DeepSeek V4.1 Flash vs Muse Ember
Muse Ember leads DeepSeek V4.1 Flash on coding, 64.9 to 56.6 on the Vibe Coding Index, and Muse Ember costs 1.6x less per output token ($0.50 vs $0.79 per million). Updated November 14, 2026; 10 benchmarks have a result for both.
vs boards won of 10 shared
DeepSeek V4.1 Flash or Muse Ember: which to pick
Pick Muse Ember for coding: it scores 64.9 on the Vibe Coding Index against 56.6 for DeepSeek V4.1 Flash, though on 3 benchmarks against 4.
Pick DeepSeek V4.1 Flash for work measured by LMArena WebDev or Vibe Code Bench (Vals AI), where it leads.
Model ADeepSeek V4.1 FlashDeepSeek56.6/1004 independent benchmarksNo cost per solved taskModel BMuse EmberMeta64.9/1003 independent benchmarksNo cost per solved task
- Shared results
- 10
- DeepSeek V4.1 Flash only
- 91
- Muse Ember only
- 5
- Like-for-like categories
- 3 of 9
Only benchmarks that tested both models count toward a pick; a result only one model has is shown but not compared.
Which one for your work
Each card is decided by one number both models carry. Where only one has it, the card says so and makes no call.
Coding
Vibe Coding Index, six public coding benchmarks
Muse Ember
Muse Ember scores 64.9 against 56.6.
4 and 3 benchmarks behind the scores
Web design
Design Arena: websites
No call
Only DeepSeek V4.1 Flash has a Design Arena: websites result, 1298.
Directional only
General
Vals Index
No call
Only Muse Ember has a Vals Index result, 60.5%.
Directional only
Long documents
Listed context window
No call
Both take 1.05M tokens.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Muse Ember
DeepSeek V4.1 Flash $0.0006, Muse Ember $0.0003 per turn.
List prices
At a glance
- Coding index
- DeepSeek V4.1 Flash 56.6 from 4 benchmarks, Muse Ember 64.9 from 3 benchmarks.
- Widest category
- Agentic work, 14.7 points apart on average over 3 shared benchmarks.
- Widest single gap
- Terminal-Bench 4 (Vals AI): DeepSeek V4.1 Flash 19.7% against Muse Ember 39.4%, 26.3 points apart on a 0 to 100 scale.
- Output price
- DeepSeek V4.1 Flash $0.79 against Muse Ember $0.50 per million output tokens, 1.6x apart.
- Context window
- Both take 1.05M tokens.
Benchmark by benchmark
First the score: boards each model leads, per category, where both have a result. Then every board, a category to a fold; bars are each result on a 0 to 100 scale, so compare the two in a row, not one row with another.
| Category | DeepSeek V4.1 Flash | Bars | Muse Ember | Ties |
|---|---|---|---|---|
| Coding | 3 | 3 | ||
| Agentic work | 0 | 3 | ||
| Reasoning | 0 | 1 | ||
| All shared boards | 3 | 7 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index33
| Benchmark | DeepSeek V4.1 Flash | Muse Ember | Leader |
|---|---|---|---|
| LMArena WebDev | 1621 | 1609 | DeepSeek V4.1 Flash |
| Terminal-Bench 4 (Vals AI) | 19.7% | 39.4% | Muse Ember |
| Vibe Code Bench (Vals AI) | 84.7% | 83.7% | DeepSeek V4.1 Flash |
| Code migration (Vals AI) | 45.6% | Not measured | Only DeepSeek V4.1 Flash measured |
| ProgramBench (Vals AI) | 0.5% | Not measured | Only DeepSeek V4.1 Flash measured |
| IOI (Vals AI) | 40.3% | Not measured | Only DeepSeek V4.1 Flash measured |
| Terminal-Bench 2.1 (Vals AI) | 74.5% | Not measured | Only DeepSeek V4.1 Flash measured |
| SWE-bench Multimodal (Vals AI) | 46.5% | Not measured | Only DeepSeek V4.1 Flash measured |
| Vibe Code Bench 1-100 (Vals AI) | 16.4% | Not measured | Only DeepSeek V4.1 Flash measured |
| SkillsBench (Vals AI) | 69.8% | Not measured | Only DeepSeek V4.1 Flash measured |
| CyberBench (Vals AI) | 73.7% | Not measured | Only DeepSeek V4.1 Flash measured |
| SciCode | 51.9% | Not measured | Only DeepSeek V4.1 Flash measured |
| Terminal-Bench 4.0 (Artificial Analysis run) | 26.8% | 40.9% | Muse Ember |
| FrontierCode 2.0 | Not measured | 27.2% | Only Muse Ember measured |
| FrontierCode | 43.9% | Not measured | Only DeepSeek V4.1 Flash measured |
| MirrorCode | 64.4% | Not measured | Only DeepSeek V4.1 Flash measured |
| ALE-Bench | 1092.3 | Not measured | Only DeepSeek V4.1 Flash measured |
| LMArena Text: coding | 1504 | Not measured | Only DeepSeek V4.1 Flash measured |
| LMArena Text: software and IT | 1496 | Not measured | Only DeepSeek V4.1 Flash measured |
| LMArena WebDev: React | 1630 | 1616 | DeepSeek V4.1 Flash |
| LMArena WebDev: HTML | 1618 | 1621 | Muse Ember |
| LMArena WebDev: from an image | 1610 | Not measured | Only DeepSeek V4.1 Flash measured |
| GameBench 2 | 68.4 | Not measured | Only DeepSeek V4.1 Flash measured |
| Neither model has a result on the other 29 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | DeepSeek V4.1 Flash | Muse Ember | Leader |
|---|---|---|---|
| Epoch Capabilities Index | 156.7 | Not measured | Only DeepSeek V4.1 Flash measured |
| Neither model has a result on the other 1 board here | |||
Agentic workLike-for-likePublic boards, shown as published, kept out of the index03
| Benchmark | DeepSeek V4.1 Flash | Muse Ember | Leader |
|---|---|---|---|
| SREBench (Vals AI) | 0.8% | Not measured | Only DeepSeek V4.1 Flash measured |
| Finance Agent (Vals AI) | 53.5% | Not measured | Only DeepSeek V4.1 Flash measured |
| Tax Agent Bench (Vals AI) | 26.6% | 38.2% | Muse Ember |
| Legal Research Bench (Vals AI) | 41.4% | 49.0% | Muse Ember |
| Harvey's Legal Agent Benchmark (Vals AI) | Not measured | 25.0% | Only Muse Ember measured |
| Excel Modeling Benchmark (Vals AI) | 57.2% | Not measured | Only DeepSeek V4.1 Flash measured |
| BioMysteryBench (Vals AI) | 67.8% | Not measured | Only DeepSeek V4.1 Flash measured |
| Terminal-Bench-Science (Vals AI) | 0.0% | Not measured | Only DeepSeek V4.1 Flash measured |
| Vals Science Index | 29.6% | Not measured | Only DeepSeek V4.1 Flash measured |
| APEX-Agents | 39.5% | 64.3% | Muse Ember |
| Neither model has a result on the other 34 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | DeepSeek V4.1 Flash | Muse Ember | Leader |
|---|---|---|---|
| MysteryMechanism (Vals AI) | 21.2% | Not measured | Only DeepSeek V4.1 Flash measured |
| Humanity's Last Exam (Artificial Analysis run) | 39.2% | Not measured | Only DeepSeek V4.1 Flash measured |
| HLE-Diamond (Artificial Analysis run) | 19.1% | 25.1% | Muse Ember |
| CritPt v2, hard subset (Artificial Analysis run) | Not measured | 39.0% | Only Muse Ember measured |
| GPQA Diamond (Epoch AI run) | 89.8% | Not measured | Only DeepSeek V4.1 Flash measured |
| ARC-AGI | 94.5% | Not measured | Only DeepSeek V4.1 Flash measured |
| ARC-AGI-2 | 72.9% | Not measured | Only DeepSeek V4.1 Flash measured |
| SimpleBench | 66.7% | Not measured | Only DeepSeek V4.1 Flash measured |
| Chess puzzles | 48.0% | Not measured | Only DeepSeek V4.1 Flash measured |
| Mystery game puzzles | 43.0% | Not measured | Only DeepSeek V4.1 Flash measured |
| LMArena Text: hard prompts | 1483 | Not measured | Only DeepSeek V4.1 Flash measured |
| LMArena Text: expert | 1503 | Not measured | Only DeepSeek V4.1 Flash measured |
| LMArena Text: hard prompts in English | 1488 | Not measured | Only DeepSeek V4.1 Flash measured |
| LMArena Vision: diagrams | 1299 | Not measured | Only DeepSeek V4.1 Flash measured |
| LMArena Vision: homework | 1307 | Not measured | Only DeepSeek V4.1 Flash measured |
| GoBench | 1706 | Not measured | Only DeepSeek V4.1 Flash measured |
| Neither model has a result on the other 10 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
| Benchmark | DeepSeek V4.1 Flash | Muse Ember | Leader |
|---|---|---|---|
| OTIS Mock AIME | 98.3% | Not measured | Only DeepSeek V4.1 Flash measured |
| FrontierMath, tiers 1 to 3 | 67.4% | Not measured | Only DeepSeek V4.1 Flash measured |
| FrontierMath, tier 4 | 26.8% | Not measured | Only DeepSeek V4.1 Flash measured |
| ProofBench | 54.0% | Not measured | Only DeepSeek V4.1 Flash measured |
| LMArena Text: math | 1479 | Not measured | Only DeepSeek V4.1 Flash measured |
| LMArena Text: mathematical work | 1479 | Not measured | Only DeepSeek V4.1 Flash measured |
| MathArena | 39.3% | Not measured | Only DeepSeek V4.1 Flash measured |
| Neither model has a result on the other 7 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
| Benchmark | DeepSeek V4.1 Flash | Muse Ember | Leader |
|---|---|---|---|
| LegalBench (Vals AI) | 83.3% | Not measured | Only DeepSeek V4.1 Flash measured |
| MedCode (Vals AI) | 41.2% | Not measured | Only DeepSeek V4.1 Flash measured |
| AA-Omniscience | Not measured | 7.7 | Only Muse Ember measured |
| SimpleQA Verified | 42.9% | Not measured | Only DeepSeek V4.1 Flash measured |
| LMArena Text: science | 1468 | Not measured | Only DeepSeek V4.1 Flash measured |
| LMArena Text: medicine and healthcare | 1490 | Not measured | Only DeepSeek V4.1 Flash measured |
| LMArena Text: legal and government | 1447 | Not measured | Only DeepSeek V4.1 Flash measured |
| LMArena Text: business and finance | 1463 | Not measured | Only DeepSeek V4.1 Flash measured |
| Neither model has a result on the other 13 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
| Benchmark | DeepSeek V4.1 Flash | Muse Ember | Leader |
|---|---|---|---|
| Design Arena: websites | 1298 | Not measured | Only DeepSeek V4.1 Flash measured |
| Design Arena: UI components | 1303 | Not measured | Only DeepSeek V4.1 Flash measured |
| Design Arena: game dev | 1324 | Not measured | Only DeepSeek V4.1 Flash measured |
| Design Arena: data viz | 1285 | Not measured | Only DeepSeek V4.1 Flash measured |
| Design Arena: 3D | 1338 | Not measured | Only DeepSeek V4.1 Flash measured |
| Design Arena: all code categories | 1309 | Not measured | Only DeepSeek V4.1 Flash measured |
| Design Arena: SVG | 1231 | Not measured | Only DeepSeek V4.1 Flash measured |
| Design Arena: ASCII art | 1259 | Not measured | Only DeepSeek V4.1 Flash measured |
| Neither model has a result on the other 1 board here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index33 boards, none shared
| Benchmark | DeepSeek V4.1 Flash | Muse Ember | Leader |
|---|---|---|---|
| Vals Index | Not measured | 60.5% | Only Muse Ember measured |
| Public Benefits Bench (Vals AI) | 64.3% | Not measured | Only DeepSeek V4.1 Flash measured |
| SAGE (Vals AI) | 85.4% | Not measured | Only DeepSeek V4.1 Flash measured |
| MedScribe (Vals AI) | 85.5% | Not measured | Only DeepSeek V4.1 Flash measured |
| Long context reasoning (AA-LCR) | 84.0% | Not measured | Only DeepSeek V4.1 Flash measured |
| GDPval-AA | 1600 | Not measured | Only DeepSeek V4.1 Flash measured |
| DTBench | 89.9% | Not measured | Only DeepSeek V4.1 Flash measured |
| LMCA | 47.0% | Not measured | Only DeepSeek V4.1 Flash measured |
| Surface Evolver Bench | 46.3% | Not measured | Only DeepSeek V4.1 Flash measured |
| Furniture assembly | 34.2% | Not measured | Only DeepSeek V4.1 Flash measured |
| LMArena Text | 1464 | Not measured | Only DeepSeek V4.1 Flash measured |
| LMArena Text: instruction following | 1473 | Not measured | Only DeepSeek V4.1 Flash measured |
| LMArena Text: creative writing | 1438 | Not measured | Only DeepSeek V4.1 Flash measured |
| LMArena Vision | 1279 | Not measured | Only DeepSeek V4.1 Flash measured |
| LMArena Text: English | 1474 | Not measured | Only DeepSeek V4.1 Flash measured |
| LMArena Text: non-English | 1450 | Not measured | Only DeepSeek V4.1 Flash measured |
| LMArena Text: Chinese | 1505 | Not measured | Only DeepSeek V4.1 Flash measured |
| LMArena Text: German | 1460 | Not measured | Only DeepSeek V4.1 Flash measured |
| LMArena Text: Japanese | 1430 | Not measured | Only DeepSeek V4.1 Flash measured |
| LMArena Text: Korean | 1423 | Not measured | Only DeepSeek V4.1 Flash measured |
| LMArena Text: French | 1470 | Not measured | Only DeepSeek V4.1 Flash measured |
| LMArena Text: Spanish | 1456 | Not measured | Only DeepSeek V4.1 Flash measured |
| LMArena Text: Russian | 1469 | Not measured | Only DeepSeek V4.1 Flash measured |
| LMArena Text: Polish | 1453 | Not measured | Only DeepSeek V4.1 Flash measured |
| LMArena Text: multi-turn | 1462 | Not measured | Only DeepSeek V4.1 Flash measured |
| LMArena Text: longer queries | 1474 | Not measured | Only DeepSeek V4.1 Flash measured |
| LMArena Text: writing and language | 1449 | Not measured | Only DeepSeek V4.1 Flash measured |
| LMArena Text: entertainment and media | 1444 | Not measured | Only DeepSeek V4.1 Flash measured |
| LMArena Vision: English | 1290 | Not measured | Only DeepSeek V4.1 Flash measured |
| LMArena Vision: Chinese | 1345 | Not measured | Only DeepSeek V4.1 Flash measured |
| LMArena Vision: reading text | 1284 | Not measured | Only DeepSeek V4.1 Flash measured |
| LMArena Vision: humor | 1279 | Not measured | Only DeepSeek V4.1 Flash measured |
| LMArena Vision: creative writing | 1283 | Not measured | Only DeepSeek V4.1 Flash measured |
| Neither model has a result on the other 16 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | DeepSeek V4.1 Flash | Muse Ember | Leader |
|---|---|---|---|
| Neither model has a result on 5 boards here | |||
What each workload costs
Three fixed token mixes at each model's listed price, so per-token rates become a bill.
Chat turn
1K input + 500 output tokens
DeepSeek V4.1 Flash$0.0006Fits in one request
Muse Ember$0.0003Fits in one request
Muse Ember costs less
Repository review
50K input + 3K output tokens
DeepSeek V4.1 Flash$0.012Fits in one request
Muse Ember$0.0065Fits in one request
Muse Ember costs less
Agent loop
200K input + 10K output tokens
DeepSeek V4.1 Flash$0.048Fits in one request
Muse Ember$0.025Fits in one request
Muse Ember costs less
Listed OpenRouter rates, without cache or batch discounts. A workload that does not fit a model's context window would need splitting.
Specification differences
The leading value in a row is marked; a row with a missing value names no leader. Our two rows are marked in red.
| Specification | DeepSeek V4.1 Flash | Meta Muse Ember |
|---|---|---|
| Maker | DeepSeek | Meta |
| Weights | Open weights | No public weights listed |
| Released | September 10, 2026 | November 9, 2026 |
| Context window | 1.05M | 1.05M |
| Input $/Mtok | $0.2025 | $0.10 |
| Output $/Mtok | $0.79 | $0.50 |
| Coding index | 56.6 | 64.9 |
| Independent measurements behind it | 4 | 3 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is DeepSeek V4.1 Flash better than Muse Ember for coding?
Pick Muse Ember for coding: it scores 64.9 on the Vibe Coding Index against 56.6 for DeepSeek V4.1 Flash, though on 3 benchmarks against 4. Pick DeepSeek V4.1 Flash for work measured by LMArena WebDev or Vibe Code Bench (Vals AI), where it leads.
Which is cheaper, DeepSeek V4.1 Flash or Muse Ember?
DeepSeek V4.1 Flash costs $0.2025 per million input tokens and $0.79 per million output tokens; Muse Ember costs $0.10 and $0.50. Cost per solved task is known for neither of them.
Which has the bigger context window, DeepSeek V4.1 Flash or Muse Ember?
Neither: both take 1.05M tokens.
How many benchmarks is the DeepSeek V4.1 Flash vs Muse Ember comparison based on?
10 benchmarks have a result for both models as of November 14, 2026. DeepSeek V4.1 Flash's coding index rests on 4 independent benchmarks and Muse Ember's on 3. Where only one model has a result, the page says so instead of naming a winner.