Long documents
Prompts that approach the documented context limit
Muse Spark
Muse Spark has the larger documented context window.
Confidence: documented
MAI-Thinking-1 vs Muse Spark
Updated October 20, 2026. Rank says Muse Spark is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.
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Decision reading
Muse Spark has the higher public point estimate, 60.49 versus 53.1. Their conditional score ranges overlap. These ranges do not establish rank confidence. 4 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
- Shared results
- 4
- MAI-Thinking-1 only
- 9
- Muse Spark only
- 21
- Like-for-like categories
- 0 / 8
Estimated: MAI-Thinking-1 · 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.
Show secondary and unsupported calls
Coding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
MAI-Thinking-1 is not ranked on the public lane for coding, so no winner is named for coding.
Confidence: limitedAgentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
MAI-Thinking-1 is not ranked on the public lane for agentic, so no winner is named for agentic.
Confidence: limitedChat 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.
—MAI-Thinking-149.9Muse 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.
9 MAI-Thinking-1 only
4 Shared
21 Muse Spark only
2 categories rest 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.
Terminal-Bench 2.0Agentic
Normalized gap 13.0MAI-Thinking-1:46%Muse Spark:59%SWE-bench VerifiedCoding
Normalized gap 3.9MAI-Thinking-1:73.5%Muse Spark:77.4%SWE-bench ProCoding
Normalized gap 0.4MAI-Thinking-1:52.8%Muse Spark:52.4%
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.
Knowledge
Directional only- MAI-Thinking-1
- 55.5
- Estimated · #59/187
- Muse Spark
- 62.5
- Supported · #45/187
- Basis
- BenchAlign v5.8 lane · 3 vs 7 public rows
- Reading
- Directional only
Instruction following
Directional only- MAI-Thinking-1
- 94.7
- #3/149
- Muse Spark
- 93.3
- #10/149
- Basis
- Provisional lane · 1 vs 0 weighted rows
- Reading
- Directional only
Agentic
Not comparable- MAI-Thinking-1
- Not ranked
- Muse Spark
- 51.4
- Supported · #46/131
- Basis
- BenchAlign v5.8 lane · 1 vs 5 public rows
- Reading
- Not comparable
Coding
Not comparable- MAI-Thinking-1
- Not ranked
- Muse Spark
- 49.9
- Supported · #45/152
- Basis
- BenchAlign v5.8 lane · 4 vs 5 public rows
- Reading
- Not comparable
Reasoning
Not comparable- MAI-Thinking-1
- Not ranked
- Muse Spark
- 57.1
- Unranked · 3 rankable rows
- Basis
- Provisional lane · 0 vs 1 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- MAI-Thinking-1
- Not ranked
- Muse Spark
- 84.4
- #12/55
- Basis
- Provisional lane · 0 vs 2 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- MAI-Thinking-1
- Not ranked
- Muse Spark
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- MAI-Thinking-1
- 81.1
- Unranked · 3 rankable rows
- Muse Spark
- Not ranked
- Basis
- Provisional lane · 2 vs 0 weighted rows
- Reading
- Not comparable
| Category | MAI-Thinking-1 | Muse Spark | Basis | Reading |
|---|---|---|---|---|
| Knowledge | 55.5Estimated · #59/187 | 62.5Supported · #45/187 | Directional onlyBenchAlign v5.8 lane · 3 vs 7 public rows | Directional only |
| Instruction following | 94.7#3/149 | 93.3#10/149 | Directional onlyProvisional lane · 1 vs 0 weighted rows | Directional only |
| Agentic | Not ranked | 51.4Supported · #46/131 | Not comparableBenchAlign v5.8 lane · 1 vs 5 public rows | Not comparable |
| Coding | Not ranked | 49.9Supported · #45/152 | Not comparableBenchAlign v5.8 lane · 4 vs 5 public rows | Not comparable |
| Reasoning | Not ranked | 57.1Unranked · 3 rankable rows | Not comparableProvisional lane · 0 vs 1 weighted rows | Not comparable |
| Multimodal | Not ranked | 84.4#12/55 | Not comparableProvisional lane · 0 vs 2 weighted rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | 81.1Unranked · 3 rankable rows | Not ranked | Not comparableProvisional lane · 2 vs 0 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
- MAI-Thinking-1
- API rate not published
- Fits in one request
- Muse Spark
- API rate not published
- Fits in one request
MAI-Thinking-1 has no comparable published API token rate. Muse Spark has no comparable published API token rate.
Repository review
50K fresh input + 3K output tokens
- MAI-Thinking-1
- API rate not published
- Fits in one request
- Muse Spark
- API rate not published
- Fits in one request
MAI-Thinking-1 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
- MAI-Thinking-1
- 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
MAI-Thinking-1 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.
SpecificationMAI-Thinking-1Muse Spark
Context window
Maximum documented context; output-token limits may be lower.
MAI-Thinking-1
256K
Muse Spark
262K
API model ID
MAI-Thinking-1
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.
MAI-Thinking-1
No comparable hosted API rate
Muse Spark
No comparable hosted API rate
Documented inputs
MAI-Thinking-1
Not sourced
Muse Spark
Not sourced
Documented outputs
MAI-Thinking-1
Not sourced
Muse Spark
Not sourced
Provider availability
MAI-Thinking-1
Not sourced
Muse Spark
Not sourced
Reasoning profile
MAI-Thinking-1
Reasoning
Muse Spark
Reasoning
Weight access
MAI-Thinking-1
Proprietary
Muse Spark
Proprietary
License
MAI-Thinking-1
Proprietary
Muse Spark
Proprietary
Release date
MAI-Thinking-1
2026-06-02
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, 60.49 versus 53.1. 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
- Muse Spark has the larger documented window (262K).
Run the same representative tasks against both endpoints before changing production traffic.
Questions
Which is better, MAI-Thinking-1 or Muse Spark?
Muse Spark has the higher public point estimate, 60.49 versus 53.1. 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, MAI-Thinking-1 or Muse Spark?
MAI-Thinking-1 is not ranked on the public lane for coding, so no winner is named for coding.
Which is better for agentic tasks, MAI-Thinking-1 or Muse Spark?
MAI-Thinking-1 is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.
Which costs less, MAI-Thinking-1 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, MAI-Thinking-1 or Muse Spark?
Muse Spark has the larger documented context window: 262K, compared with 256K.
Benchmark evidence
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Browse raw public benchmark evidence34 rows
Agentic
Terminal-Bench 2.0
MAI-Thinking-146%SourceMuse Spark59%SourceMuse Spark leads this result
τ²-bench results
MAI-Thinking-1—Muse Spark91.5%SourceNot directly comparable
DeepSearchQA
MAI-Thinking-1—Muse Spark74.8%SourceNot directly comparable
CyberGym
MAI-Thinking-1—Muse Spark43.5%SourceNot directly comparable
Claw-Eval
MAI-Thinking-1—Muse Spark63.8%SourceNot directly comparable
Coding
LiveCodeBench v6
MAI-Thinking-187.7%SourceMuse Spark—Not directly comparable
SWE-bench Verified
MAI-Thinking-173.5%SourceMuse Spark77.4%SourceMuse Spark leads this result
SWE-bench Pro
MAI-Thinking-152.8%SourceMuse Spark52.4%SourceMAI-Thinking-1 leads this result
Terminal-Bench 2.0
MAI-Thinking-146.0%SourceMuse Spark—Not directly comparable
LiveCodeBench Pro
MAI-Thinking-1—Muse Spark80.0%SourceNot directly comparable
Vibe Code Bench
MAI-Thinking-1—Muse Spark19.67%SourceNot directly comparable
SWE-bench (Vals)
MAI-Thinking-1—Muse Spark74.4%SourceNot directly comparable
Reasoning
Multimodal
CharXiv
MAI-Thinking-1—Muse Spark86.4%SourceNot directly comparable
MMMU-Pro
MAI-Thinking-1—Muse Spark80.4%SourceNot directly comparable
ERQA
MAI-Thinking-1—Muse Spark64.7%SourceNot directly comparable
SimpleVQA
MAI-Thinking-1—Muse Spark71.3%SourceNot directly comparable
ScreenSpot Pro
MAI-Thinking-1—Muse Spark84.1%SourceNot directly comparable
ZeroBench
MAI-Thinking-1—Muse Spark33.0%SourceNot directly comparable
MedXpertQA (MM)
MAI-Thinking-1—Muse Spark78.4%SourceNot directly comparable
Knowledge
GPQA
MAI-Thinking-184.2%SourceMuse Spark—Not directly comparable
GPQA-D
MAI-Thinking-184.2%SourceMuse Spark89.5%SourceMuse Spark leads this result
MMLU-Pro
MAI-Thinking-185%SourceMuse Spark—Not directly comparable
HLE
MAI-Thinking-1—Muse Spark50.4%SourceNot directly comparable
HLE w/o tools
MAI-Thinking-1—Muse Spark42.8%SourceNot directly comparable
HealthBench Hard
MAI-Thinking-1—Muse Spark42.8%SourceNot directly comparable
MedXpertQA (Text)
MAI-Thinking-1—Muse Spark52.6%SourceNot directly comparable
GPQA Diamond (Vals)
MAI-Thinking-1—Muse Spark89.6%SourceNot directly comparable
MMLU-Pro (Vals)
MAI-Thinking-1—Muse Spark87.3%SourceNot directly comparable
Instruction following
IFBench
MAI-Thinking-185%SourceMuse Spark—Not directly comparable
Math
34 public results · 4 shared
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Last updated October 20, 2026