Compare / DeepSeek-V4.1-Flash-1017 vs Muse Ember
DeepSeek-V4.1-Flash-1017 vs Muse Ember
Which is cheaper, which is more capable, and which does better at each kind of work, from 85 results on 5 sources that measured both models.
DeepSeekDeepSeek-V4.1-Flash-1017
MetaMuse Ember
DeepSeek-V4.1-Flash-1017 wins 2615 too close to callMuse Ember wins 44
Of the 85 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 DeepSeek vs Meta, every model from both labs.
DeepSeek-V4.1-Flash-1017 or Muse Ember? The short answer
Pick Muse Ember if quality matters most: it scores higher (Spring Prompt overall 65 against 40). Pick DeepSeek-V4.1-Flash-1017 if cost matters more: it costs 24% less per product listing on CatalogBench.
- Spring Prompt overall: DeepSeek-V4.1-Flash-1017 40, Muse Ember 65 (out of 100; how the score works).
- DeepSeek-V4.1-Flash-1017 is cheaper to run: $0.002 against $0.002 per product listing on CatalogBench (24% less).
- Muse Ember is newer: released 9 Nov 2026, against 17 Oct 2026 for DeepSeek-V4.1-Flash-1017.
- On our own benchmarks, on the results both models have: Muse Ember does better for product listings, user surveys and marketing planning.
Head to head
40
Spring Prompt overall
65
$0.52
Price per million tokens, blendedlower is better
$0.20
1,048,576
Context window, tokens
1,048,576
Every fact side by side
| DeepSeek-V4.1-Flash-1017 | Muse Ember | |
|---|---|---|
| Price per million tokens | $0.30 in · $1.20 out | $0.10 in · $0.50 out |
| Blended (3 in : 1 out) | $0.52 | $0.20 |
| Cheapest host, blended | $0.11 (Sail Research, FP4) | – |
| Cost per product listing (CatalogBench) | $0.002 | $0.002 |
| Cached input, per million | $0.006 cached input | $0.004 cached input |
| Intelligence Index | 45.1 | – |
| Spring Prompt overall | 40 | 65 |
| Output speed, tokens a second | 212 | – |
| Context window | 1,048,576 tokens | 1,048,576 tokens |
| Released | 17 Oct 2026 | 9 Nov 2026 (newer) |
| Weights | Open | 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.
Product listingsTurning a sparse product feed and photos into listings that can go live
25 of 29
9 of 29
Decks from an analysisTurning a finished analysis into a deck you could present as it is
18 of 29
15 of 29
User surveysPlanning a user survey and reading its results without being misled
8 of 20
5 of 20
Marketing planningPlanning a year of ad spend without overspending
17 of 29
8 of 29
Agents and tool useMulti-step tasks with tools: support desks, coding agents, function calls
24 of 159
not measured
Reasoning and knowledgeHard questions across science, maths and general knowledge
24 of 187
not measured
Speed under pressureGood decisions against a real clock (fast chess)
24 of 32
19 of 32
From our research
- DeepSeek-V4.1-Flash-1017 review: better at analysis, much worse at sticking to the product data 19 Oct 2026
- Muse Ember review: GPT-6 Luna's price, better at analysis, and no Gemini Flash against a clock 10 Nov 2026
- Mercury 3 first look: very fast with thinking off, too slow with it on 20 Oct 2026
- MiniMax M3.1 Flash first look: careful with product data, unreliable on long jobs 19 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 | DeepSeek-V4.1-Flash-1017 | Muse Ember |
|---|---|---|
| BulletBench measured by Spring Prompt · 3 results | ||
| Cost per game · Bullet 60sUS dollars, lower is better 15 · 13 of 38 measured | $0.0047low reasoning | $0.0038minimal reasoning |
| Ladder Elo · Bullet 60sladder Elo, higher is better 31 · 27 of 38 measured | 147low reasoning | 310minimal reasoning |
| Games lost on time · Bullet 60s% of games, lower is better 34 · 27 of 38 measured | 87.5%low reasoning | 62.5%minimal reasoning |
| CatalogBench measured by Spring Prompt · 3 results | ||
| Cost per productUS dollars, lower is better 5 · 6 of 30 measured | $0.0015 | $0.0020 |
| Reliably publish-ready% of products, higher is better 22 · 11 of 29 measured | 21.4% | 53.6% |
| Reliably publish-ready · sales brief% of products, higher is better 24 · 9 of 29 measured | 0.0% | 35.7% |
| DeckBench measured by Spring Prompt · 3 results | ||
| Cost per deckUS dollars, lower is better 9 · 5 of 30 measured | $0.0255 | $0.0112 |
| 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. 18 · 15 of 30 measured | 1,075 | 1,193 |
| Accurate decks% of tasks, higher is better 18 · 9 of 30 measured | 33.3% | 66.7% |
| ROASBench measured by Spring Prompt · 3 results | ||
| Overall scorescore out of 100, higher is better 17 · 8 of 30 measured | 41.4 | 51.8 |
| Audience scorescore out of 100, higher is better 12 · 18 of 30 measured | 65.3 | 60.6 |
| Business scorescore out of 100, higher is better 17 · 7 of 30 measured | 38.3 | 53.1 |
| 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. 8 · 5 of 21 measured | 1,169 | 1,392 |
| Cost per taskUS dollars, lower is better 8 · 6 of 21 measured | $0.0373 | $0.0213 |
| Analysis scorepoints of 100, higher is better 7 · 10 of 21 measured | 90.2 | 84.3 |
Show all 85 results (70 more)
| Measure | DeepSeek-V4.1-Flash-1017 | Muse Ember |
|---|---|---|
| BulletBench measured by Spring Prompt · 2 results | ||
| Invalid moves · Bullet 60s% of moves, lower is better 1 · 1 of 38 measured | 0.0%low reasoning | 0.0% |
| Median move time · Bullet 60smilliseconds, lower is better 36 · 26 of 38 measured | 3.3 slow reasoning | 1.8 sminimal reasoning |
| CatalogBench measured by Spring Prompt · 29 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 24 · 1 of 30 measured | 1.2% | 0.0% |
| Channel rules broken · sales brief% of products, lower is better 27 · 1 of 30 measured | 2.4% | 0.0% |
| Claims to check% of products, lower is better 23 · 1 of 28 measured | 1.2% | 0.0% |
| Claims to check · sales brief% of products, lower is better 22 · 1 of 28 measured | 3.6% | 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 26 · 23 of 30 measured | 95.3% | 95.6% |
| Content quality · sales brief% of checks, higher is better 25 · 4 of 30 measured | 94.8% | 98.5% |
| Decision accuracy% of decisions, higher is better 24 · 20 of 30 measured | 94.9% | 95.8% |
| Failed outputs% of products, lower is better 1 · 28 of 30 measured | 0.0% | 2.4% |
| 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 14 · 18 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 · 9 of 30 measured | 3.6% | 3.6% |
| Not findable · sales brief% of products, lower is better 11 · 14 of 30 measured | 2.4% | 3.6% |
| Output tokens per producttokens, lower is better 2 · 7 of 8 measured | 1,226 | 3,816 |
| Publish-ready listings% of products, higher is better 23 · 8 of 30 measured | 40.5% | 71.4% |
| Publish-ready listings · sales brief% of products, higher is better 25 · 11 of 30 measured | 8.3% | 54.2% |
| Time per productseconds, lower is better 3 · 5 of 30 measured | 6.70 | 8.10 |
| Unsupported claimsclaims per product, lower is better 27 · 8 of 30 measured | 1.70 | 0.29 |
| Unsupported claims% of products, lower is better 26 · 7 of 30 measured | 44.6% | 7.1% |
| Unsupported claims · sales brief% of products, lower is better 29 · 12 of 30 measured | 85.7% | 33.3% |
| Unsupported claims · sales briefclaims per product, lower is better 28 · 10 of 30 measured | 4.01 | 0.69 |
| Wrong attributes% of products, lower is better 18 · 5 of 30 measured | 17.9% | 13.7% |
| Wrong attributes · sales brief% of products, lower is better 26 · 8 of 30 measured | 22.0% | 14.9% |
| Wrong category or variant% of products, lower is better 2 · 5 of 30 measured | 0.6% | 1.8% |
| Wrong category or variant · sales brief% of products, lower is better 4 · 3 of 30 measured | 1.2% | 0.6% |
| DeckBench measured by Spring Prompt · 15 results | ||
| Caveat dropped% of tasks, lower is better 14 · 29 of 30 measured | 16.7% | 33.3% |
| Clean layout% of tasks, higher is better 23 · 27 of 30 measured | 16.7% | 0.0% |
| Design quality% of the maximum, higher is better 12 · 21 of 30 measured | 68.5% | 56.7% |
| Draft figure quoted% of tasks, lower is better 23 · 12 of 30 measured | 50.0% | 16.7% |
| Findings missing% of tasks, lower is better 1 · 20 of 30 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. 21 · 16 of 30 measured | 46.0% | 51.9% |
| Layout defects% of tasks, lower is better 23 · 27 of 30 measured | 83.3% | 100.0% |
| Misleading metric used% of tasks, lower is better 1 · 26 of 30 measured | 0.0% | 16.7% |
| Output tokens per decktokens, lower is better 3 · 4 of 9 measured | 21,019 | 22,094 |
| 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 12 · 17 of 30 measured | 16.7% | 33.3% |
| Slides needing work% of tasks, lower is better 22 · 15 of 30 measured | 66.7% | 50.0% |
| Time per deckseconds, lower is better 8 · 4 of 30 measured | 73.0 | 30.2 |
| Unsupported claims% of tasks, lower is better 15 · 15 of 30 measured | 16.7% | 16.7% |
| Unsupported numbers% of tasks, lower is better 1 · 24 of 30 measured | 0.0% | 16.7% |
| ROASBench measured by Spring Prompt · 6 results | ||
| Consistency scorescore out of 100, higher is better 21 · 4 of 30 measured | 20.2 | 38.6 |
| Contribution profitsimulated US dollars, higher is better 16 · 13 of 30 measured | $585,572 | $621,558 |
| Cost of a runUS dollars, lower is better 10 · 4 of 30 measured | $0.0799 | $0.0297 |
| Months over budgetmonths of 12, lower is better 1 · 1 of 30 measured | 0 | 0 |
| Planning scorescore out of 100, higher is better 25 · 8 of 30 measured | 54.3 | 55.0 |
| Return on ad spendprofit per $1 spent, higher is better 18 · 5 of 30 measured | $0.44 | $0.70 |
| 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. 9 · 6 of 21 measured | 58.1% | 62.2% |
| Leading or double-barrelled questions% of tasks, lower is better 3 · 9 of 21 measured | 11.1% | 33.3% |
| Mistaken next steps% of tasks, lower is better 1 · 9 of 21 measured | 0.0% | 22.2% |
| Numbers right% of answers, higher is better 1 · 1 of 21 measured | 100.0% | 100.0% |
| Objectives unanswerable% of tasks, lower is better 12 · 1 of 21 measured | 11.1% | 0.0% |
| Output tokens per tasktokens, lower is better 5 · 7 of 11 measured | 30,893 | 40,298 |
| Precision ignored% of tasks, lower is better 1 · 11 of 21 measured | 0.0% | 11.1% |
| Research scorepoints of 100, higher is better 7 · 10 of 21 measured | 90.9 | 86.9 |
| Sound research% of tasks, higher is better 4 · 8 of 21 measured | 33.3% | 22.2% |
| Stakeholder's view presumed% of tasks, lower is better 1 · 1 of 21 measured | 0.0% | 0.0% |
| Survey plan scorepoints of 100, higher is better 10 · 8 of 21 measured | 92.5 | 92.8 |
| Survey structure% of tasks, lower is better 16 · 9 of 21 measured | 33.3% | 22.2% |
| Time per taskseconds, lower is better 9 · 3 of 21 measured | 134 | 73.2 |
| Traps handled% of traps, higher is better 11 · 3 of 21 measured | 91.3% | 98.4% |
| Traps missed% of tasks, lower is better 10 · 3 of 21 measured | 44.4% | 22.2% |
| Unsupported findings% of tasks, lower is better 8 · 5 of 21 measured | 44.4% | 22.2% |
| Wrong numbers% of tasks, lower is better 1 · 11 of 21 measured | 0.0% | 11.1% |
| Wrong recommendation% of tasks, lower is better 5 · 12 of 21 measured | 11.1% | 22.2% |
Compare them with other models
- DeepSeek vs MetaEvery model from both labs, best against best
- DeepSeek-V4.1-Flash-1017 vs Claude Haiku 5.5Cheapest with similar intelligence
- DeepSeek-V4.1-Flash-1017 vs Muse Spark 1.3Fastest with similar intelligence
- DeepSeek-V4.1-Flash-1017 vs Claude Opus 5.5Most intelligent available today
- 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
Quick answers
Is DeepSeek-V4.1-Flash-1017 better than Muse Ember?
Pick Muse Ember if quality matters most: it scores higher (Spring Prompt overall 65 against 40). Pick DeepSeek-V4.1-Flash-1017 if cost matters more: it costs 24% less per product listing on CatalogBench.
Which is cheaper, DeepSeek-V4.1-Flash-1017 or Muse Ember?
DeepSeek-V4.1-Flash-1017 is cheaper to run: $0.002 against $0.002 per product listing on CatalogBench (24% less).
Which is better for product listings, DeepSeek-V4.1-Flash-1017 or Muse Ember?
Muse Ember ranks 9 of 29 models on sale for turning a sparse product feed and photos into listings that can go live, against 25 for DeepSeek-V4.1-Flash-1017.
Which is better for decks from an analysis, DeepSeek-V4.1-Flash-1017 or Muse Ember?
Muse Ember ranks 15 of 29 models on sale for turning a finished analysis into a deck you could present as it is, against 18 for DeepSeek-V4.1-Flash-1017.
Which is better for user surveys, DeepSeek-V4.1-Flash-1017 or Muse Ember?
Muse Ember ranks 5 of 20 models on sale for planning a user survey and reading its results without being misled, against 8 for DeepSeek-V4.1-Flash-1017.
Which is better for marketing planning, DeepSeek-V4.1-Flash-1017 or Muse Ember?
Muse Ember ranks 8 of 29 models on sale for planning a year of ad spend without overspending, against 17 for DeepSeek-V4.1-Flash-1017.
Which is better for speed under pressure, DeepSeek-V4.1-Flash-1017 or Muse Ember?
Muse Ember ranks 19 of 32 models on sale for good decisions against a real clock (fast chess), against 24 for DeepSeek-V4.1-Flash-1017.
Run DeepSeek-V4.1-Flash-1017 and Muse Ember 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.