Agentic
Not comparable- Muse Spark 1.2
- Not measured
- Muse Spark 1.3
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Muse Spark 1.2 vs Muse Spark 1.3
Updated September 2, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload. This is a same-family comparison, so migration details appear when the source data supports them.
Decision reading
At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner.
3 results are shared. Category rows based on different benchmark sets are marked directional and do not name a winner.
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.
No workload recommendation clears the current evidence threshold.
Use the matched evidence, workload costs, and sourced specifications below instead of treating a point score as a universal answer.
Show secondary and unsupported calls
Coding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
No shared weighted benchmark basis supports a winner.
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
No shared weighted benchmark basis supports a winner.
Confidence: limited
Long documents
Prompts that approach the documented context limit
No clear pick
The documented context windows are equal.
Confidence: documented
Chat turn cost
1K fresh input + 500 output tokens
No clear pick
A complete comparable API-rate estimate is not available for both models.
Confidence: listed-rates
Cache-heavy agent loop cost
200K cached + 20K fresh input + 10K output tokens
No clear pick
A complete comparable API-rate estimate is not available for both models.
Confidence: listed-rates
Repository review cost
50K fresh input + 3K output tokens
No clear pick
A complete comparable API-rate estimate is not available for both models.
Confidence: listed-rates
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
3 Shared
7 Muse Spark 1.3 only
- Shared results
- 3
- Muse Spark 1.2 only
- 0
- Muse Spark 1.3 only
- 7
- Like-for-like categories
- 0 / 8
Category results, on a stated basis
Each row states whether both averages use the same weighted benchmark set. Directional and not-comparable rows remain visible, but they never receive a winner in this template.
Coding
Not comparable- Muse Spark 1.2
- Not measured
- Muse Spark 1.3
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Reasoning
Not comparable- Muse Spark 1.2
- Not measured
- Muse Spark 1.3
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Knowledge
Not comparable- Muse Spark 1.2
- Not measured
- Muse Spark 1.3
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Math
Not comparable- Muse Spark 1.2
- Not measured
- Muse Spark 1.3
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Multilingual
Not comparable- Muse Spark 1.2
- Not measured
- Muse Spark 1.3
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Multimodal
Not comparable- Muse Spark 1.2
- Not measured
- Muse Spark 1.3
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Instruction following
Not comparable- Muse Spark 1.2
- Not measured
- Muse Spark 1.3
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
| Category | Muse Spark 1.2 | Muse Spark 1.3 | Weighted basis | Reading |
|---|---|---|---|---|
| Agentic | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Coding | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Reasoning | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Knowledge | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Math | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Multilingual | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Multimodal | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Instruction following | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
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.
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 Spark 1.2
- $0.00338
- Fits in one request
- Muse Spark 1.3
- $0.00338
- Fits in one request
Modeled costs are equal
Costs use the listed standard API rates.
Repository review
50K fresh input + 3K output tokens
- Muse Spark 1.2
- $0.07525
- Fits in one request
- Muse Spark 1.3
- $0.07525
- Fits in one request
Modeled costs are equal
Costs use the listed standard API rates.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- Muse Spark 1.2
- $0.0975
- Fits in one request
- Muse Spark 1.3
- $0.0975
- Fits in one request
Modeled costs are equal
Costs use the listed standard API rates.
Specification differences
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
SpecificationMuse Spark 1.2Muse Spark 1.3
Context window
Maximum documented context; output-token limits may be lower.
Muse Spark 1.2
Muse Spark 1.3
API model ID
Muse Spark 1.2
muse-spark-1.2
Meta: Muse Spark 1.2 model pageMuse Spark 1.3
muse-spark-1.3
Meta Muse Spark 1.3 model pageCached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
Muse Spark 1.2
$0.15 per 1M cached input tokens
Meta: Muse Spark 1.2 model pageMuse Spark 1.3
$0.15 per 1M cached input tokens
Meta: Muse Spark 1.3 model pageDocumented inputs
Muse Spark 1.2
Not sourced
Muse Spark 1.3
text, image, video, document
Meta Muse Spark 1.3 model pageDocumented outputs
Muse Spark 1.2
Not sourced
Muse Spark 1.3
Provider availability
Muse Spark 1.2
Not sourced
Muse Spark 1.3
Generally Available · Meta Model API, Muse Code
Meta AI Research Muse Spark 1.3 launchReasoning profile
Muse Spark 1.2
Reasoning
Muse Spark 1.3
Reasoning
Weight access
Muse Spark 1.2
Proprietary
Muse Spark 1.3
Proprietary
License
Muse Spark 1.2
Proprietary
Muse Spark 1.3
Proprietary
Release date
Muse Spark 1.2
2026-08-05
Muse Spark 1.3
2026-09-02
If you are considering the documented upgrade path
- Deployment change
- Both entries list Meta as the provider. Confirm endpoint, model ID, limits, and feature support before switching.
- Quality signal
- At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner.
- Workload cost
- Repository review: $0.07525 vs $0.07525. Cache-heavy agent loop: $0.0975 vs $0.0975.
- Context tradeoff
- Both models list 1M.
Run the same representative tasks against both endpoints before changing production traffic.
Benchmark evidence
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Browse raw public benchmark evidence10 rows
Agentic
Terminal-Bench 2.1
Muse Spark 1.282.9%SourceMuse Spark 1.388.8%SourceMuse Spark 1.3 leads this result
JobBench
Muse Spark 1.2—Muse Spark 1.364.9%SourceNot directly comparable
OSWorld 2.0
Muse Spark 1.2—Muse Spark 1.366.9%SourceNot directly comparable
DeepSearchQA
Muse Spark 1.2—Muse Spark 1.389.4%SourceNot directly comparable
AutomationBench
Muse Spark 1.2—Muse Spark 1.349.4%SourceNot directly comparable
Coding
Reasoning
Frequently asked questions
Which is better, Muse Spark 1.2 or Muse Spark 1.3?
At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner. The page therefore keeps the decision tied to the specific documented workload.
Which is better for coding, Muse Spark 1.2 or Muse Spark 1.3?
The published evidence does not provide a shared weighted coding basis for both models, so BenchLM does not name a coding winner.
Which is better for agentic tasks, Muse Spark 1.2 or Muse Spark 1.3?
The published evidence does not provide a shared weighted agentic tasks basis for both models, so BenchLM does not name a agentic tasks winner.
Which costs less, Muse Spark 1.2 or Muse Spark 1.3?
For the stated presets, chat costs $0.00337 on Muse Spark 1.2 and $0.00337 on Muse Spark 1.3; repository review costs $0.07525 and $0.07525; the cache-heavy agent loop costs $0.0975 and $0.0975. Costs use the listed standard API rates.
Which has the larger context window, Muse Spark 1.2 or Muse Spark 1.3?
Both models list the same context window, 1M.
Related comparisons
Last updated September 2, 2026
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