Long documents
Prompts that approach the documented context limit
Llama 4 Scout
Llama 4 Scout has the larger documented context window.
Confidence: documented
Llama 4 Scout vs Muse Spark
Updated October 5, 2026. Rank says Muse Spark is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.
Both of these models will change. Get the price, version and retirement notices for the pair, each with its source. Follow model changes
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Decision reading
Muse Spark has the higher public score, 59.99 versus 29.46, and the 90% score intervals do not overlap. 1 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
- Shared results
- 1
- Llama 4 Scout only
- 0
- Muse Spark only
- 26
- Like-for-like categories
- 0 / 8
Supported: Llama 4 Scout and Muse SparkHow 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.
Show secondary and unsupported calls
Coding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
Llama 4 Scout is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like.
Confidence: limitedAgentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
Llama 4 Scout is scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.
Confidence: limitedChat turn cost
1K fresh input + 500 output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
Confidence: listed-ratesCache-heavy agent loop cost
200K cached + 20K fresh input + 10K output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
Confidence: listed-ratesRepository review cost
50K fresh input + 3K output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
Confidence: listed-rates
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.
12.0Llama 4 Scout51.1Muse Spark
Directional only · BenchAlign v5.8
Muse Spark has the higher coding point estimate. Conditional score ranges do not establish rank confidence.
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.
1 Shared
26 Muse Spark only
4 categories rest on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.
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.
Too few matched category axes support a radar. The ruled list below shows only shared benchmark results; positions use each benchmark’s normalized display scale when available.
FrontierMath v2 (Tiers 1-3)Math
Normalized gap 39.0Llama 4 Scout:0.000%Muse Spark:39.000%
Bars run 0–100 on each benchmark’s normalized display scale
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, 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
Directional only- Llama 4 Scout
- 7.6
- Estimated · #114/119
- Muse Spark
- 49.8
- Supported · #43/119
- Basis
- BenchAlign v5.8 lane · 0 vs 5 public rows
- Reading
- Directional only
Coding
Directional only- Llama 4 Scout
- 12.0
- Estimated · #137/144
- Muse Spark
- 51.1
- Supported · #39/144
- Basis
- BenchAlign v5.8 lane · 0 vs 5 public rows
- Reading
- Directional only
Knowledge
Directional only- Llama 4 Scout
- 26.4
- Estimated · #152/171
- Muse Spark
- 60.3
- Supported · #40/171
- Basis
- BenchAlign v5.8 lane · 0 vs 7 public rows
- Reading
- Directional only
Instruction following
Directional only- Llama 4 Scout
- 44.3
- #92/125
- Muse Spark
- 91.9
- #8/125
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Directional only
Reasoning
Not comparable- Llama 4 Scout
- 41.2
- Unranked · 2 rankable rows
- Muse Spark
- 53.4
- Unranked · 3 rankable rows
- Basis
- Provisional lane · 0 vs 1 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- Llama 4 Scout
- 39.1
- Unranked · 1 rankable row
- Muse Spark
- 78.5
- #14/49
- Basis
- Provisional lane · 0 vs 2 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- Llama 4 Scout
- Not ranked
- Muse Spark
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- Llama 4 Scout
- 25.1
- Unranked · 1 rankable row
- Muse Spark
- 55.1
- Unranked · 2 rankable rows
- Basis
- Provisional lane · 1 vs 2 weighted rows
- Reading
- Not comparable
| Category | Llama 4 Scout | Muse Spark | Basis | Reading |
|---|---|---|---|---|
| Agentic | 7.6Estimated · #114/119 | 49.8Supported · #43/119 | Directional onlyBenchAlign v5.8 lane · 0 vs 5 public rows | Directional only |
| Coding | 12.0Estimated · #137/144 | 51.1Supported · #39/144 | Directional onlyBenchAlign v5.8 lane · 0 vs 5 public rows | Directional only |
| Knowledge | 26.4Estimated · #152/171 | 60.3Supported · #40/171 | Directional onlyBenchAlign v5.8 lane · 0 vs 7 public rows | Directional only |
| Instruction following | 44.3#92/125 | 91.9#8/125 | Directional onlyProvisional lane · 0 vs 0 weighted rows | Directional only |
| Reasoning | 41.2Unranked · 2 rankable rows | 53.4Unranked · 3 rankable rows | Not comparableProvisional lane · 0 vs 1 weighted rows | Not comparable |
| Multimodal | 39.1Unranked · 1 rankable row | 78.5#14/49 | Not comparableProvisional lane · 0 vs 2 weighted rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | 25.1Unranked · 1 rankable row | 55.1Unranked · 2 rankable rows | Not comparableProvisional lane · 1 vs 2 weighted rows | Not comparable |
Ranks count the models scored in each category’s lane, so the agentic, coding, 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
- Llama 4 Scout
- Self-hosted; infrastructure cost varies
- Fits in one request
- Muse Spark
- API rate not published
- Fits in one request
Llama 4 Scout has no comparable published API token rate. Muse Spark has no comparable published API token rate.
Repository review
50K fresh input + 3K output tokens
- Llama 4 Scout
- Self-hosted; infrastructure cost varies
- Fits in one request
- Muse Spark
- API rate not published
- Fits in one request
Llama 4 Scout has no comparable published API token rate. Muse Spark has no comparable published API token rate.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- Llama 4 Scout
- Self-hosted; infrastructure cost varies
- Fits in one request
- Cached-input rate unavailable
- Muse Spark
- API rate not published
- Fits in one request
- Cached-input rate unavailable
Llama 4 Scout has no comparable published API token rate. Muse Spark has no comparable published API token rate.
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.
SpecificationLlama 4 ScoutMuse Spark
Context window
Maximum documented context; output-token limits may be lower.
Llama 4 Scout
10M
Muse Spark
262K
API model ID
Llama 4 Scout
Not sourced
Muse Spark
Not sourced
Cached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
Llama 4 Scout
No comparable hosted API rate
Muse Spark
No comparable hosted API rate
Documented inputs
Llama 4 Scout
Not sourced
Muse Spark
Not sourced
Documented outputs
Llama 4 Scout
Not sourced
Muse Spark
Not sourced
Provider availability
Llama 4 Scout
Not sourced
Muse Spark
Not sourced
Reasoning profile
Llama 4 Scout
Non-Reasoning
Muse Spark
Reasoning
Weight access
Llama 4 Scout
Open Weight
Muse Spark
Proprietary
License
Llama 4 Scout
Open Weight
Muse Spark
Proprietary
Release date
Llama 4 Scout
2026-02-28
Muse Spark
2026-04-08
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
- Muse Spark has the higher public score, 59.99 versus 29.46, and the 90% score intervals do not overlap.
- Workload cost
- A complete comparable API-rate estimate is not available for both models.
- Context tradeoff
- Llama 4 Scout has the larger documented window (10M).
Run the same representative tasks against both endpoints before changing production traffic.
Questions
Which is better, Llama 4 Scout or Muse Spark?
Muse Spark has the higher public score, 59.99 versus 29.46, and the 90% score intervals do not overlap. That is the clearest overall quality signal in the current public data.
Which is better for coding, Llama 4 Scout or Muse Spark?
Muse Spark scores higher for coding on the public lane, 51.1 to 12. Llama 4 Scout is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like. BenchLM does not name a winner for coding from a directional reading; read the shared benchmark rows directly and test the models on the same task set.
Which is better for agentic tasks, Llama 4 Scout or Muse Spark?
Muse Spark scores higher for agentic tasks on the public lane, 49.8 to 7.6. Llama 4 Scout is scored on Estimated evidence for agentic tasks, so the reading is directional rather than like-for-like. BenchLM does not name a winner for agentic tasks from a directional reading; read the shared benchmark rows directly and test the models on the same task set.
Which costs less, Llama 4 Scout or Muse Spark?
Both models do not have comparable published API token rates, so this page does not name a universal price winner.
Which has the larger context window, Llama 4 Scout or Muse Spark?
Llama 4 Scout has the larger documented context window: 10M, compared with 262K.
Self-host vs API cost
Estimates at 50,000 req/day · 1000 tokens/req average.
Llama 4 Scout
API / mo$0
Self-host / mo$2,278
Break-even—
Muse Spark
API / mo$0
Self-host / moNot listed
Break-even—
Proprietary model — self-hosting not applicable.
Benchmark evidence
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Browse raw public benchmark evidence27 rows
Agentic
Terminal-Bench 2.0
Llama 4 Scout—Muse Spark59%SourceNot directly comparable
τ²-bench results
Llama 4 Scout—Muse Spark91.5%SourceNot directly comparable
DeepSearchQA
Llama 4 Scout—Muse Spark74.8%SourceNot directly comparable
CyberGym
Llama 4 Scout—Muse Spark43.5%SourceNot directly comparable
Claw-Eval
Llama 4 Scout—Muse Spark63.8%SourceNot directly comparable
Coding
SWE-bench Verified
Llama 4 Scout—Muse Spark77.4%SourceNot directly comparable
SWE-bench Pro
Llama 4 Scout—Muse Spark52.4%SourceNot directly comparable
LiveCodeBench Pro
Llama 4 Scout—Muse Spark80.0%SourceNot directly comparable
Vibe Code Bench
Llama 4 Scout—Muse Spark19.67%SourceNot directly comparable
SWE-bench (Vals)
Llama 4 Scout—Muse Spark74.4%SourceNot directly comparable
Reasoning
ARC-AGI-2
Llama 4 Scout—Muse Spark42.5%SourceNot directly comparable
Multimodal
CharXiv
Llama 4 Scout—Muse Spark86.4%SourceNot directly comparable
MMMU-Pro
Llama 4 Scout—Muse Spark80.4%SourceNot directly comparable
ERQA
Llama 4 Scout—Muse Spark64.7%SourceNot directly comparable
SimpleVQA
Llama 4 Scout—Muse Spark71.3%SourceNot directly comparable
ScreenSpot Pro
Llama 4 Scout—Muse Spark84.1%SourceNot directly comparable
ZeroBench
Llama 4 Scout—Muse Spark33.0%SourceNot directly comparable
MedXpertQA (MM)
Llama 4 Scout—Muse Spark78.4%SourceNot directly comparable
Knowledge
GPQA-D
Llama 4 Scout—Muse Spark89.5%SourceNot directly comparable
HLE
Llama 4 Scout—Muse Spark50.4%SourceNot directly comparable
HLE w/o tools
Llama 4 Scout—Muse Spark42.8%SourceNot directly comparable
HealthBench Hard
Llama 4 Scout—Muse Spark42.8%SourceNot directly comparable
MedXpertQA (Text)
Llama 4 Scout—Muse Spark52.6%SourceNot directly comparable
GPQA Diamond (Vals)
Llama 4 Scout—Muse Spark89.6%SourceNot directly comparable
MMLU-Pro (Vals)
Llama 4 Scout—Muse Spark87.3%SourceNot directly comparable
Math
FrontierMath v2 (Tiers 1-3)
Shared sourceLlama 4 Scout0.000%Muse Spark39.000%Muse Spark leads this result
FrontierMath v2 (Tier 4)
Llama 4 Scout—Muse Spark14.600%SourceNot directly comparable
27 public results · 1 shared
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Last updated October 5, 2026