Knowledge
Like-for-like- Agents-A1
- 47.6
- Muse Spark
- 50.4
- Weighted basis
- 1 vs 1 rows
- Reading
- Muse Spark leads
Agents-A1 vs Muse Spark
Updated August 21, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
Decision reading
Muse Spark has the higher public score estimate, 70.6 versus 61.25, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.
1 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
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
A complete comparable API-rate estimate is not available for both models.
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.
5 Agents-A1 only
1 Shared
23 Muse Spark only
- Shared results
- 1
- Agents-A1 only
- 5
- Muse Spark only
- 23
- Like-for-like categories
- 1 / 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.
Agentic
Not comparable- Agents-A1
- 75.5
- Muse Spark
- 59.0
- Weighted basis
- 1 vs 1 rows
- Reading
- Not comparable
Coding
Not comparable- Agents-A1
- Not measured
- Muse Spark
- 67.8
- Weighted basis
- 0 vs 2 rows
- Reading
- Not comparable
Reasoning
Not comparable- Agents-A1
- 60.2
- Muse Spark
- 42.5
- Weighted basis
- 1 vs 1 rows
- Reading
- Not comparable
Math
Not comparable- Agents-A1
- Not measured
- Muse Spark
- 32.9
- Weighted basis
- 0 vs 2 rows
- Reading
- Not comparable
Multilingual
Not comparable- Agents-A1
- Not measured
- Muse Spark
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Multimodal
Not comparable- Agents-A1
- Not measured
- Muse Spark
- 82.5
- Weighted basis
- 0 vs 2 rows
- Reading
- Not comparable
Instruction following
Not comparable- Agents-A1
- 94.8
- Muse Spark
- Not measured
- Weighted basis
- 1 vs 0 rows
- Reading
- Not comparable
| Category | Agents-A1 | Muse Spark | Weighted basis | Reading |
|---|---|---|---|---|
| Knowledge | 47.6 | 50.4 | Like-for-like1 vs 1 rows | Muse Spark leads |
| Agentic | 75.5 | 59.0 | Not comparable1 vs 1 rows | Not comparable |
| Coding | Not measured | 67.8 | Not comparable0 vs 2 rows | Not comparable |
| Reasoning | 60.2 | 42.5 | Not comparable1 vs 1 rows | Not comparable |
| Math | Not measured | 32.9 | Not comparable0 vs 2 rows | Not comparable |
| Multilingual | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Multimodal | Not measured | 82.5 | Not comparable0 vs 2 rows | Not comparable |
| Instruction following | 94.8 | Not measured | Not comparable1 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.
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.
HLE
Knowledge
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
- Agents-A1
- API rate not published
- Fits in one request
- Muse Spark
- API rate not published
- Fits in one request
Agents-A1 has no comparable published API token rate. Muse Spark has no comparable published API token rate.
Repository review
50K fresh input + 3K output tokens
- Agents-A1
- API rate not published
- Fits in one request
- Muse Spark
- API rate not published
- Fits in one request
Agents-A1 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
- Agents-A1
- API rate not published
- Fits in one request
- Cached-input rate unavailable
- Muse Spark
- API rate not published
- Fits in one request
- Cached-input rate unavailable
Agents-A1 has no comparable published API token rate. Muse Spark 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.
SpecificationAgents-A1Muse Spark
Context window
Maximum documented context; output-token limits may be lower.
Agents-A1
262K
Muse Spark
262K
API model ID
Agents-A1
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.
Agents-A1
No comparable hosted API rate
Muse Spark
No comparable hosted API rate
Documented inputs
Agents-A1
Not sourced
Muse Spark
Not sourced
Documented outputs
Agents-A1
Not sourced
Muse Spark
Not sourced
Provider availability
Agents-A1
Not sourced
Muse Spark
Not sourced
Reasoning profile
Agents-A1
Reasoning
Muse Spark
Reasoning
Weight access
Agents-A1
Open Weight
Muse Spark
Proprietary
License
Agents-A1
Open Weight
Muse Spark
Proprietary
Release date
Agents-A1
2026-06-26
Muse Spark
2026-04-08
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
- Muse Spark has the higher public score estimate, 70.6 versus 61.25, but the 90% score intervals overlap.
- Workload cost
- A complete comparable API-rate estimate is not available for both models.
- Context tradeoff
- Both models list 262K.
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 evidence29 rows
Agentic
BrowseComp
Agents-A175.5%SourceMuse Spark—Not directly comparable
HLE w/ tools
Agents-A147.6%SourceMuse Spark—Not directly comparable
VITA-Bench
Agents-A138.8%SourceMuse Spark—Not directly comparable
Terminal-Bench 2.0
Agents-A1—Muse Spark59%SourceNot directly comparable
τ²-bench results
Agents-A1—Muse Spark91.5%SourceNot directly comparable
DeepSearchQA
Agents-A1—Muse Spark74.8%SourceNot directly comparable
CyberGym
Agents-A1—Muse Spark43.5%SourceNot directly comparable
Claw-Eval
Agents-A1—Muse Spark63.8%SourceNot directly comparable
Coding
Reasoning
Knowledge
HLE
Agents-A147.6%SourceMuse Spark50.4%SourceMuse Spark leads this result
GPQA-D
Agents-A1—Muse Spark89.5%SourceNot directly comparable
HLE w/o tools
Agents-A1—Muse Spark42.8%SourceNot directly comparable
HealthBench Hard
Agents-A1—Muse Spark42.8%SourceNot directly comparable
MedXpertQA (Text)
Agents-A1—Muse Spark52.6%SourceNot directly comparable
Math
Multimodal
CharXiv
Agents-A1—Muse Spark86.4%SourceNot directly comparable
MMMU-Pro
Agents-A1—Muse Spark80.4%SourceNot directly comparable
ERQA
Agents-A1—Muse Spark64.7%SourceNot directly comparable
SimpleVQA
Agents-A1—Muse Spark71.3%SourceNot directly comparable
ScreenSpot Pro
Agents-A1—Muse Spark84.1%SourceNot directly comparable
ZeroBench
Agents-A1—Muse Spark33.0%SourceNot directly comparable
MedXpertQA (MM)
Agents-A1—Muse Spark78.4%SourceNot directly comparable
Instruction following
IFEval
Agents-A194.8%SourceMuse Spark—Not directly comparable
Frequently asked questions
Which is better, Agents-A1 or Muse Spark?
Muse Spark has the higher public score estimate, 70.6 versus 61.25, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.
Which is better for coding, Agents-A1 or Muse Spark?
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, Agents-A1 or Muse Spark?
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, Agents-A1 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, Agents-A1 or Muse Spark?
Both models list the same context window, 262K.
Related comparisons
Last updated August 21, 2026
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