Chat turn cost
1K fresh input + 500 output tokens
Muse Ember
Muse Ember has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
Muse Ember vs Muse Spark 1.2
Updated November 9, 2026. Rank says Muse Spark 1.2 is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.
Share or export
Decision reading
The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. Use the documented cost, context, and runtime rows instead. 0 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
- Shared results
- 0
- Muse Ember only
- 4
- Muse Spark 1.2 only
- 9
- Like-for-like categories
- 0 / 8
Estimated: Muse Ember · Supported: Muse Spark 1.2. Conditional ranges do not establish rank confidence.How the comparison works
Which one for your work
Recommendations appear only when a shared evidence basis or an explicit operating constraint supports the call. Secondary and unsupported use cases stay disclosed below the initial list.
Cache-heavy agent loop cost
200K cached + 20K fresh input + 10K output tokens
Muse Ember
Muse Ember has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
Show secondary and unsupported calls
Repository review cost
50K fresh input + 3K output tokens
Muse Ember
Muse Ember has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-ratesCoding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
Muse Ember is not ranked on the public lane for coding, so no winner is named for coding.
Confidence: limitedAgentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
Muse Ember is not ranked on the public lane for agentic, so no winner is named for agentic.
Confidence: limitedLong documents
Prompts that approach the documented context limit
No clear pick
The documented context windows are equal.
Confidence: documented
Which one for a specific job
Choose a job from the LLM Selector's task catalog to see the category row it rests on, under the same basis rules as the table below. A directional row stays directional; choosing a job never creates a winner.
The same task catalog as the LLM Selector. Each job names the evidence surface it rests on; nothing here adds a new score.
—Muse Ember53.0Muse Spark 1.2
Not comparable · BenchAlign v5.8
The coding row is not comparable on the public lane: at least one model is not measured or not ranked there.
Coding scores combine specific tasks and setups. Match the editor, harness, and effort to your workflow.
Same basis rules as the category table below
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
4 Muse Ember only
9 Muse Spark 1.2 only
Shape of the matched evidence
Only shared public evidence is shown. Sparse evidence stays a ruled list rather than being closed into a radar shape.
A shared-evidence shape is not available.
BenchLM does not draw a radar or infer missing axes when the matched evidence is too sparse.
Category results, on a stated basis
Each row shows the public-lane category score for both models: the BenchAlign v5.8 lane for agentic, coding, reasoning, multimodal, and knowledge, and the weighted public rows elsewhere, with the same rank each model page reports. A row is like-for-like only when both scores rest on Supported evidence or the same weighted set. Directional and not-comparable rows remain visible, but they never receive a winner.
Agentic
Not comparable- Muse Ember
- Not ranked
- Muse Spark 1.2
- 59.4
- Supported · #32/132
- Basis
- BenchAlign v5.8 lane · 3 vs 3 public rows
- Reading
- Not comparable
Coding
Not comparable- Muse Ember
- Not ranked
- Muse Spark 1.2
- 53.0
- Supported · #39/158
- Basis
- BenchAlign v5.8 lane · 0 vs 5 public rows
- Reading
- Not comparable
Reasoning
Not comparable- Muse Ember
- Not ranked
- Muse Spark 1.2
- 61.4
- Estimated · #29/157
- Basis
- BenchAlign v5.8 lane · 0 vs 0 public rows
- Reading
- Not comparable
Multimodal
Not comparable- Muse Ember
- Not ranked
- Muse Spark 1.2
- Not ranked
- Basis
- BenchAlign v5.8 lane · 1 vs 0 public rows
- Reading
- Not comparable
Knowledge
Not comparable- Muse Ember
- Not ranked
- Muse Spark 1.2
- 64.1
- Supported · #33/179
- Basis
- BenchAlign v5.8 lane · 0 vs 1 public rows
- Reading
- Not comparable
Multilingual
Not comparable- Muse Ember
- Not ranked
- Muse Spark 1.2
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- Muse Ember
- Not ranked
- Muse Spark 1.2
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- Muse Ember
- Not ranked
- Muse Spark 1.2
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
| Category | Muse Ember | Muse Spark 1.2 | Basis | Reading |
|---|---|---|---|---|
| Agentic | Not ranked | 59.4Supported · #32/132 | Not comparableBenchAlign v5.8 lane · 3 vs 3 public rows | Not comparable |
| Coding | Not ranked | 53.0Supported · #39/158 | Not comparableBenchAlign v5.8 lane · 0 vs 5 public rows | Not comparable |
| Reasoning | Not ranked | 61.4Estimated · #29/157 | Not comparableBenchAlign v5.8 lane · 0 vs 0 public rows | Not comparable |
| Multimodal | Not ranked | Not ranked | Not comparableBenchAlign v5.8 lane · 1 vs 0 public rows | Not comparable |
| Knowledge | Not ranked | 64.1Supported · #33/179 | Not comparableBenchAlign v5.8 lane · 0 vs 1 public rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Instruction following | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
Ranks count the models scored in each category’s lane, so the agentic, coding, reasoning, multimodal, and knowledge denominators (BenchAlign v5.8) differ from the provisional-lane categories. Unranked scores sit on the provisional lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.
Supported evidence per lane · bars run 0–100Methodology
What each workload costs
Three fixed token mixes turn per-token rates into comparable decisions. Each scenario states context fit and whether cached input had to fall back to the published list-input rate.
Chat turn
1K fresh input + 500 output tokens
- Muse Ember
- $0.00035
- Fits in one request
- Muse Spark 1.2
- $0.00338
- Fits in one request
Muse Ember has the lower modeled cost
Costs use the listed standard API rates.
Repository review
50K fresh input + 3K output tokens
- Muse Ember
- $0.0065
- Fits in one request
- Muse Spark 1.2
- $0.07525
- Fits in one request
Muse Ember has the lower modeled cost
Costs use the listed standard API rates.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- Muse Ember
- $0.0078
- Fits in one request
- Muse Spark 1.2
- $0.0975
- Fits in one request
Muse Ember has the lower modeled cost
Costs use the listed standard API rates.
Cached input falls back to the list input rate only where a cached rate is unpublished
Specification differences
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
SpecificationMuse EmberMuse Spark 1.2
Context window
Maximum documented context; output-token limits may be lower.
Muse Ember
Muse Spark 1.2
API model ID
Muse Ember
muse-ember-1.0
Meta: Muse Ember models and pricingMuse Spark 1.2
muse-spark-1.2
Meta: Muse Spark 1.2 model pageCached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
Muse Ember
$0.004 per 1M cached input tokens
Meta: Muse Ember models and pricingMuse Spark 1.2
$0.15 per 1M cached input tokens
Meta: Muse Spark 1.2 model pageDocumented inputs
Muse Ember
Not sourced
Muse Spark 1.2
Not sourced
Documented outputs
Muse Ember
Not sourced
Muse Spark 1.2
Not sourced
Provider availability
Muse Ember
Not sourced
Muse Spark 1.2
Not sourced
Reasoning profile
Muse Ember
Reasoning
Muse Spark 1.2
Reasoning
Weight access
Muse Ember
Proprietary
Muse Spark 1.2
Proprietary
License
Muse Ember
Proprietary
Muse Spark 1.2
Proprietary
Release date
Muse Ember
2026-11-09
Muse Spark 1.2
2026-08-05
If you already use one of these models
- Deployment change
- Both entries list Meta as the provider. Confirm endpoint, model ID, limits, and feature support before switching.
- Quality signal
- The public evidence has no benchmark result shared by both models, so it does not support a quality verdict.
- Workload cost
- Repository review: $0.0065 vs $0.07525. Cache-heavy agent loop: $0.0078 vs $0.0975.
- Context tradeoff
- Both models list 1M.
Run the same representative tasks against both endpoints before changing production traffic.
Questions
Which is better, Muse Ember or Muse Spark 1.2?
The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. The page therefore keeps the decision tied to the specific documented workload.
Which is better for coding, Muse Ember or Muse Spark 1.2?
Muse Ember is not ranked on the public lane for coding, so no winner is named for coding.
Which is better for agentic tasks, Muse Ember or Muse Spark 1.2?
Muse Ember is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.
Which costs less, Muse Ember or Muse Spark 1.2?
For the stated presets, chat costs $0.00035 on Muse Ember and $0.00337 on Muse Spark 1.2; repository review costs $0.0065 and $0.07525; the cache-heavy agent loop costs $0.0078 and $0.0975. Costs use the listed standard API rates.
Which has the larger context window, Muse Ember or Muse Spark 1.2?
Both models list the same context window, 1M.
Benchmark evidence
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Browse raw public benchmark evidence13 rows
Agentic
Terminal-Bench 4.0
Muse Ember40.90%SourceMuse Spark 1.2—Not directly comparable
OSWorld 2.1
Muse Ember69.5%SourceMuse Spark 1.2—Not directly comparable
Agents' Last Exam (Linux)
Muse Ember61.6%SourceMuse Spark 1.2—Not directly comparable
Terminal-Bench 2.1
Muse Ember—Muse Spark 1.282.9%SourceNot directly comparable
Terminal-Bench 2.1 (Vals)
Muse Ember—Muse Spark 1.269.7%SourceNot directly comparable
MazeBench
Muse Ember—Muse Spark 1.20%SourceNot directly comparable
Coding
Terminal-Bench 2.1
Muse Ember—Muse Spark 1.282.9%SourceNot directly comparable
DeepSWE
Muse Ember—Muse Spark 1.259.3%SourceNot directly comparable
VulcanBench v3
Muse Ember—Muse Spark 1.287.0%SourceNot directly comparable
FrontierSWE v2
Muse Ember—Muse Spark 1.212.0%SourceNot directly comparable
SWE-bench (Vals)
Muse Ember—Muse Spark 1.286.6%SourceNot directly comparable
Multimodal
PerceptionBench (tools)
Muse Ember80.6%SourceMuse Spark 1.2—Not directly comparable
Knowledge
MMLU-Pro (Vals)
Muse Ember—Muse Spark 1.288.3%SourceNot directly comparable
13 public results · 0 shared
Watch Muse Ember vs Muse Spark 1.2
One weekly email when material rank, price, or benchmark evidence changes make this matchup worth revisiting.
Read a sample issueJoin 6,100+ subscribers.
Last updated November 9, 2026