Agentic
Not comparable- Muse Spark 1.3
- Not measured
- Quasar 438B
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Muse Spark 1.3 vs Quasar 438B
Updated September 2, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
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.
2 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.
Chat turn cost
1K fresh input + 500 output tokens
Quasar 438B
Quasar 438B has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
Cache-heavy agent loop cost
200K cached + 20K fresh input + 10K output tokens
Muse Spark 1.3
Muse Spark 1.3 has the lower estimated token cost for this stated workload. Quasar 438B has no published cached-input rate, so cached tokens use its listed input rate.
Confidence: rate-fallback
Show secondary and unsupported calls
Repository review cost
50K fresh input + 3K output tokens
Quasar 438B
Quasar 438B has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
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
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
8 Muse Spark 1.3 only
2 Shared
- Shared results
- 2
- Muse Spark 1.3 only
- 8
- Quasar 438B only
- 0
- 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 1.3
- Not measured
- Quasar 438B
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Reasoning
Not comparable- Muse Spark 1.3
- Not measured
- Quasar 438B
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Knowledge
Not comparable- Muse Spark 1.3
- Not measured
- Quasar 438B
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Math
Not comparable- Muse Spark 1.3
- Not measured
- Quasar 438B
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Multilingual
Not comparable- Muse Spark 1.3
- Not measured
- Quasar 438B
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Multimodal
Not comparable- Muse Spark 1.3
- Not measured
- Quasar 438B
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Instruction following
Not comparable- Muse Spark 1.3
- Not measured
- Quasar 438B
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
| Category | Muse Spark 1.3 | Quasar 438B | 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 1.3
- $0.00338
- Fits in one request
- Quasar 438B
- $0.0015
- Fits in one request
Quasar 438B has the lower modeled cost
Costs use the listed standard API rates.
Repository review
50K fresh input + 3K output tokens
- Muse Spark 1.3
- $0.07525
- Fits in one request
- Quasar 438B
- $0.0354
- Fits in one request
Quasar 438B 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 Spark 1.3
- $0.0975
- Fits in one request
- Quasar 438B
- $0.15
- Fits in one request
- Cached input priced at the published list-input rate
Muse Spark 1.3 has the lower modeled cost
Quasar 438B has no published cached-input rate, so cached tokens use its listed input rate.
Specification differences
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
SpecificationMuse Spark 1.3Quasar 438B
Context window
Maximum documented context; output-token limits may be lower.
Muse Spark 1.3
Quasar 438B
API model ID
Muse Spark 1.3
muse-spark-1.3
Meta Muse Spark 1.3 model pageQuasar 438B
quasar-438b
CompactifAI Quasar 438B documentationCached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
Muse Spark 1.3
$0.15 per 1M cached input tokens
Meta: Muse Spark 1.3 model pageQuasar 438B
Not published
CompactifAI API pricingDocumented inputs
Muse Spark 1.3
text, image, video, document
Meta Muse Spark 1.3 model pageQuasar 438B
Not sourced
Documented outputs
Muse Spark 1.3
Quasar 438B
Not sourced
Provider availability
Muse Spark 1.3
Generally Available · Meta Model API, Muse Code
Meta AI Research Muse Spark 1.3 launchQuasar 438B
Generally Available · CompactifAI API
CompactifAI Quasar 438B documentationReasoning profile
Muse Spark 1.3
Reasoning
Quasar 438B
Reasoning
Weight access
Muse Spark 1.3
Proprietary
Quasar 438B
Proprietary
License
Muse Spark 1.3
Proprietary
Quasar 438B
Proprietary
Release date
Muse Spark 1.3
2026-09-02
Quasar 438B
2026-09-02
If you already use one of these models
- Deployment change
- The models list different providers, so authentication, endpoint behavior, limits, and feature support may change.
- 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 $0.0354. Cache-heavy agent loop: $0.0975 vs $0.15.
- 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 1.388.8%SourceQuasar 438B69.3%SourceMuse Spark 1.3 leads this result
JobBench
Muse Spark 1.364.9%SourceQuasar 438B—Not directly comparable
OSWorld 2.0
Muse Spark 1.366.9%SourceQuasar 438B—Not directly comparable
DeepSearchQA
Muse Spark 1.389.4%SourceQuasar 438B—Not directly comparable
AutomationBench
Muse Spark 1.349.4%SourceQuasar 438B—Not directly comparable
Coding
Reasoning
Frequently asked questions
Which is better, Muse Spark 1.3 or Quasar 438B?
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 1.3 or Quasar 438B?
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 1.3 or Quasar 438B?
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 1.3 or Quasar 438B?
For the stated presets, chat costs $0.00337 on Muse Spark 1.3 and $0.0015 on Quasar 438B; repository review costs $0.07525 and $0.0354; the cache-heavy agent loop costs $0.0975 and $0.15. Quasar 438B has no published cached-input rate, so cached tokens use its listed input rate.
Which has the larger context window, Muse Spark 1.3 or Quasar 438B?
Both models list the same context window, 1M.
Related comparisons
Last updated September 2, 2026
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