Agentic
Not comparable- Exaone 4.0 32B
- Not measured
- Muse Spark 1.3
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Exaone 4.0 32B vs Muse Spark 1.3
Updated September 2, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
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 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.
Long documents
Prompts that approach the documented context limit
Muse Spark 1.3
Muse Spark 1.3 has the larger documented context window.
Confidence: documented
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
Chat 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-rates
Cache-heavy agent loop cost
200K cached + 20K fresh input + 10K output tokens
Not enough matched evidence
The page does not recommend a cost winner because at least one model cannot fit the stated workload in one request. Exaone 4.0 32B does not fit this workload in one request. Exaone 4.0 32B has no comparable published API token rate.
Confidence: listed-rates
Repository 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
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
2 Exaone 4.0 32B only
10 Muse Spark 1.3 only
- Shared results
- 0
- Exaone 4.0 32B only
- 2
- Muse Spark 1.3 only
- 10
- 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- Exaone 4.0 32B
- Not measured
- Muse Spark 1.3
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Reasoning
Not comparable- Exaone 4.0 32B
- Not measured
- Muse Spark 1.3
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Knowledge
Not comparable- Exaone 4.0 32B
- 81.8
- Muse Spark 1.3
- Not measured
- Weighted basis
- 1 vs 0 rows
- Reading
- Not comparable
Math
Not comparable- Exaone 4.0 32B
- Not measured
- Muse Spark 1.3
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Multilingual
Not comparable- Exaone 4.0 32B
- Not measured
- Muse Spark 1.3
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Multimodal
Not comparable- Exaone 4.0 32B
- Not measured
- Muse Spark 1.3
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Instruction following
Not comparable- Exaone 4.0 32B
- Not measured
- Muse Spark 1.3
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
| Category | Exaone 4.0 32B | 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 | 81.8 | Not measured | Not comparable1 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
- Exaone 4.0 32B
- API rate not published
- Fits in one request
- Muse Spark 1.3
- $0.00338
- Fits in one request
Exaone 4.0 32B has no comparable published API token rate.
Repository review
50K fresh input + 3K output tokens
- Exaone 4.0 32B
- API rate not published
- Fits in one request
- Muse Spark 1.3
- $0.07525
- Fits in one request
Exaone 4.0 32B has no comparable published API token rate.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- Exaone 4.0 32B
- API rate not published
- Does not fit in one request
- Cached-input rate unavailable
- Muse Spark 1.3
- $0.0975
- Fits in one request
Exaone 4.0 32B does not fit this workload in one request. Exaone 4.0 32B has no comparable published API token rate.
Specification differences
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
SpecificationExaone 4.0 32BMuse Spark 1.3
Context window
Maximum documented context; output-token limits may be lower.
Exaone 4.0 32B
128K
Muse Spark 1.3
API model ID
Exaone 4.0 32B
Not sourced
Muse 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.
Exaone 4.0 32B
No comparable hosted API rate
Muse Spark 1.3
$0.15 per 1M cached input tokens
Meta: Muse Spark 1.3 model pageDocumented inputs
Exaone 4.0 32B
Not sourced
Muse Spark 1.3
text, image, video, document
Meta Muse Spark 1.3 model pageDocumented outputs
Exaone 4.0 32B
Not sourced
Muse Spark 1.3
Provider availability
Exaone 4.0 32B
Not sourced
Muse Spark 1.3
Generally Available · Meta Model API, Muse Code
Meta AI Research Muse Spark 1.3 launchReasoning profile
Exaone 4.0 32B
Reasoning
Muse Spark 1.3
Reasoning
Weight access
Exaone 4.0 32B
Open Weight
Muse Spark 1.3
Proprietary
License
Exaone 4.0 32B
Open Weight
Muse Spark 1.3
Proprietary
Release date
Exaone 4.0 32B
Not sourced
Muse Spark 1.3
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
- The public evidence has no benchmark result shared by both models, so it does not support a quality verdict.
- Workload cost
- A complete comparable API-rate estimate is not available for both models.
- Context tradeoff
- Muse Spark 1.3 has the larger documented window (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 evidence12 rows
Agentic
Terminal-Bench 2.1
Exaone 4.0 32B—Muse Spark 1.388.8%SourceNot directly comparable
JobBench
Exaone 4.0 32B—Muse Spark 1.364.9%SourceNot directly comparable
OSWorld 2.0
Exaone 4.0 32B—Muse Spark 1.366.9%SourceNot directly comparable
DeepSearchQA
Exaone 4.0 32B—Muse Spark 1.389.4%SourceNot directly comparable
AutomationBench
Exaone 4.0 32B—Muse Spark 1.349.4%SourceNot directly comparable
Coding
Reasoning
Knowledge
MMLU-Pro
Exaone 4.0 32B81.8%SourceMuse Spark 1.3—Not directly comparable
Math
AIME 2025
Exaone 4.0 32B85.3%SourceMuse Spark 1.3—Not directly comparable
Frequently asked questions
Which is better, Exaone 4.0 32B or Muse Spark 1.3?
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, Exaone 4.0 32B 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, Exaone 4.0 32B 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, Exaone 4.0 32B or Muse Spark 1.3?
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, Exaone 4.0 32B or Muse Spark 1.3?
Muse Spark 1.3 has the larger documented context window: 1M, compared with 128K.
Related comparisons
Last updated September 2, 2026
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