 100.0% | 100.0% |
| Channel rules broken% of products, lower is better 1 · 1 of 32 measured | 0.0% | 0.0% |
| DeckBench measured by Spring Prompt · 3 results | ||
| Cost per deckUS dollars, lower is better 5 · 29 of 32 measured | $0.0112 | $0.54 |
| 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. 15 · 14 of 32 measured | 1,169 | 1,212 |
| Accurate decks% of tasks, higher is better 9 · 9 of 32 measured | 66.7% | 66.7% |
| ROASBench measured by Spring Prompt · 3 results | ||
| Overall scorescore out of 100, higher is better 8 · 4 of 32 measured | 51.8 | 55.1 |
| Audience scorescore out of 100, higher is better 20 · 8 of 32 measured | 60.6 | 67.6 |
| Business scorescore out of 100, higher is better 7 · 3 of 32 measured | 53.1 | 58.2 |
| 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. 5 · 20 of 23 measured | 1,393 | 620 |
| Cost per taskUS dollars, lower is better 6 · 22 of 23 measured | $0.0213 | $0.74 |
| Analysis scorepoints of 100, higher is better 11 · 22 of 23 measured | 84.3 | 14.5 |
Show all 83 results (68 more)
| Measure | Muse Ember | Kimi K3.1 Pro |
|---|---|---|
| BulletBench measured by Spring Prompt · 2 results | ||
| Invalid moves · Bullet 60s% of moves, lower is better 1 · 1 of 58 measured | 0.0% | 0.0%low reasoning |
| Median move time · Bullet 60smilliseconds, lower is better 44 · 58 of 58 measured | 1.8 sminimal reasoning | 5.0 slow reasoning |
| CatalogBench measured by Spring Prompt · 27 results | ||
| Channel rules broken · sales brief% of products, lower is better 1 · 1 of 32 measured | 0.0% | 0.0% |
| Claims to check% of products, lower is better 1 · 1 of 30 measured | 0.0% | 0.0% |
| Claims to check · sales brief% of products, lower is better 1 · 1 of 30 measured | 0.0% | 0.0% |
| Conflicts caught% of conflicts, higher is better 1 · 1 of 32 measured | 100.0% | 100.0% |
| Content quality% of checks, higher is better 25 · 1 of 32 measured | 95.6% | 98.9% |
| Content quality · sales brief% of checks, higher is better 4 · 26 of 32 measured | 98.5% | 95.3% |
| Decision accuracy% of decisions, higher is better 22 · 1 of 32 measured | 95.8% | 98.9% |
| Failed outputs% of products, lower is better 30 · 1 of 32 measured | 2.4% | 0.0% |
| Failed outputs · sales brief% of products, lower is better 1 · 30 of 32 measured | 0.0% | 3.6% |
| Field accuracy% of missing fields, higher is better 15 · 3 of 32 measured | 93.7% | 95.6% |
| Invented values% of filled values, lower is better 20 · 23 of 32 measured | 1.7% | 2.1% |
| Missing UK information% of products, lower is better 1 · 1 of 32 measured | 0.0% | 0.0% |
| Missing UK information · sales brief% of products, lower is better 1 · 1 of 32 measured | 0.0% | 0.0% |
| Not findable% of products, lower is better 11 · 19 of 32 measured | 3.6% | 5.4% |
| Not findable · sales brief% of products, lower is better 16 · 16 of 32 measured | 3.6% | 3.6% |
| Output tokens per producttokens, lower is better 9 · 10 of 10 measured | 3,816 | 3,996 |
| Publish-ready listings% of products, higher is better 9 · 2 of 32 measured | 71.4% | 78.6% |
| Publish-ready listings · sales brief% of products, higher is better 11 · 2 of 32 measured | 54.2% | 75.0% |
| Time per productseconds, lower is better 5 · 32 of 32 measured | 8.10 | 114 |
| Unsupported claims% of products, lower is better 7 · 1 of 32 measured | 7.1% | 0.0% |
| Unsupported claimsclaims per product, lower is better 9 · 1 of 32 measured | 0.29 | 0.00 |
| Unsupported claims · sales briefclaims per product, lower is better 10 · 5 of 32 measured | 0.69 | 0.21 |
| Unsupported claims · sales brief% of products, lower is better 12 · 1 of 32 measured | 33.3% | 1.8% |
| Wrong attributes% of products, lower is better 7 · 15 of 32 measured | 13.7% | 16.1% |
| Wrong attributes · sales brief% of products, lower is better 10 · 5 of 32 measured | 14.9% | 14.3% |
| Wrong category or variant% of products, lower is better 6 · 6 of 32 measured | 1.8% | 1.8% |
| Wrong category or variant · sales brief% of products, lower is better 3 · 29 of 32 measured | 0.6% | 5.4% |
| DeckBench measured by Spring Prompt · 15 results | ||
| Caveat dropped% of tasks, lower is better 31 · 15 of 32 measured | 33.3% | 16.7% |
| Clean layout% of tasks, higher is better 28 · 28 of 32 measured | 0.0% | 0.0% |
| Design quality% of the maximum, higher is better 23 · 3 of 32 measured | 56.7% | 83.9% |
| Draft figure quoted% of tasks, lower is better 13 · 1 of 32 measured | 16.7% | 0.0% |
| Findings missing% of tasks, lower is better 22 · 30 of 32 measured | 16.7% | 33.3% |
| 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. 17 · 11 of 32 measured | 50.0% | 69.4% |
| Layout defects% of tasks, lower is better 28 · 28 of 32 measured | 100.0% | 100.0% |
| Misleading metric used% of tasks, lower is better 28 · 1 of 32 measured | 16.7% | 0.0% |
| Output tokens per decktokens, lower is better 5 · 11 of 11 measured | 22,094 | 51,739 |
| Presentable decks% of tasks, higher is better 15 · 15 of 32 measured | 0.0% | 0.0% |
| Recommendation late or wrong% of tasks, lower is better 19 · 1 of 32 measured | 33.3% | 0.0% |
| Slides needing work% of tasks, lower is better 16 · 1 of 32 measured | 50.0% | 0.0% |
| Time per deckseconds, lower is better 4 · 32 of 32 measured | 30.2 | 1,288 |
| Unsupported claims% of tasks, lower is better 15 · 1 of 32 measured | 16.7% | 0.0% |
| Unsupported numbers% of tasks, lower is better 26 · 1 of 32 measured | 16.7% | 0.0% |
| ROASBench measured by Spring Prompt · 6 results | ||
| Consistency scorescore out of 100, higher is better 4 · 7 of 32 measured | 38.6 | 36.3 |
| Contribution profitsimulated US dollars, higher is better 15 · 4 of 32 measured | $621,558 | $858,023 |
| Cost of a runUS dollars, lower is better 4 · 30 of 32 measured | $0.0297 | $1.60 |
| Months over budgetmonths of 12, lower is better 1 · 1 of 32 measured | 0 | 0 |
| Planning scorescore out of 100, higher is better 10 · 4 of 32 measured | 55.0 | 55.0 |
| Return on ad spendprofit per $1 spent, higher is better 5 · 2 of 32 measured | $0.70 | $0.72 |
| SurveyBench measured by Spring Prompt · 18 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. 6 · 17 of 23 measured | 62.2% | 37.3% |
| Leading or double-barrelled questions% of tasks, lower is better 9 · 7 of 23 measured | 33.3% | 22.2% |
| Mistaken next steps% of tasks, lower is better 10 · 21 of 23 measured | 22.2% | 77.8% |
| Numbers right% of answers, higher is better 1 · 1 of 23 measured | 100.0% | 100.0% |
| Objectives unanswerable% of tasks, lower is better 1 · 1 of 23 measured | 0.0% | 0.0% |
| Output tokens per tasktokens, lower is better 8 · 13 of 13 measured | 40,298 | 70,935 |
| Precision ignored% of tasks, lower is better 12 · 1 of 23 measured | 11.1% | 0.0% |
| Research scorepoints of 100, higher is better 11 · 22 of 23 measured | 86.9 | 39.2 |
| Sound research% of tasks, higher is better 8 · 14 of 23 measured | 22.2% | 0.0% |
| Stakeholder's view presumed% of tasks, lower is better 1 · 1 of 23 measured | 0.0% | 0.0% |
| Survey plan scorepoints of 100, higher is better 9 · 3 of 23 measured | 92.8 | 97.0 |
| Survey structure% of tasks, lower is better 11 · 3 of 23 measured | 22.2% | 11.1% |
| Time per taskseconds, lower is better 3 · 23 of 23 measured | 73.2 | 1,956 |
| Traps handled% of traps, higher is better 3 · 10 of 23 measured | 98.4% | 93.3% |
| Traps missed% of tasks, lower is better 3 · 15 of 23 measured | 22.2% | 88.9% |
| Unsupported findings% of tasks, lower is better 5 · 13 of 23 measured | 22.2% | 77.8% |
| Wrong numbers% of tasks, lower is better 13 · 19 of 23 measured | 11.1% | 77.8% |
| Wrong recommendation% of tasks, lower is better 12 · 23 of 23 measured | 22.2% | 100.0% |
Compare them with other models
- Meta vs Moonshot AIEvery model from both labs, best against best
- Muse Ember vs Claude Fable 5.2Highest-scoring overall available today
- Muse Ember vs Gemini 4 HydrogenJust below overall
- Muse Ember vs Muse WonderMeta's best other model
- Kimi K3.1 Pro vs Claude Opus 5.5Highest-scoring overall at $3–10 per million tokens
- Kimi K3.1 Pro vs Claude Fable 5.2Highest-scoring overall available today
- Kimi K3.1 Pro vs GPT-6 SolJust above overall
Quick answers
Is Muse Ember better than Kimi K3.1 Pro?
They score about the same, so pick Muse Ember: it costs 22 times less per product listing on CatalogBench.
Which is cheaper, Muse Ember or Kimi K3.1 Pro?
Muse Ember is cheaper to run: $0.002 against $0.045 per product listing on CatalogBench (22 times less).
Which is better for decks from an analysis, Muse Ember or Kimi K3.1 Pro?
Kimi K3.1 Pro ranks 14 of 31 models on sale for turning a finished analysis into a deck you could present as it is, against 15 for Muse Ember.
Which is better for user surveys, Muse Ember or Kimi K3.1 Pro?
Muse Ember ranks 5 of 22 models on sale for planning a user survey and reading its results without being misled, against 19 for Kimi K3.1 Pro.
Which is better for marketing planning, Muse Ember or Kimi K3.1 Pro?
Kimi K3.1 Pro ranks 4 of 31 models on sale for planning a year of ad spend without overspending, against 8 for Muse Ember.
Which is better for speed under pressure, Muse Ember or Kimi K3.1 Pro?
Muse Ember ranks 21 of 34 models on sale for good decisions against a real clock (fast chess), against 26 for Kimi K3.1 Pro.
Run Muse Ember and Kimi K3.1 Pro on your own prompt.
Benchmarks aren't your task. Test both side by side on your prompt, with every answer checked against your rules and the cost of each one.