Coding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
Agents-A1 is not ranked on the public lane for coding, so no winner is named for coding.
Confidence: limited
Agents-A1 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
Share or export
Decision reading
Muse Spark has the higher public point estimate, 59.99 versus 53.24. Their conditional score ranges overlap. These ranges do not establish rank confidence. 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
- Agents-A1 only
- 5
- Muse Spark only
- 26
- Like-for-like categories
- 0 / 8
Estimated: Agents-A1 · Supported: Muse Spark. Conditional ranges do not establish rank confidence.How 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.
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
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
Agents-A1 is scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.
Confidence: limitedLong documents
Prompts that approach the documented context limit
No clear pick
The documented context windows are equal.
Confidence: documentedChat 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.
—Agents-A151.1Muse Spark
Not comparable · BenchAlign v5.8
The coding row is not comparable on the public lane: at least one model is not measured or not ranked there.
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.
5 Agents-A1 only
1 Shared
26 Muse Spark only
1 category rests 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.
HLEKnowledge
Normalized gap 2.8Agents-A1:47.6%Muse Spark:50.4%
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- Agents-A1
- 44.9
- Estimated · #46/119
- Muse Spark
- 49.8
- Supported · #43/119
- Basis
- BenchAlign v5.8 lane · 3 vs 5 public rows
- Reading
- Directional only
Coding
Not comparable- Agents-A1
- Not ranked
- Muse Spark
- 51.1
- Supported · #39/144
- Basis
- BenchAlign v5.8 lane · 0 vs 5 public rows
- Reading
- Not comparable
Reasoning
Not comparable- Agents-A1
- 35.2
- Unranked · 1 rankable row
- Muse Spark
- 53.4
- Unranked · 3 rankable rows
- Basis
- Provisional lane · 1 vs 1 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- Agents-A1
- Not ranked
- Muse Spark
- 78.5
- #14/49
- Basis
- Provisional lane · 0 vs 2 weighted rows
- Reading
- Not comparable
Knowledge
Not comparable- Agents-A1
- Not ranked
- Muse Spark
- 60.3
- Supported · #40/171
- Basis
- BenchAlign v5.8 lane · 1 vs 7 public rows
- Reading
- Not comparable
Multilingual
Not comparable- Agents-A1
- Not ranked
- Muse Spark
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- Agents-A1
- Not ranked
- Muse Spark
- 91.9
- #8/125
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- Agents-A1
- Not ranked
- Muse Spark
- 55.1
- Unranked · 2 rankable rows
- Basis
- Provisional lane · 0 vs 2 weighted rows
- Reading
- Not comparable
| Category | Agents-A1 | Muse Spark | Basis | Reading |
|---|---|---|---|---|
| Agentic | 44.9Estimated · #46/119 | 49.8Supported · #43/119 | Directional onlyBenchAlign v5.8 lane · 3 vs 5 public rows | Directional only |
| Coding | Not ranked | 51.1Supported · #39/144 | Not comparableBenchAlign v5.8 lane · 0 vs 5 public rows | Not comparable |
| Reasoning | 35.2Unranked · 1 rankable row | 53.4Unranked · 3 rankable rows | Not comparableProvisional lane · 1 vs 1 weighted rows | Not comparable |
| Multimodal | Not ranked | 78.5#14/49 | Not comparableProvisional lane · 0 vs 2 weighted rows | Not comparable |
| Knowledge | Not ranked | 60.3Supported · #40/171 | Not comparableBenchAlign v5.8 lane · 1 vs 7 public rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Instruction following | Not ranked | 91.9#8/125 | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | Not ranked | 55.1Unranked · 2 rankable rows | Not comparableProvisional lane · 0 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
- 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.
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.
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 point estimate, 59.99 versus 53.24. Their conditional score ranges overlap. These ranges do not establish rank confidence.
- 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.
Questions
Which is better, Agents-A1 or Muse Spark?
Muse Spark has the higher public point estimate, 59.99 versus 53.24. Their conditional score ranges overlap. These ranges do not establish rank confidence. The page therefore keeps the decision tied to the specific documented workload.
Which is better for coding, Agents-A1 or Muse Spark?
Agents-A1 is not ranked on the public lane for coding, so no winner is named for coding.
Which is better for agentic tasks, Agents-A1 or Muse Spark?
Muse Spark scores higher for agentic tasks on the public lane, 49.8 to 44.9. Agents-A1 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, 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.
Benchmark evidence
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Browse raw public benchmark evidence32 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
SWE-bench Verified
Agents-A1—Muse Spark77.4%SourceNot directly comparable
SWE-bench Pro
Agents-A1—Muse Spark52.4%SourceNot directly comparable
LiveCodeBench Pro
Agents-A1—Muse Spark80.0%SourceNot directly comparable
Vibe Code Bench
Agents-A1—Muse Spark19.67%SourceNot directly comparable
SWE-bench (Vals)
Agents-A1—Muse Spark74.4%SourceNot directly comparable
Reasoning
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
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
GPQA Diamond (Vals)
Agents-A1—Muse Spark89.6%SourceNot directly comparable
MMLU-Pro (Vals)
Agents-A1—Muse Spark87.3%SourceNot directly comparable
Instruction following
IFEval
Agents-A194.8%SourceMuse Spark—Not directly comparable
Math
32 public results · 1 shared
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Last updated October 5, 2026