DeepSeek V4 Pro 0813 vs Muse Ember
Muse Ember leads DeepSeek V4 Pro 0813 on coding, 64.9 to 56.1 on the Vibe Coding Index, and Muse Ember costs 4.0x less per output token ($0.50 vs $1.98 per million). Updated November 14, 2026; 15 benchmarks have a result for both.
DeepSeek V4 Pro 0813 or Muse Ember: which to pick
Pick Muse Ember for coding: it scores 64.9 on the Vibe Coding Index against 56.1 for DeepSeek V4 Pro 0813, though on 3 benchmarks against 5.
DeepSeek V4 Pro 0813 is neither cheaper nor larger in context, and leads no benchmark both models were measured on.
- Shared results
- 15
- DeepSeek V4 Pro 0813 only
- 72
- Muse Ember only
- 0
- Like-for-like categories
- 5 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 56.1.
5 and 3 benchmarks behind the scores
Intelligence
AA Intelligence Index
No call
Only DeepSeek V4 Pro 0813 has a AA Intelligence Index result, 23.1.
Directional only
Agentic work
AA Agentic Index
No call
Only DeepSeek V4 Pro 0813 has a AA Agentic Index result, 41.3.
Directional only
Reasoning
GPQA Diamond
No call
Only DeepSeek V4 Pro 0813 has a GPQA Diamond result, 92.4%.
Directional only
Knowledge
MMLU Pro
No call
Only DeepSeek V4 Pro 0813 has a MMLU Pro result, 87.0%.
Directional only
General
Vals Index
Muse Ember
Muse Ember leads, 60.5% against 53.0%.
Same board, both measured
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 Pro 0813 $0.0016, Muse Ember $0.0003 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only DeepSeek V4 Pro 0813 has the number, $0.10.
Directional only
At a glance
- Coding index
- DeepSeek V4 Pro 0813 56.1 from 5 benchmarks, Muse Ember 64.9 from 3 benchmarks.
- Widest category
- Agentic work, 12.7 points apart on average over 4 shared benchmarks.
- Widest single gap
- Terminal-Bench 4 (Vals AI): DeepSeek V4 Pro 0813 14.1% against Muse Ember 39.4%, 33.7 points apart on a 0 to 100 scale.
- Output price
- DeepSeek V4 Pro 0813 $1.98 against Muse Ember $0.50 per million output tokens, 4.0x 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 Pro 0813 | Bars | Muse Ember | Ties |
|---|---|---|---|---|
| Coding | 0 | 7 | ||
| Agentic work | 0 | 4 | ||
| Reasoning | 0 | 2 | ||
| Knowledge | 0 | 1 | ||
| General | 0 | 1 | ||
| All shared boards | 0 | 15 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index07
| Benchmark | DeepSeek V4 Pro 0813 | Muse Ember | Leader |
|---|---|---|---|
| LMArena WebDev | 1581 | 1609 | Muse Ember |
| AA Coding Index | 68.8 | Not measured | Only DeepSeek V4 Pro 0813 measured |
| LiveCodeBench (Vals AI) | 87.5% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| SWE-bench Verified (Vals AI) | 96.4% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| Terminal-Bench 4 (Vals AI) | 14.1% | 39.4% | Muse Ember |
| Vibe Code Bench (Vals AI) | 82.3% | 83.7% | Muse Ember |
| Code migration (Vals AI) | 41.5% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| ProgramBench (Vals AI) | 0.0% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| IOI (Vals AI) | 51.6% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| Terminal-Bench 2.1 (Vals AI) | 54.7% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| Vibe Code Bench 1-100 (Vals AI) | 17.5% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| SkillsBench (Vals AI) | 53.8% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| CyberBench (Vals AI) | 72.9% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| SciCode | 51.7% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| Terminal-Bench 4.0 (Artificial Analysis run) | 14.1% | 40.9% | Muse Ember |
| FrontierCode 2.0 | 17.1% | 27.2% | Muse Ember |
| FrontierCode | 28.5% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| WeirdML | 66.2% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| MirrorCode | 33.3% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| ALE-Bench | 1403.2 | Not measured | Only DeepSeek V4 Pro 0813 measured |
| LMArena Text: coding | 1467 | Not measured | Only DeepSeek V4 Pro 0813 measured |
| LMArena Text: software and IT | 1460 | Not measured | Only DeepSeek V4 Pro 0813 measured |
| LMArena WebDev: React | 1586 | 1616 | Muse Ember |
| LMArena WebDev: HTML | 1561 | 1621 | Muse Ember |
| GameBench 2 | 53.6 | Not measured | Only DeepSeek V4 Pro 0813 measured |
| Terminal-Bench 4.0 | 6.7% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| Neither model has a result on the other 26 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | DeepSeek V4 Pro 0813 | Muse Ember | Leader |
|---|---|---|---|
| AA Intelligence Index | 23.1 | Not measured | Only DeepSeek V4 Pro 0813 measured |
| Epoch Capabilities Index | 155.9 | Not measured | Only DeepSeek V4 Pro 0813 measured |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index04
| Benchmark | DeepSeek V4 Pro 0813 | Muse Ember | Leader |
|---|---|---|---|
| AA Agentic Index | 41.3 | Not measured | Only DeepSeek V4 Pro 0813 measured |
| SREBench (Vals AI) | 1.9% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| Finance Agent (Vals AI) | 50.4% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| Tax Agent Bench (Vals AI) | 26.8% | 38.2% | Muse Ember |
| Legal Research Bench (Vals AI) | 40.9% | 49.0% | Muse Ember |
| Harvey's Legal Agent Benchmark (Vals AI) | 10.8% | 25.0% | Muse Ember |
| Excel Modeling Benchmark (Vals AI) | 52.8% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| Terminal-Bench-Science (Vals AI) | 4.3% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| APEX-Agents | 47.3% | 64.3% | Muse Ember |
| Toolathlon-Verified | 74.4% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| Neither model has a result on the other 34 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | DeepSeek V4 Pro 0813 | Muse Ember | Leader |
|---|---|---|---|
| GPQA Diamond | 92.4% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| GPQA Diamond (Artificial Analysis run) | 92.8% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| Humanity's Last Exam (Artificial Analysis run) | 41.5% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| HLE-Diamond (Artificial Analysis run) | 20.5% | 25.1% | Muse Ember |
| CritPt v2, hard subset (Artificial Analysis run) | 28.5% | 39.0% | Muse Ember |
| GPQA Diamond (Epoch AI run) | 91.7% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| ARC-AGI | 90.5% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| ARC-AGI-2 | 61.3% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| Chess puzzles | 46.0% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| Mystery game puzzles | 43.0% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| LMArena Text: hard prompts | 1457 | Not measured | Only DeepSeek V4 Pro 0813 measured |
| LMArena Text: expert | 1455 | Not measured | Only DeepSeek V4 Pro 0813 measured |
| LMArena Text: hard prompts in English | 1463 | Not measured | Only DeepSeek V4 Pro 0813 measured |
| Neither model has a result on the other 13 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
| Benchmark | DeepSeek V4 Pro 0813 | Muse Ember | Leader |
|---|---|---|---|
| OTIS Mock AIME | 98.6% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| FrontierMath, tiers 1 to 3 | 64.6% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| FrontierMath, tier 4 | 26.8% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| ProofBench | 50.0% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| LMArena Text: math | 1433 | Not measured | Only DeepSeek V4 Pro 0813 measured |
| LMArena Text: mathematical work | 1438 | Not measured | Only DeepSeek V4 Pro 0813 measured |
| Neither model has a result on the other 8 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | DeepSeek V4 Pro 0813 | Muse Ember | Leader |
|---|---|---|---|
| MMLU Pro | 87.0% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| LegalBench (Vals AI) | 82.4% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| CorpFin (Vals AI) | 65.4% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| TaxEval (Vals AI) | 73.1% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| MedCode (Vals AI) | 42.5% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| AA-Omniscience | -6.4 | 7.7 | Muse Ember |
| SimpleQA Verified | 52.9% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| LMArena Text: science | 1457 | Not measured | Only DeepSeek V4 Pro 0813 measured |
| LMArena Text: medicine and healthcare | 1446 | Not measured | Only DeepSeek V4 Pro 0813 measured |
| LMArena Text: legal and government | 1460 | Not measured | Only DeepSeek V4 Pro 0813 measured |
| LMArena Text: business and finance | 1442 | Not measured | Only DeepSeek V4 Pro 0813 measured |
| Neither model has a result on the other 10 boards here | |||
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | DeepSeek V4 Pro 0813 | Muse Ember | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | DeepSeek V4 Pro 0813 | Muse Ember | Leader |
|---|---|---|---|
| Vals Index | 53.0% | 60.5% | Muse Ember |
| MedScribe (Vals AI) | 80.2% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| Long context reasoning (AA-LCR) | 80.3% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| GDPval-AA | 1456 | Not measured | Only DeepSeek V4 Pro 0813 measured |
| DTBench | 93.9% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| LMCA | 45.5% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| LMArena Text | 1445 | Not measured | Only DeepSeek V4 Pro 0813 measured |
| LMArena Text: instruction following | 1448 | Not measured | Only DeepSeek V4 Pro 0813 measured |
| LMArena Text: creative writing | 1437 | Not measured | Only DeepSeek V4 Pro 0813 measured |
| LMArena Text: English | 1458 | Not measured | Only DeepSeek V4 Pro 0813 measured |
| LMArena Text: non-English | 1429 | Not measured | Only DeepSeek V4 Pro 0813 measured |
| LMArena Text: Chinese | 1478 | Not measured | Only DeepSeek V4 Pro 0813 measured |
| LMArena Text: French | 1440 | Not measured | Only DeepSeek V4 Pro 0813 measured |
| LMArena Text: Spanish | 1436 | Not measured | Only DeepSeek V4 Pro 0813 measured |
| LMArena Text: Russian | 1444 | Not measured | Only DeepSeek V4 Pro 0813 measured |
| LMArena Text: multi-turn | 1452 | Not measured | Only DeepSeek V4 Pro 0813 measured |
| LMArena Text: longer queries | 1457 | Not measured | Only DeepSeek V4 Pro 0813 measured |
| LMArena Text: writing and language | 1442 | Not measured | Only DeepSeek V4 Pro 0813 measured |
| LMArena Text: entertainment and media | 1424 | Not measured | Only DeepSeek V4 Pro 0813 measured |
| Neither model has a result on the other 30 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | DeepSeek V4 Pro 0813 | 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
Muse Ember costs less
Repository review
50K input + 3K output tokens
Muse Ember costs less
Agent loop
200K input + 10K output tokens
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 Pro 0813 | Meta Muse Ember |
|---|---|---|
| Maker | DeepSeek | Meta |
| Weights | Open weights | No public weights listed |
| Released | August 12, 2026 | November 9, 2026 |
| Context window | 1.05M | 1.05M |
| Input $/Mtok | $0.66 | $0.10 |
| Output $/Mtok | $1.98 | $0.50 |
| Coding index | 56.1 | 64.9 |
| Independent measurements behind it | 5 | 3 |
| Cost per solved task | $0.10 | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is DeepSeek V4 Pro 0813 better than Muse Ember for coding?
Pick Muse Ember for coding: it scores 64.9 on the Vibe Coding Index against 56.1 for DeepSeek V4 Pro 0813, though on 3 benchmarks against 5. DeepSeek V4 Pro 0813 is neither cheaper nor larger in context, and leads no benchmark both models were measured on.
Which is cheaper, DeepSeek V4 Pro 0813 or Muse Ember?
DeepSeek V4 Pro 0813 costs $0.66 per million input tokens and $1.98 per million output tokens; Muse Ember costs $0.10 and $0.50. Cost per solved task is known for DeepSeek V4 Pro 0813 only.
Which has the bigger context window, DeepSeek V4 Pro 0813 or Muse Ember?
Neither: both take 1.05M tokens.
How many benchmarks is the DeepSeek V4 Pro 0813 vs Muse Ember comparison based on?
15 benchmarks have a result for both models as of November 14, 2026. DeepSeek V4 Pro 0813's coding index rests on 5 independent benchmarks and Muse Ember's on 3. Where only one model has a result, the page says so instead of naming a winner.