Compare / Muse Ember vs GPT-6 Luna
Muse Ember vs GPT-6 Luna
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
OpenAIGPT-6 Luna
Muse Ember wins 2220 too close to callGPT-6 Luna wins 41
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 10 November 2026 (catalogue release 2026-11-10-8342a7178929). See also Meta vs OpenAI, every model from both labs.
Muse Ember or GPT-6 Luna? The short answer
Pick Muse Ember if quality matters most: it scores higher (Spring Prompt overall 65 against 39). Pick GPT-6 Luna if cost matters more: it costs 4.3 times less per product listing on CatalogBench.
- Spring Prompt overall: Muse Ember 65, GPT-6 Luna 39 (out of 100; how the score works).
- GPT-6 Luna is cheaper to run: $0.000 against $0.002 per product listing on CatalogBench (4.3 times less).
- Muse Ember is newer: released 9 Nov 2026, against 22 Sep 2026 for GPT-6 Luna.
- On our own benchmarks, on the results both models have: Muse Ember does better for user surveys; GPT-6 Luna for product listings and marketing planning.
Head to head
65
Spring Prompt overall
39
$0.20
Price per million tokens, blendedlower is better
$0.20
1,048,576
Context window, tokens
1,050,000
Every fact side by side
| Muse Ember | GPT-6 Luna | |
|---|---|---|
| Price per million tokens | $0.10 in · $0.50 out | $0.10 in · $0.50 out |
| Blended (3 in : 1 out) | $0.20 | $0.20 |
| Cheapest host, blended | – | $0.20 (Amazon Bedrock) |
| Cost per product listing (CatalogBench) | $0.002 | $0.000 |
| Cached input, per million | $0.004 cached input | $0.010 cached input |
| Intelligence Index | – | 38.1 |
| Spring Prompt overall | 65 | 39 |
| Output speed, tokens a second | – | 127 |
| Context window | 1,048,576 tokens | 1,050,000 tokens |
| Released | 9 Nov 2026 (newer) | 22 Sep 2026 |
| Weights | Closed | Closed |
| Developer based in | the US | 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.
Product listingsTurning a sparse product feed and photos into listings that can go live
9 of 29
7 of 29
Decks from an analysisTurning a finished analysis into a deck you could present as it is
15 of 29
8 of 29
User surveysPlanning a user survey and reading its results without being misled
5 of 20
10 of 20
Marketing planningPlanning a year of ad spend without overspending
8 of 29
6 of 29
Agents and tool useMulti-step tasks with tools: support desks, coding agents, function calls
not measured
53 of 159
Professional workReal tasks from banking, consulting and law, business documents and freelance projects
not measured
25 of 51
Reasoning and knowledgeHard questions across science, maths and general knowledge
not measured
48 of 187
WritingWhat people prefer in blind comparisons, and judged writing quality
not measured
61 of 169
Sticking to the factsSummarising without inventing things, and factual answers
not measured
89 of 139
Speed under pressureGood decisions against a real clock (fast chess)
19 of 32
23 of 32
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
- Muse Ember review: GPT-6 Luna's price, better at analysis, and no Gemini Flash against a clock 10 Nov 2026
- Best AI for Product Descriptions: Accuracy, Cost and Real Examples 20 Oct 2026
- GPT-6.1 Terra review: close to the top of our business tests at half the price of Sol 5 Nov 2026
- Mercury 3 first look: very fast with thinking off, too slow with it on 20 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 Ember | GPT-6 Luna |
|---|---|---|
| BulletBench measured by Spring Prompt · 3 results | ||
| Cost per game · Bullet 60sUS dollars, lower is better 13 · 6 of 38 measured | $0.0038minimal reasoning | $0.0016no reasoning |
| Ladder Elo · Bullet 60sladder Elo, higher is better 27 · 29 of 38 measured | 310minimal reasoning | 249no reasoning |
| Games lost on time · Bullet 60s% of games, lower is better 27 · 21 of 38 measured | 62.5%minimal reasoning | 50.0%no reasoning |
| CatalogBench measured by Spring Prompt · 3 results | ||
| Cost per productUS dollars, lower is better 6 · 2 of 30 measured | $0.0020 | $0.0005 |
| Reliably publish-ready% of products, higher is better 11 · 9 of 29 measured | 53.6% | 55.4% |
| Reliably publish-ready · sales brief% of products, higher is better 9 · 5 of 29 measured | 35.7% | 51.8% |
| DeckBench measured by Spring Prompt · 3 results | ||
| Cost per deckUS dollars, lower is better 5 · 3 of 30 measured | $0.0112 | $0.0087 |
| 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 · 8 of 30 measured | 1,193 | 1,287 |
| Accurate decks% of tasks, higher is better 9 · 14 of 30 measured | 66.7% | 50.0% |
| ROASBench measured by Spring Prompt · 3 results | ||
| Overall scorescore out of 100, higher is better 8 · 6 of 30 measured | 51.8 | 53.4 |
| Audience scorescore out of 100, higher is better 18 · 5 of 30 measured | 60.6 | 68.3 |
| Business scorescore out of 100, higher is better 7 · 6 of 30 measured | 53.1 | 54.8 |
| 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 · 10 of 21 measured | 1,392 | 1,102 |
| Cost per taskUS dollars, lower is better 6 · 1 of 21 measured | $0.0213 | $0.0088 |
| Analysis scorepoints of 100, higher is better 10 · 6 of 21 measured | 84.3 | 90.8 |
Show all 83 results (68 more)
| Measure | Muse Ember | GPT-6 Luna |
|---|---|---|
| BulletBench measured by Spring Prompt · 2 results | ||
| Invalid moves · Bullet 60s% of moves, lower is better 1 · 1 of 38 measured | 0.0% | 0.0%no reasoning |
| Median move time · Bullet 60smilliseconds, lower is better 26 · 21 of 38 measured | 1.8 sminimal reasoning | 1.5 sno reasoning |
| CatalogBench measured by Spring Prompt · 28 results | ||
| Channel compliance% of products, higher is better 1 · 1 of 30 measured | 100.0% | 100.0% |
| Channel rules broken% of products, lower is better 1 · 1 of 30 measured | 0.0% | 0.0% |
| Channel rules broken · sales brief% of products, lower is better 1 · 1 of 30 measured | 0.0% | 0.0% |
| Claims to check% of products, lower is better 1 · 1 of 28 measured | 0.0% | 0.0% |
| Claims to check · sales brief% of products, lower is better 1 · 1 of 28 measured | 0.0% | 0.0% |
| Conflicts caught% of conflicts, higher is better 1 · 1 of 30 measured | 100.0% | 100.0% |
| Content quality% of checks, higher is better 23 · 15 of 30 measured | 95.6% | 97.0% |
| Content quality · sales brief% of checks, higher is better 4 · 13 of 30 measured | 98.5% | 97.4% |
| Decision accuracy% of decisions, higher is better 20 · 12 of 30 measured | 95.8% | 97.0% |
| Failed outputs% of products, lower is better 28 · 1 of 30 measured | 2.4% | 0.0% |
| Failed outputs · sales brief% of products, lower is better 1 · 1 of 30 measured | 0.0% | 0.0% |
| Field accuracy% of missing fields, higher is better 14 · 14 of 30 measured | 93.7% | 93.7% |
| Invented values% of filled values, lower is better 18 · 14 of 30 measured | 1.7% | 1.7% |
| Missing UK information% of products, lower is better 1 · 1 of 30 measured | 0.0% | 0.0% |
| Missing UK information · sales brief% of products, lower is better 1 · 1 of 30 measured | 0.0% | 0.0% |
| Not findable% of products, lower is better 9 · 26 of 30 measured | 3.6% | 10.1% |
| Not findable · sales brief% of products, lower is better 14 · 26 of 30 measured | 3.6% | 10.1% |
| Publish-ready listings% of products, higher is better 8 · 12 of 30 measured | 71.4% | 67.9% |
| Publish-ready listings · sales brief% of products, higher is better 11 · 6 of 30 measured | 54.2% | 67.9% |
| Time per productseconds, lower is better 5 · 7 of 30 measured | 8.10 | 8.20 |
| Unsupported claimsclaims per product, lower is better 8 · 9 of 30 measured | 0.29 | 0.31 |
| Unsupported claims% of products, lower is better 7 · 10 of 30 measured | 7.1% | 8.9% |
| Unsupported claims · sales brief% of products, lower is better 12 · 6 of 30 measured | 33.3% | 8.3% |
| Unsupported claims · sales briefclaims per product, lower is better 10 · 6 of 30 measured | 0.69 | 0.35 |
| Wrong attributes% of products, lower is better 5 · 19 of 30 measured | 13.7% | 18.5% |
| Wrong attributes · sales brief% of products, lower is better 8 · 18 of 30 measured | 14.9% | 19.1% |
| Wrong category or variant% of products, lower is better 5 · 5 of 30 measured | 1.8% | 1.8% |
| Wrong category or variant · sales brief% of products, lower is better 3 · 1 of 30 measured | 0.6% | 0.0% |
| DeckBench measured by Spring Prompt · 14 results | ||
| Caveat dropped% of tasks, lower is better 29 · 1 of 30 measured | 33.3% | 0.0% |
| Clean layout% of tasks, higher is better 27 · 8 of 30 measured | 0.0% | 66.7% |
| Design quality% of the maximum, higher is better 21 · 7 of 30 measured | 56.7% | 78.1% |
| Draft figure quoted% of tasks, lower is better 12 · 17 of 30 measured | 16.7% | 33.3% |
| Findings missing% of tasks, lower is better 20 · 1 of 30 measured | 16.7% | 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. 16 · 9 of 30 measured | 51.9% | 71.2% |
| Layout defects% of tasks, lower is better 27 · 8 of 30 measured | 100.0% | 33.3% |
| Misleading metric used% of tasks, lower is better 26 · 1 of 30 measured | 16.7% | 0.0% |
| Presentable decks% of tasks, higher is better 14 · 14 of 30 measured | 0.0% | 0.0% |
| Recommendation late or wrong% of tasks, lower is better 17 · 12 of 30 measured | 33.3% | 16.7% |
| Slides needing work% of tasks, lower is better 15 · 3 of 30 measured | 50.0% | 16.7% |
| Time per deckseconds, lower is better 4 · 15 of 30 measured | 30.2 | 101 |
| Unsupported claims% of tasks, lower is better 15 · 1 of 30 measured | 16.7% | 0.0% |
| Unsupported numbers% of tasks, lower is better 24 · 1 of 30 measured | 16.7% | 0.0% |
| ROASBench measured by Spring Prompt · 6 results | ||
| Consistency scorescore out of 100, higher is better 4 · 7 of 30 measured | 38.6 | 35.8 |
| Contribution profitsimulated US dollars, higher is better 13 · 6 of 30 measured | $621,558 | $738,879 |
| Cost of a runUS dollars, lower is better 4 · 2 of 30 measured | $0.0297 | $0.0142 |
| Months over budgetmonths of 12, lower is better 1 · 1 of 30 measured | 0 | 0 |
| Planning scorescore out of 100, higher is better 8 · 16 of 30 measured | 55.0 | 54.8 |
| Return on ad spendprofit per $1 spent, higher is better 5 · 8 of 30 measured | $0.70 | $0.66 |
| 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 · 12 of 21 measured | 62.2% | 48.3% |
| Leading or double-barrelled questions% of tasks, lower is better 9 · 3 of 21 measured | 33.3% | 11.1% |
| Mistaken next steps% of tasks, lower is better 9 · 5 of 21 measured | 22.2% | 11.1% |
| Numbers right% of answers, higher is better 1 · 15 of 21 measured | 100.0% | 98.0% |
| Objectives unanswerable% of tasks, lower is better 1 · 1 of 21 measured | 0.0% | 0.0% |
| Output tokens per tasktokens, lower is better 7 · 2 of 11 measured | 40,298 | 14,333 |
| Precision ignored% of tasks, lower is better 11 · 1 of 21 measured | 11.1% | 0.0% |
| Research scorepoints of 100, higher is better 10 · 6 of 21 measured | 86.9 | 91.7 |
| Sound research% of tasks, higher is better 8 · 4 of 21 measured | 22.2% | 33.3% |
| Stakeholder's view presumed% of tasks, lower is better 1 · 9 of 21 measured | 0.0% | 11.1% |
| Survey plan scorepoints of 100, higher is better 8 · 6 of 21 measured | 92.8 | 94.0 |
| Survey structure% of tasks, lower is better 9 · 9 of 21 measured | 22.2% | 22.2% |
| Time per taskseconds, lower is better 3 · 10 of 21 measured | 73.2 | 138 |
| Traps handled% of traps, higher is better 3 · 7 of 21 measured | 98.4% | 94.2% |
| Traps missed% of tasks, lower is better 3 · 5 of 21 measured | 22.2% | 33.3% |
| Unsupported findings% of tasks, lower is better 5 · 5 of 21 measured | 22.2% | 22.2% |
| Wrong numbers% of tasks, lower is better 11 · 11 of 21 measured | 11.1% | 11.1% |
| Wrong recommendation% of tasks, lower is better 12 · 5 of 21 measured | 22.2% | 11.1% |
Compare them with other models
- Meta vs OpenAIEvery model from both labs, best against best
- Muse Ember vs Claude Fable 5.2Highest-scoring overall available today
- Muse Ember vs Claude Fable 5.1Just above overall
- Muse Ember vs Claude Sonnet 5.5Just below overall
- Muse Ember vs Muse WonderMeta's best other model
- GPT-6 Luna vs Claude Haiku 5.5Most intelligent at under $0.25 per million tokens
- GPT-6 Luna vs DeepSeek-V4.1-FlashFastest with similar intelligence
- GPT-6 Luna vs Claude Opus 5.5Most intelligent available today
Quick answers
Is Muse Ember better than GPT-6 Luna?
Pick Muse Ember if quality matters most: it scores higher (Spring Prompt overall 65 against 39). Pick GPT-6 Luna if cost matters more: it costs 4.3 times less per product listing on CatalogBench.
Which is cheaper, Muse Ember or GPT-6 Luna?
GPT-6 Luna is cheaper to run: $0.000 against $0.002 per product listing on CatalogBench (4.3 times less).
Which is better for product listings, Muse Ember or GPT-6 Luna?
GPT-6 Luna ranks 7 of 29 models on sale for turning a sparse product feed and photos into listings that can go live, against 9 for Muse Ember.
Which is better for decks from an analysis, Muse Ember or GPT-6 Luna?
GPT-6 Luna ranks 8 of 29 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 GPT-6 Luna?
Muse Ember ranks 5 of 20 models on sale for planning a user survey and reading its results without being misled, against 10 for GPT-6 Luna.
Which is better for marketing planning, Muse Ember or GPT-6 Luna?
GPT-6 Luna ranks 6 of 29 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 GPT-6 Luna?
Muse Ember ranks 19 of 32 models on sale for good decisions against a real clock (fast chess), against 23 for GPT-6 Luna.
Which has the bigger context window, Muse Ember or GPT-6 Luna?
GPT-6 Luna: 1,050,000 tokens, against 1,048,576 for Muse Ember.
Run Muse Ember and GPT-6 Luna 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.