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 Glimmer 30B
Updated October 2, 2026. Rank says Kimi K3 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
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Decision reading
Kimi K3 has the higher public point estimate, 72.14 versus 42.28. Their conditional score ranges do not 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
- Kimi K3 only
- 44
- Muse Glimmer 30B only
- 10
- Like-for-like categories
- 1 / 8
Supported: Kimi K3 · Estimated: Muse Glimmer 30B. 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
Muse Glimmer 30B is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like.
Confidence: limitedAgentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
Muse Glimmer 30B is scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.
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
The page does not recommend a cost winner because at least one model cannot fit the stated workload in one request. Muse Glimmer 30B does not fit this workload in one request. Muse Glimmer 30B has no comparable published API token rate.
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.
61.4Kimi K332.9Muse Glimmer 30B
Directional only · BenchAlign v5.8
Kimi K3 has the higher coding point estimate. Conditional score ranges do not establish rank confidence.
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.
44 Kimi K3 only
4 Shared
10 Muse Glimmer 30B only
3 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.
CharXivMultimodal
Normalized gap 12.5Kimi K3:91.3%Muse Glimmer 30B:78.8%MMMU-ProMultimodal
Normalized gap 7.6Kimi K3:81.6%Muse Glimmer 30B:74%
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
Like-for-like- Kimi K3
- 67.9
- Supported · #18/171
- Muse Glimmer 30B
- 42.6
- Supported · #91/171
- Basis
- BenchAlign v5.8 lane · 6 vs 0 public rows
- Reading
- Kimi K3 leads
Agentic
Directional only- Kimi K3
- 68.1
- Supported · #8/119
- Muse Glimmer 30B
- 24.0
- Estimated · #94/119
- Basis
- BenchAlign v5.8 lane · 12 vs 4 public rows
- Reading
- Directional only
Coding
Directional only- Kimi K3
- 61.4
- Supported · #18/144
- Muse Glimmer 30B
- 32.9
- Estimated · #87/144
- Basis
- BenchAlign v5.8 lane · 14 vs 4 public rows
- Reading
- Directional only
Multimodal
Directional only- Kimi K3
- 89.4
- #1/49
- Muse Glimmer 30B
- 47.4
- #41/49
- Basis
- Provisional lane · 3 vs 2 weighted rows
- Reading
- Directional only
Reasoning
Not comparable- Kimi K3
- 65.8
- #18/27
- Muse Glimmer 30B
- 79.6
- Unranked · 2 rankable rows
- Basis
- Provisional lane · 1 vs 0 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- Kimi K3
- Not ranked
- Muse Glimmer 30B
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- Kimi K3
- Not ranked
- Muse Glimmer 30B
- 77.0
- #55/125
- Basis
- Provisional lane · 0 vs 1 weighted rows
- Reading
- Not comparable
Math
Not comparable- Kimi K3
- Not ranked
- Muse Glimmer 30B
- 75.4
- Unranked · 1 rankable row
- Basis
- Provisional lane · 0 vs 1 weighted rows
- Reading
- Not comparable
| Category | Kimi K3 | Muse Glimmer 30B | Basis | Reading |
|---|---|---|---|---|
| Knowledge | 67.9Supported · #18/171 | 42.6Supported · #91/171 | Like-for-likeBenchAlign v5.8 lane · 6 vs 0 public rows | Kimi K3 leads |
| Agentic | 68.1Supported · #8/119 | 24.0Estimated · #94/119 | Directional onlyBenchAlign v5.8 lane · 12 vs 4 public rows | Directional only |
| Coding | 61.4Supported · #18/144 | 32.9Estimated · #87/144 | Directional onlyBenchAlign v5.8 lane · 14 vs 4 public rows | Directional only |
| Multimodal | 89.4#1/49 | 47.4#41/49 | Directional onlyProvisional lane · 3 vs 2 weighted rows | Directional only |
| Reasoning | 65.8#18/27 | 79.6Unranked · 2 rankable rows | Not comparableProvisional lane · 1 vs 0 weighted rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Instruction following | Not ranked | 77.0#55/125 | Not comparableProvisional lane · 0 vs 1 weighted rows | Not comparable |
| Math | Not ranked | 75.4Unranked · 1 rankable row | Not comparableProvisional lane · 0 vs 1 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 Glimmer 30B
- Self-hosted; infrastructure cost varies
- Fits in one request
Muse Glimmer 30B has no comparable published API token rate.
Repository review
50K fresh input + 3K output tokens
- Kimi K3
- $0.195
- Fits in one request
- Muse Glimmer 30B
- Self-hosted; infrastructure cost varies
- Fits in one request
Muse Glimmer 30B 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 Glimmer 30B
- Self-hosted; infrastructure cost varies
- Does not fit in one request
- Cached-input rate unavailable
Muse Glimmer 30B does not fit this workload in one request. Muse Glimmer 30B 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.
SpecificationKimi K3Muse Glimmer 30B
Context window
Maximum documented context; output-token limits may be lower.
Kimi K3
1.05M
Muse Glimmer 30B
131K
API model ID
Kimi K3
Not sourced
Muse Glimmer 30B
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 Glimmer 30B
No comparable hosted API rate
Documented inputs
Kimi K3
Not sourced
Muse Glimmer 30B
Not sourced
Documented outputs
Kimi K3
Not sourced
Muse Glimmer 30B
Not sourced
Provider availability
Kimi K3
Not sourced
Muse Glimmer 30B
Not sourced
Reasoning profile
Kimi K3
Reasoning
Muse Glimmer 30B
Reasoning
Weight access
Kimi K3
Pending
Muse Glimmer 30B
Open Weight
License
Kimi K3
Pending
Muse Glimmer 30B
Open Weight
Release date
Kimi K3
2026-07-16
Muse Glimmer 30B
2026-08-10
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 point estimate, 72.14 versus 42.28. Their conditional score ranges do not overlap. These ranges do not establish rank confidence.
- 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.
Questions
Which is better, Kimi K3 or Muse Glimmer 30B?
Kimi K3 has the higher public point estimate, 72.14 versus 42.28. Their conditional score ranges do not 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, Kimi K3 or Muse Glimmer 30B?
Kimi K3 scores higher for coding on the public lane, 61.4 to 32.9. Muse Glimmer 30B is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like. BenchLM does not name a winner for coding from a directional reading; read the shared benchmark rows directly and test the models on the same task set.
Which is better for agentic tasks, Kimi K3 or Muse Glimmer 30B?
Kimi K3 scores higher for agentic tasks on the public lane, 68.1 to 24. Muse Glimmer 30B 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, Kimi K3 or Muse Glimmer 30B?
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 Glimmer 30B?
Kimi K3 has the larger documented context window: 1.05M, compared with 131K.
Benchmark evidence
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Browse raw public benchmark evidence58 rows
Agentic
Terminal-Bench 2.1
Kimi K388.3%SourceMuse Glimmer 30B—Not directly comparable
BrowseComp
Kimi K391.2%SourceMuse Glimmer 30B—Not directly comparable
DeepSearchQA
Kimi K395.0%SourceMuse Glimmer 30B74.6%SourceKimi K3 leads this result
Toolathlon-Verified
Kimi K373.2%SourceMuse Glimmer 30B—Not directly comparable
MCP Atlas
Kimi K384.2%SourceMuse Glimmer 30B75.5%SourceKimi K3 leads this result
AutomationBench
Kimi K330.8%SourceMuse Glimmer 30B—Not directly comparable
JobBench
Kimi K352.9%SourceMuse Glimmer 30B—Not directly comparable
APEX-Agents
Kimi K337.6%SourceMuse Glimmer 30B—Not directly comparable
SpreadsheetBench 2
Kimi K334.8%SourceMuse Glimmer 30B—Not directly comparable
DECK-Bench
Kimi K373.5%SourceMuse Glimmer 30B—Not directly comparable
Terminal-Bench 2.1 (Vals)
Kimi K380.9%SourceMuse Glimmer 30B—Not directly comparable
ApprenticeBench
Kimi K318%SourceMuse Glimmer 30B—Not directly comparable
skillsBench
Kimi K3—Muse Glimmer 30B44.3%SourceNot directly comparable
OSWorld-Verified
Kimi K3—Muse Glimmer 30B65.9%SourceNot directly comparable
Coding
DeepSWE
Kimi K367.5%SourceMuse Glimmer 30B—Not directly comparable
CursorBench 3.2
Kimi K360.8%SourceMuse Glimmer 30B—Not directly comparable
FrontierSWE
Kimi K381.2%SourceMuse Glimmer 30B—Not directly comparable
ProgramBench
Kimi K377.8%SourceMuse Glimmer 30B—Not directly comparable
Kimi Code Bench v2
Kimi K372.9%SourceMuse Glimmer 30B—Not directly comparable
sweMarathon
Kimi K342%SourceMuse Glimmer 30B—Not directly comparable
PostTrain Bench
Kimi K336.6%SourceMuse Glimmer 30B—Not directly comparable
MLS-Bench Lite
Kimi K348.3%SourceMuse Glimmer 30B—Not directly comparable
VulcanBench v3
Kimi K373.7%SourceMuse Glimmer 30B—Not directly comparable
OpenHarmony Bench
Kimi K357.3%SourceMuse Glimmer 30B—Not directly comparable
FrontierSWE v2
Kimi K325.9%SourceMuse Glimmer 30B—Not directly comparable
LiveCodeBench (Vals)
Kimi K387.2%SourceMuse Glimmer 30B—Not directly comparable
SWE-bench (Vals)
Kimi K393.4%SourceMuse Glimmer 30B—Not directly comparable
PostTrainBench v1.1
Kimi K332.0%SourceMuse Glimmer 30B—Not directly comparable
SWE-bench Pro
Kimi K3—Muse Glimmer 30B51.2%SourceNot directly comparable
SWE-bench Verified
Kimi K3—Muse Glimmer 30B76%SourceNot directly comparable
Terminal-Bench 2.1
Kimi K3—Muse Glimmer 30B51.7%SourceNot directly comparable
SciCode
Kimi K3—Muse Glimmer 30B43.6%SourceNot directly comparable
Reasoning
Multimodal
OfficeQA Pro
Kimi K363.3%SourceMuse Glimmer 30B—Not directly comparable
MMMU-Pro
Kimi K381.6%SourceMuse Glimmer 30B74%SourceKimi K3 leads this result
MMMU-Pro w/ Python
Kimi K383.4%SourceMuse Glimmer 30B—Not directly comparable
CharXiv w/o tools
Kimi K384.8%SourceMuse Glimmer 30B—Not directly comparable
CharXiv
Kimi K391.3%SourceMuse Glimmer 30B78.8%SourceKimi K3 leads this result
MathVision
Kimi K394.3%SourceMuse Glimmer 30B—Not directly comparable
MathVision w/ Python
Kimi K397.8%SourceMuse Glimmer 30B—Not directly comparable
BabyVision w/ Python
Kimi K385.7%SourceMuse Glimmer 30B—Not directly comparable
ZeroBench
Kimi K323.0%SourceMuse Glimmer 30B—Not directly comparable
ZeroBench w/ Python
Kimi K341.0%SourceMuse Glimmer 30B—Not directly comparable
WorldVQA ForceAnswer
Kimi K351.0%SourceMuse Glimmer 30B—Not directly comparable
OmniDocBench
Kimi K391.1%SourceMuse Glimmer 30B—Not directly comparable
PerceptionBench
Kimi K358.5%SourceMuse Glimmer 30B—Not directly comparable
ScreenSpot Pro
Kimi K3—Muse Glimmer 30B75.4%SourceNot directly comparable
OmniDocBench 1.5
Kimi K3—Muse Glimmer 30B75.8%SourceNot directly comparable
Knowledge
GPQA
Kimi K393.5%SourceMuse Glimmer 30B—Not directly comparable
GPQA-D
Kimi K393.5%SourceMuse Glimmer 30B—Not directly comparable
HLE
Kimi K356%SourceMuse Glimmer 30B—Not directly comparable
HLE w/o tools
Kimi K343.5%SourceMuse Glimmer 30B—Not directly comparable
GPQA Diamond (Vals)
Kimi K392.9%SourceMuse Glimmer 30B—Not directly comparable
MMLU-Pro (Vals)
Kimi K388.0%SourceMuse Glimmer 30B—Not directly comparable
Instruction following
Math
AIME26
Kimi K3—Muse Glimmer 30B94.7%SourceNot directly comparable
58 public results · 4 shared
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Last updated October 2, 2026