Agentic
Directional only- Kimi K3
- 89.5
- Muse Spark
- 59.0
- Weighted basis
- 2 vs 1 rows
- Reading
- Directional only
Kimi K3 vs Muse Spark
Updated August 19, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
Decision reading
Kimi K3 has the higher public score estimate, 80.53 versus 70.84, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.
8 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
Kimi K3
Kimi K3 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
The category averages use different weighted benchmark sets, so they are directional rather than like-for-like.
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
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.
32 Kimi K3 only
8 Shared
16 Muse Spark only
- Shared results
- 8
- Kimi K3 only
- 32
- Muse Spark only
- 16
- Like-for-like categories
- 0 / 8
3 categories use different evidence sets. Those rows remain visible for coverage context but do not name a winner.
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.
Knowledge
Directional only- Kimi K3
- 61.0
- Muse Spark
- 50.4
- Weighted basis
- 2 vs 1 rows
- Reading
- Directional only
Multimodal
Directional only- Kimi K3
- 78.5
- Muse Spark
- 82.5
- Weighted basis
- 3 vs 2 rows
- Reading
- Directional only
Coding
Not comparable- Kimi K3
- Not measured
- Muse Spark
- 67.8
- Weighted basis
- 0 vs 2 rows
- Reading
- Not comparable
Reasoning
Not comparable- Kimi K3
- Not measured
- Muse Spark
- 42.5
- Weighted basis
- 0 vs 1 rows
- Reading
- Not comparable
Math
Not comparable- Kimi K3
- Not measured
- Muse Spark
- 32.9
- Weighted basis
- 0 vs 2 rows
- Reading
- Not comparable
Multilingual
Not comparable- Kimi K3
- Not measured
- Muse Spark
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Instruction following
Not comparable- Kimi K3
- Not measured
- Muse Spark
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
| Category | Kimi K3 | Muse Spark | Weighted basis | Reading |
|---|---|---|---|---|
| Agentic | 89.5 | 59.0 | Directional only2 vs 1 rows | Directional only |
| Knowledge | 61.0 | 50.4 | Directional only2 vs 1 rows | Directional only |
| Multimodal | 78.5 | 82.5 | Directional only3 vs 2 rows | Directional only |
| Coding | Not measured | 67.8 | Not comparable0 vs 2 rows | Not comparable |
| Reasoning | Not measured | 42.5 | Not comparable0 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 |
| 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.
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 2.0
Agentic
HLE
Knowledge
CharXiv
Multimodal
MMMU-Pro
Multimodal
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
- API rate not published
- Fits in one request
Muse Spark has no comparable published API token rate.
Repository review
50K fresh input + 3K output tokens
- Kimi K3
- $0.195
- Fits in one request
- Muse Spark
- API rate not published
- Fits in one request
Muse Spark has no comparable published API token rate.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- Kimi K3
- $0.27
- Fits in one request
- Muse Spark
- API rate not published
- Fits in one request
- Cached-input rate unavailable
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.
SpecificationKimi K3Muse Spark
Context window
Maximum documented context; output-token limits may be lower.
Kimi K3
1.05M
Muse Spark
262K
API model ID
Kimi K3
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.
Kimi K3
$0.3 per 1M cached input tokens
Muse Spark
No comparable hosted API rate
Documented inputs
Kimi K3
Not sourced
Muse Spark
Not sourced
Documented outputs
Kimi K3
Not sourced
Muse Spark
Not sourced
Provider availability
Kimi K3
Not sourced
Muse Spark
Not sourced
Reasoning profile
Kimi K3
Reasoning
Muse Spark
Reasoning
Weight access
Kimi K3
Pending
Muse Spark
Proprietary
License
Kimi K3
Pending
Muse Spark
Proprietary
Release date
Kimi K3
2026-07-16
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
- Kimi K3 has the higher public score estimate, 80.53 versus 70.84, but the 90% score intervals overlap.
- Workload cost
- A complete comparable API-rate estimate is not available for both models.
- Context tradeoff
- Kimi K3 has the larger documented window (1.05M).
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 evidence56 rows
Agentic
Terminal-Bench 2.0
Kimi K388.3%SourceMuse Spark59%SourceKimi K3 leads this result
BrowseComp
Kimi K391.2%SourceMuse Spark—Not directly comparable
DeepSearchQA
Kimi K395.0%SourceMuse Spark74.8%SourceKimi K3 leads this result
Toolathlon-Verified
Kimi K373.2%SourceMuse Spark—Not directly comparable
MCP Atlas
Kimi K384.2%SourceMuse Spark—Not directly comparable
AutomationBench
Kimi K330.8%SourceMuse Spark—Not directly comparable
JobBench
Kimi K352.9%SourceMuse Spark—Not directly comparable
APEX-Agents
Kimi K337.6%SourceMuse Spark—Not directly comparable
SpreadsheetBench 2
Kimi K334.8%SourceMuse Spark—Not directly comparable
DECK-Bench
Kimi K373.5%SourceMuse Spark—Not directly comparable
τ²-bench results
Kimi K3—Muse Spark91.5%SourceNot directly comparable
CyberGym
Kimi K3—Muse Spark43.5%SourceNot directly comparable
Claw-Eval
Kimi K3—Muse Spark63.8%SourceNot directly comparable
Coding
deepSwe
Kimi K367.5%SourceMuse Spark—Not directly comparable
cursorBench32
Kimi K360.8%SourceMuse Spark—Not directly comparable
FrontierSWE
Kimi K381.2%SourceMuse Spark—Not directly comparable
ProgramBench
Kimi K377.8%SourceMuse Spark—Not directly comparable
Kimi Code Bench v2
Kimi K372.9%SourceMuse Spark—Not directly comparable
sweMarathon
Kimi K342%SourceMuse Spark—Not directly comparable
PostTrain Bench
Kimi K336.6%SourceMuse Spark—Not directly comparable
MLS-Bench Lite
Kimi K348.3%SourceMuse Spark—Not directly comparable
VulcanBench v3
Kimi K373.9%SourceMuse Spark—Not directly comparable
APEX-SWE
Kimi K348.0%SourceMuse Spark—Not directly comparable
EEBench
Kimi K338.3%SourceMuse Spark—Not directly comparable
InferenceEval
Kimi K339.8%SourceMuse Spark—Not directly comparable
KernelBench Internal
Kimi K368.5%SourceMuse Spark—Not directly comparable
SWE-bench Verified
Kimi K3—Muse Spark77.4%SourceNot directly comparable
SWE-bench Pro
Kimi K3—Muse Spark52.4%SourceNot directly comparable
LiveCodeBench Pro
Kimi K3—Muse Spark80.0%SourceNot directly comparable
Vibe Code Bench
Kimi K3—Muse Spark19.67%SourceNot directly comparable
Reasoning
ARC-AGI-2
Kimi K3—Muse Spark42.5%SourceNot directly comparable
Knowledge
GPQA
Kimi K393.5%SourceMuse Spark—Not directly comparable
GPQA-D
Kimi K393.5%SourceMuse Spark89.5%SourceKimi K3 leads this result
HLE
Kimi K356%SourceMuse Spark50.4%SourceKimi K3 leads this result
HLE w/o tools
Kimi K343.5%SourceMuse Spark42.8%SourceKimi K3 leads this result
HealthBench Hard
Kimi K3—Muse Spark42.8%SourceNot directly comparable
MedXpertQA (Text)
Kimi K3—Muse Spark52.6%SourceNot directly comparable
Math
Multimodal
OfficeQA Pro
Kimi K363.3%SourceMuse Spark—Not directly comparable
MMMU-Pro
Kimi K381.6%SourceMuse Spark80.4%SourceKimi K3 leads this result
MMMU-Pro w/ Python
Kimi K383.4%SourceMuse Spark—Not directly comparable
CharXiv w/o tools
Kimi K384.8%SourceMuse Spark—Not directly comparable
CharXiv
Kimi K391.3%SourceMuse Spark86.4%SourceKimi K3 leads this result
MathVision
Kimi K394.3%SourceMuse Spark—Not directly comparable
MathVision w/ Python
Kimi K397.8%SourceMuse Spark—Not directly comparable
BabyVision w/ Python
Kimi K385.7%SourceMuse Spark—Not directly comparable
ZeroBench
Kimi K323.0%SourceMuse Spark33.0%SourceMuse Spark leads this result
ZeroBench w/ Python
Kimi K341.0%SourceMuse Spark—Not directly comparable
WorldVQA ForceAnswer
Kimi K351.0%SourceMuse Spark—Not directly comparable
OmniDocBench
Kimi K391.1%SourceMuse Spark—Not directly comparable
PerceptionBench
Kimi K358.5%SourceMuse Spark—Not directly comparable
ERQA
Kimi K3—Muse Spark64.7%SourceNot directly comparable
SimpleVQA
Kimi K3—Muse Spark71.3%SourceNot directly comparable
ScreenSpot Pro
Kimi K3—Muse Spark84.1%SourceNot directly comparable
MedXpertQA (MM)
Kimi K3—Muse Spark78.4%SourceNot directly comparable
Frequently asked questions
Which is better, Kimi K3 or Muse Spark?
Kimi K3 has the higher public score estimate, 80.53 versus 70.84, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.
Which is better for coding, Kimi K3 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, Kimi K3 or Muse Spark?
The current agentic tasks averages use different weighted benchmark sets, so BenchLM does not name a winner from them. Read the shared benchmark rows directly and test the models on the same task set.
Which costs less, Kimi K3 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, Kimi K3 or Muse Spark?
Kimi K3 has the larger documented context window: 1.05M, compared with 262K.
Related comparisons
Last updated August 19, 2026
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