Long documents
Prompts that approach the documented context limit
Kimi K3
Kimi K3 has the larger documented context window.
Confidence: documented
Kimi K3 vs Muse Spark 1.3
Updated October 17, 2026. We do not rank this pair: at least one has no public score. Public scores include evidence status and uncertainty.
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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. 11 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
- Shared results
- 11
- Kimi K3 only
- 41
- Muse Spark 1.3 only
- 9
- Like-for-like categories
- 0 / 8
Supported: Kimi K3How 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.
Chat turn cost
1K fresh input + 500 output tokens
Muse Spark 1.3
Muse Spark 1.3 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-ratesCache-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. Costs use the listed standard API rates.
Confidence: listed-rates
Show secondary and unsupported calls
Repository review cost
50K fresh input + 3K output tokens
Muse Spark 1.3
Muse Spark 1.3 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-ratesCoding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
Muse Spark 1.3 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
Muse Spark 1.3 is not ranked on the public lane for agentic, so no winner is named for agentic.
Confidence: limited
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.
58.9Kimi K3—Muse Spark 1.3
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.
41 Kimi K3 only
11 Shared
9 Muse Spark 1.3 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.
AutomationBenchAgentic
Normalized gap 18.6Kimi K3:30.8%Muse Spark 1.3:49.4%
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.
Reasoning
Directional only- Kimi K3
- 70.0
- #21/28
- Muse Spark 1.3
- 82.9
- #6/28
- Basis
- Provisional lane · 1 vs 0 weighted rows
- Reading
- Directional only
Agentic
Not comparable- Kimi K3
- 69.4
- Supported · #10/128
- Muse Spark 1.3
- Not ranked
- Basis
- BenchAlign v5.8 lane · 15 vs 9 public rows
- Reading
- Not comparable
Coding
Not comparable- Kimi K3
- 58.9
- Supported · #22/150
- Muse Spark 1.3
- Not ranked
- Basis
- BenchAlign v5.8 lane · 15 vs 6 public rows
- Reading
- Not comparable
Multimodal
Not comparable- Kimi K3
- 92.6
- #1/54
- Muse Spark 1.3
- Not ranked
- Basis
- Provisional lane · 3 vs 0 weighted rows
- Reading
- Not comparable
Knowledge
Not comparable- Kimi K3
- 68.8
- Supported · #21/184
- Muse Spark 1.3
- Not ranked
- Basis
- BenchAlign v5.8 lane · 6 vs 0 public rows
- Reading
- Not comparable
Multilingual
Not comparable- Kimi K3
- Not ranked
- Muse Spark 1.3
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- Kimi K3
- Not ranked
- Muse Spark 1.3
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- Kimi K3
- Not ranked
- Muse Spark 1.3
- 78.5
- Unranked · 2 rankable rows
- Basis
- Provisional lane · 0 vs 2 weighted rows
- Reading
- Not comparable
| Category | Kimi K3 | Muse Spark 1.3 | Basis | Reading |
|---|---|---|---|---|
| Reasoning | 70.0#21/28 | 82.9#6/28 | Directional onlyProvisional lane · 1 vs 0 weighted rows | Directional only |
| Agentic | 69.4Supported · #10/128 | Not ranked | Not comparableBenchAlign v5.8 lane · 15 vs 9 public rows | Not comparable |
| Coding | 58.9Supported · #22/150 | Not ranked | Not comparableBenchAlign v5.8 lane · 15 vs 6 public rows | Not comparable |
| Multimodal | 92.6#1/54 | Not ranked | Not comparableProvisional lane · 3 vs 0 weighted rows | Not comparable |
| Knowledge | 68.8Supported · #21/184 | Not ranked | Not comparableBenchAlign v5.8 lane · 6 vs 0 public rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Instruction following | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | Not ranked | 78.5Unranked · 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
- Kimi K3
- $0.0105
- Fits in one request
- Muse Spark 1.3
- $0.00338
- Fits in one request
Muse Spark 1.3 has the lower modeled cost
Costs use the listed standard API rates.
Repository review
50K fresh input + 3K output tokens
- Kimi K3
- $0.195
- Fits in one request
- Muse Spark 1.3
- $0.07525
- Fits in one request
Muse Spark 1.3 has the lower modeled cost
Costs use the listed standard API rates.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- Kimi K3
- $0.27
- Fits in one request
- Muse Spark 1.3
- $0.0975
- Fits in one request
Muse Spark 1.3 has the lower modeled cost
Costs use the listed standard API rates.
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.
SpecificationKimi K3Muse Spark 1.3
Context window
Maximum documented context; output-token limits may be lower.
Kimi K3
1.05M
Muse Spark 1.3
API model ID
Kimi K3
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.
Kimi K3
$0.3 per 1M cached input tokens
Muse Spark 1.3
$0.15 per 1M cached input tokens
Meta: Muse Spark 1.3 model pageDocumented inputs
Kimi K3
Not sourced
Muse Spark 1.3
text, image, video, document
Meta Muse Spark 1.3 model pageDocumented outputs
Kimi K3
Not sourced
Muse Spark 1.3
Provider availability
Kimi K3
Not sourced
Muse Spark 1.3
Generally Available · Meta Model API, Muse Code
Meta AI Research Muse Spark 1.3 launchReasoning profile
Kimi K3
Reasoning
Muse Spark 1.3
Reasoning
Weight access
Kimi K3
Pending
Muse Spark 1.3
Proprietary
License
Kimi K3
Pending
Muse Spark 1.3
Proprietary
Release date
Kimi K3
2026-07-16
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
- 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: $0.195 vs $0.07525. Cache-heavy agent loop: $0.27 vs $0.0975.
- Context tradeoff
- Kimi K3 has the larger documented window (1.05M).
Run the same representative tasks against both endpoints before changing production traffic.
Questions
Which is better, Kimi K3 or Muse Spark 1.3?
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, Kimi K3 or Muse Spark 1.3?
Muse Spark 1.3 is not ranked on the public lane for coding, so no winner is named for coding.
Which is better for agentic tasks, Kimi K3 or Muse Spark 1.3?
Muse Spark 1.3 is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.
Which costs less, Kimi K3 or Muse Spark 1.3?
For the stated presets, chat costs $0.0105 on Kimi K3 and $0.00337 on Muse Spark 1.3; repository review costs $0.195 and $0.07525; the cache-heavy agent loop costs $0.27 and $0.0975. Costs use the listed standard API rates.
Which has the larger context window, Kimi K3 or Muse Spark 1.3?
Kimi K3 has the larger documented context window: 1.05M, compared with 1M.
Benchmark evidence
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Browse raw public benchmark evidence61 rows
Agentic
Terminal-Bench 2.1
Kimi K388.3%SourceMuse Spark 1.388.8%SourceMuse Spark 1.3 leads this result
BrowseComp
Kimi K391.2%SourceMuse Spark 1.3—Not directly comparable
DeepSearchQA
Kimi K395.0%SourceMuse Spark 1.389.4%SourceKimi K3 leads this result
Toolathlon-Verified
Kimi K373.2%SourceMuse Spark 1.3—Not directly comparable
MCP Atlas
Kimi K384.2%SourceMuse Spark 1.3—Not directly comparable
AutomationBench
Kimi K330.8%SourceMuse Spark 1.349.4%SourceMuse Spark 1.3 leads this result
JobBench
Kimi K352.9%SourceMuse Spark 1.364.9%SourceMuse Spark 1.3 leads this result
APEX-Agents
Kimi K337.6%SourceMuse Spark 1.3—Not directly comparable
SpreadsheetBench 2
Kimi K334.8%SourceMuse Spark 1.3—Not directly comparable
DECK-Bench
Kimi K373.5%SourceMuse Spark 1.3—Not directly comparable
Terminal-Bench 2.1 (Vals)
Kimi K380.9%SourceMuse Spark 1.372.3%SourceKimi K3 leads this result
ApprenticeBench
Shared sourceKimi K318%Muse Spark 1.319%Muse Spark 1.3 leads this result
Hermes Index
Kimi K339.2%SourceMuse Spark 1.3—Not directly comparable
Hermes Bench
Kimi K371.5%SourceMuse Spark 1.3—Not directly comparable
MazeBench
Kimi K31%SourceMuse Spark 1.30%SourceKimi K3 leads this result
OSWorld 2.0
Kimi K3—Muse Spark 1.366.9%SourceNot directly comparable
CWE-bench v1
Kimi K3—Muse Spark 1.355.0%SourceNot directly comparable
Coding
DeepSWE
Kimi K367.5%SourceMuse Spark 1.375.4%SourceMuse Spark 1.3 leads this result
CursorBench 3.2
Kimi K360.8%SourceMuse Spark 1.3—Not directly comparable
FrontierSWE
Kimi K381.2%SourceMuse Spark 1.3—Not directly comparable
ProgramBench
Kimi K377.8%SourceMuse Spark 1.3—Not directly comparable
Kimi Code Bench v2
Kimi K372.9%SourceMuse Spark 1.3—Not directly comparable
sweMarathon
Kimi K342%SourceMuse Spark 1.3—Not directly comparable
PostTrain Bench
Kimi K336.6%SourceMuse Spark 1.3—Not directly comparable
MLS-Bench Lite
Kimi K348.3%SourceMuse Spark 1.3—Not directly comparable
VulcanBench v3
Kimi K373.7%SourceMuse Spark 1.3—Not directly comparable
OpenHarmony Bench
Kimi K357.3%SourceMuse Spark 1.3—Not directly comparable
FrontierSWE v2
Shared sourceKimi K325.9%Muse Spark 1.325.8%Kimi K3 leads this result
LiveCodeBench (Vals)
Kimi K387.2%SourceMuse Spark 1.3—Not directly comparable
SWE-bench (Vals)
Kimi K393.4%SourceMuse Spark 1.3—Not directly comparable
PostTrainBench v1.1
Kimi K332.0%SourceMuse Spark 1.3—Not directly comparable
Bug Hunt Bench
Shared sourceKimi K321.0 fixesMuse Spark 1.332.2 fixesMuse Spark 1.3 leads this result
Terminal-Bench 2.1
Kimi K3—Muse Spark 1.388.8%SourceNot directly comparable
SWE-Atlas Codebase QnA
Kimi K3—Muse Spark 1.359.4%SourceNot directly comparable
CursorBench 4.0
Kimi K3—Muse Spark 1.341.6%SourceNot directly comparable
Reasoning
Multimodal
OfficeQA Pro
Kimi K363.3%SourceMuse Spark 1.3—Not directly comparable
MMMU-Pro
Kimi K381.6%SourceMuse Spark 1.3—Not directly comparable
MMMU-Pro w/ Python
Kimi K383.4%SourceMuse Spark 1.3—Not directly comparable
CharXiv w/o tools
Kimi K384.8%SourceMuse Spark 1.3—Not directly comparable
CharXiv
Kimi K391.3%SourceMuse Spark 1.3—Not directly comparable
MathVision
Kimi K394.3%SourceMuse Spark 1.3—Not directly comparable
MathVision w/ Python
Kimi K397.8%SourceMuse Spark 1.3—Not directly comparable
BabyVision w/ Python
Kimi K385.7%SourceMuse Spark 1.3—Not directly comparable
ZeroBench
Kimi K323.0%SourceMuse Spark 1.3—Not directly comparable
ZeroBench w/ Python
Kimi K341.0%SourceMuse Spark 1.3—Not directly comparable
WorldVQA ForceAnswer
Kimi K351.0%SourceMuse Spark 1.3—Not directly comparable
OmniDocBench
Kimi K391.1%SourceMuse Spark 1.3—Not directly comparable
PerceptionBench
Kimi K358.5%SourceMuse Spark 1.3—Not directly comparable
Knowledge
GPQA
Kimi K393.5%SourceMuse Spark 1.3—Not directly comparable
GPQA-D
Kimi K393.5%SourceMuse Spark 1.3—Not directly comparable
HLE
Kimi K356%SourceMuse Spark 1.3—Not directly comparable
HLE w/o tools
Kimi K343.5%SourceMuse Spark 1.3—Not directly comparable
GPQA Diamond (Vals)
Kimi K392.9%SourceMuse Spark 1.3—Not directly comparable
MMLU-Pro (Vals)
Kimi K388.0%SourceMuse Spark 1.3—Not directly comparable
Instruction following
Gray Swan IPI (15 attempts)
Shared sourceKimi K352.7%Muse Spark 1.315.9%Muse Spark 1.3 leads this result
Math
61 public results · 11 shared
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Last updated October 17, 2026