Ling 3.0 Flash vs Muse Ember
Muse Ember leads Ling 3.0 Flash on coding, 64.9 to 29.6 on the Vibe Coding Index, and Ling 3.0 Flash costs 4.1x less per output token ($0.1215 vs $0.50 per million). Updated November 14, 2026; 4 benchmarks have a result for both.
Ling 3.0 Flash or Muse Ember: which to pick
Pick Muse Ember for coding: it scores 64.9 on the Vibe Coding Index against 29.6 for Ling 3.0 Flash.
Pick Ling 3.0 Flash for output tokens 4.1x cheaper ($0.1215 vs $0.50 per million).
- Shared results
- 4
- Ling 3.0 Flash only
- 23
- Muse Ember only
- 11
- 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 29.6.
2 and 3 benchmarks behind the scores
Agentic work
AA Agentic Index
No call
Only Ling 3.0 Flash has a AA Agentic Index result, 19.3.
Directional only
Reasoning
GPQA Diamond
No call
Only Ling 3.0 Flash has a GPQA Diamond result, 84.8%.
Directional only
Knowledge
MMLU Pro
No call
Only Ling 3.0 Flash has a MMLU Pro result, 82.0%.
Directional only
General
Vals Index
No call
Only Muse Ember has a Vals Index result, 60.5%.
Directional only
Long documents
Listed context window
Muse Ember
Muse Ember takes 1.05M tokens against 262K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Ling 3.0 Flash
Ling 3.0 Flash $0.0001, Muse Ember $0.0003 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only Ling 3.0 Flash has the number, $0.05.
Directional only
At a glance
- Coding index
- Ling 3.0 Flash 29.6 from 2 benchmarks, Muse Ember 64.9 from 3 benchmarks.
- Widest category
- Coding, 60.8 points apart on average over 2 shared benchmarks.
- Widest single gap
- Vibe Code Bench (Vals AI): Ling 3.0 Flash 2.9% against Muse Ember 83.7%, 80.8 points apart on a 0 to 100 scale.
- Output price
- Ling 3.0 Flash $0.1215 against Muse Ember $0.50 per million output tokens, 4.1x apart.
- Context window
- Ling 3.0 Flash takes 262K tokens, Muse Ember 1.05M.
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 | Ling 3.0 Flash | Bars | Muse Ember | Ties |
|---|---|---|---|---|
| Coding | 0 | 2 | ||
| Agentic work | 0 | 1 | ||
| Reasoning | 0 | 1 | ||
| All shared boards | 0 | 4 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index02
| Benchmark | Ling 3.0 Flash | Muse Ember | Leader |
|---|---|---|---|
| LMArena WebDev | Not measured | 1609 | Only Muse Ember measured |
| AA Coding Index | 50.6 | Not measured | Only Ling 3.0 Flash measured |
| LiveCodeBench (Vals AI) | 84.0% | Not measured | Only Ling 3.0 Flash measured |
| SWE-bench Verified (Vals AI) | 65.2% | Not measured | Only Ling 3.0 Flash measured |
| Terminal-Bench 4 (Vals AI) | Not measured | 39.4% | Only Muse Ember measured |
| Vibe Code Bench (Vals AI) | 2.9% | 83.7% | Muse Ember |
| Code migration (Vals AI) | 0.0% | Not measured | Only Ling 3.0 Flash measured |
| Terminal-Bench 2.1 (Vals AI) | 50.2% | Not measured | Only Ling 3.0 Flash measured |
| SkillsBench (Vals AI) | 27.9% | Not measured | Only Ling 3.0 Flash measured |
| SciCode | 42.0% | Not measured | Only Ling 3.0 Flash measured |
| Terminal-Bench 4.0 (Artificial Analysis run) | 0.0% | 40.9% | Muse Ember |
| FrontierCode 2.0 | Not measured | 27.2% | Only Muse Ember measured |
| LMArena WebDev: React | Not measured | 1616 | Only Muse Ember measured |
| LMArena WebDev: HTML | Not measured | 1621 | Only Muse Ember measured |
| GameBench 2 | 18.1 | Not measured | Only Ling 3.0 Flash measured |
| Neither model has a result on the other 37 boards here | |||
IntelligenceNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Ling 3.0 Flash | Muse Ember | Leader |
|---|---|---|---|
| Neither model has a result on 2 boards here | |||
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Ling 3.0 Flash | Muse Ember | Leader |
|---|---|---|---|
| AA Agentic Index | 19.3 | Not measured | Only Ling 3.0 Flash measured |
| Finance Agent (Vals AI) | 30.3% | Not measured | Only Ling 3.0 Flash measured |
| Tax Agent Bench (Vals AI) | Not measured | 38.2% | Only Muse Ember measured |
| Legal Research Bench (Vals AI) | 0.0% | 49.0% | Muse Ember |
| Harvey's Legal Agent Benchmark (Vals AI) | Not measured | 25.0% | Only Muse Ember measured |
| Excel Modeling Benchmark (Vals AI) | 25.0% | Not measured | Only Ling 3.0 Flash measured |
| APEX-Agents | Not measured | 64.3% | Only Muse Ember measured |
| Neither model has a result on the other 37 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Ling 3.0 Flash | Muse Ember | Leader |
|---|---|---|---|
| GPQA Diamond | 84.8% | Not measured | Only Ling 3.0 Flash measured |
| GPQA Diamond (Artificial Analysis run) | 85.5% | Not measured | Only Ling 3.0 Flash measured |
| Humanity's Last Exam (Artificial Analysis run) | 23.7% | Not measured | Only Ling 3.0 Flash measured |
| HLE-Diamond (Artificial Analysis run) | 7.2% | 25.1% | Muse Ember |
| CritPt v2, hard subset (Artificial Analysis run) | Not measured | 39.0% | Only Muse Ember measured |
| Neither model has a result on the other 21 boards here | |||
MathNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Ling 3.0 Flash | Muse Ember | Leader |
|---|---|---|---|
| Neither model has a result on 14 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
| Benchmark | Ling 3.0 Flash | Muse Ember | Leader |
|---|---|---|---|
| MMLU Pro | 82.0% | Not measured | Only Ling 3.0 Flash measured |
| LegalBench (Vals AI) | 79.7% | Not measured | Only Ling 3.0 Flash measured |
| CorpFin (Vals AI) | 61.9% | Not measured | Only Ling 3.0 Flash measured |
| TaxEval (Vals AI) | 70.7% | Not measured | Only Ling 3.0 Flash measured |
| MedCode (Vals AI) | 32.3% | Not measured | Only Ling 3.0 Flash measured |
| AA-Omniscience | Not measured | 7.7 | Only Muse Ember measured |
| Neither model has a result on the other 15 boards here | |||
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Ling 3.0 Flash | Muse Ember | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index5 boards, none shared
| Benchmark | Ling 3.0 Flash | Muse Ember | Leader |
|---|---|---|---|
| Vals Index | Not measured | 60.5% | Only Muse Ember measured |
| Public Benefits Bench (Vals AI) | 50.7% | Not measured | Only Ling 3.0 Flash measured |
| MedScribe (Vals AI) | 80.9% | Not measured | Only Ling 3.0 Flash measured |
| Long context reasoning (AA-LCR) | 73.0% | Not measured | Only Ling 3.0 Flash measured |
| GDPval-AA | 956 | Not measured | Only Ling 3.0 Flash measured |
| Neither model has a result on the other 44 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Ling 3.0 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
Ling 3.0 Flash costs less
Repository review
50K input + 3K output tokens
Ling 3.0 Flash costs less
Agent loop
200K input + 10K output tokens
Ling 3.0 Flash 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 | inclusionAI Ling 3.0 Flash | Meta Muse Ember |
|---|---|---|
| Maker | inclusionAI | Meta |
| Weights | Open weights | No public weights listed |
| Released | July 23, 2026 | November 9, 2026 |
| Context window | 262K | 1.05M |
| Input $/Mtok | $0.0405 | $0.10 |
| Output $/Mtok | $0.1215 | $0.50 |
| Coding index | 29.6 | 64.9 |
| Independent measurements behind it | 2 | 3 |
| Cost per solved task | $0.05 | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Ling 3.0 Flash better than Muse Ember for coding?
Pick Muse Ember for coding: it scores 64.9 on the Vibe Coding Index against 29.6 for Ling 3.0 Flash. Pick Ling 3.0 Flash for output tokens 4.1x cheaper ($0.1215 vs $0.50 per million).
Which is cheaper, Ling 3.0 Flash or Muse Ember?
Ling 3.0 Flash costs $0.0405 per million input tokens and $0.1215 per million output tokens; Muse Ember costs $0.10 and $0.50. Cost per solved task is known for Ling 3.0 Flash only.
Which has the bigger context window, Ling 3.0 Flash or Muse Ember?
Muse Ember, with 1.05M tokens against 262K.
How many benchmarks is the Ling 3.0 Flash vs Muse Ember comparison based on?
4 benchmarks have a result for both models as of November 14, 2026. Ling 3.0 Flash's coding index rests on 2 independent benchmarks and Muse Ember's on 3. Where only one model has a result, the page says so instead of naming a winner.