Agentic
Like-for-like- GLM-4.7
- 45.7
- Kimi K3
- 89.5
- Weighted basis
- 2 vs 2 rows
- Reading
- Kimi K3 leads
GLM-4.7 vs Kimi K3
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, 80.53 versus 60.68, and the 90% score intervals do not overlap.
4 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.
Agentic work
Tool use, computer use, and multi-step task completion
Kimi K3
Kimi K3 leads on the same 2 weighted benchmark rows.
Confidence: limited
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
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
The page does not recommend a cost winner because at least one model cannot fit the stated workload in one request. GLM-4.7 does not fit this workload in one request. GLM-4.7 has no comparable published API token rate.
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.
9 GLM-4.7 only
4 Shared
36 Kimi K3 only
- Shared results
- 4
- GLM-4.7 only
- 9
- Kimi K3 only
- 36
- Like-for-like categories
- 1 / 8
1 category uses 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- GLM-4.7
- 51.8
- Kimi K3
- 61.0
- Weighted basis
- 3 vs 2 rows
- Reading
- Directional only
Coding
Not comparable- GLM-4.7
- 75.4
- Kimi K3
- Not measured
- Weighted basis
- 3 vs 0 rows
- Reading
- Not comparable
Reasoning
Not comparable- GLM-4.7
- Not measured
- Kimi K3
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Math
Not comparable- GLM-4.7
- 1.8
- Kimi K3
- Not measured
- Weighted basis
- 2 vs 0 rows
- Reading
- Not comparable
Multilingual
Not comparable- GLM-4.7
- Not measured
- Kimi K3
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Multimodal
Not comparable- GLM-4.7
- Not measured
- Kimi K3
- 78.5
- Weighted basis
- 0 vs 3 rows
- Reading
- Not comparable
Instruction following
Not comparable- GLM-4.7
- Not measured
- Kimi K3
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
| Category | GLM-4.7 | Kimi K3 | Weighted basis | Reading |
|---|---|---|---|---|
| Agentic | 45.7 | 89.5 | Like-for-like2 vs 2 rows | Kimi K3 leads |
| Knowledge | 51.8 | 61.0 | Directional only3 vs 2 rows | Directional only |
| Coding | 75.4 | Not measured | Not comparable3 vs 0 rows | Not comparable |
| Reasoning | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Math | 1.8 | Not measured | Not comparable2 vs 0 rows | Not comparable |
| Multilingual | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Multimodal | Not measured | 78.5 | Not comparable0 vs 3 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
BrowseComp
Agentic
HLE
Knowledge
GPQA
Knowledge
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
- GLM-4.7
- Self-hosted; infrastructure cost varies
- Fits in one request
- Kimi K3
- $0.0105
- Fits in one request
GLM-4.7 has no comparable published API token rate.
Repository review
50K fresh input + 3K output tokens
- GLM-4.7
- Self-hosted; infrastructure cost varies
- Fits in one request
- Kimi K3
- $0.195
- Fits in one request
GLM-4.7 has no comparable published API token rate.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- GLM-4.7
- Self-hosted; infrastructure cost varies
- Does not fit in one request
- Cached-input rate unavailable
- Kimi K3
- $0.27
- Fits in one request
GLM-4.7 does not fit this workload in one request. GLM-4.7 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.
SpecificationGLM-4.7Kimi K3
Context window
Maximum documented context; output-token limits may be lower.
GLM-4.7
200K
Kimi K3
1.05M
API model ID
GLM-4.7
Not sourced
Kimi K3
Not sourced
Cached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
GLM-4.7
No comparable hosted API rate
Kimi K3
$0.3 per 1M cached input tokens
Documented inputs
GLM-4.7
Not sourced
Kimi K3
Not sourced
Documented outputs
GLM-4.7
Not sourced
Kimi K3
Not sourced
Provider availability
GLM-4.7
Not sourced
Kimi K3
Not sourced
Reasoning profile
GLM-4.7
Reasoning
Kimi K3
Reasoning
Weight access
GLM-4.7
Open Weight
Kimi K3
Pending
License
GLM-4.7
Open Weight
Kimi K3
Pending
Release date
GLM-4.7
2025-10-01
Kimi K3
2026-07-16
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, 80.53 versus 60.68, and the 90% score intervals do not 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 evidence49 rows
Agentic
Terminal-Bench 2.0
GLM-4.741%SourceKimi K388.3%SourceKimi K3 leads this result
BrowseComp
GLM-4.752%SourceKimi K391.2%SourceKimi K3 leads this result
VITA-Bench
GLM-4.715.5%SourceKimi K3—Not directly comparable
Gert Labs
GLM-4.739.95%SourceKimi K3—Not directly comparable
DeepSearchQA
GLM-4.7—Kimi K395.0%SourceNot directly comparable
Toolathlon-Verified
GLM-4.7—Kimi K373.2%SourceNot directly comparable
MCP Atlas
GLM-4.7—Kimi K384.2%SourceNot directly comparable
AutomationBench
GLM-4.7—Kimi K330.8%SourceNot directly comparable
JobBench
GLM-4.7—Kimi K352.9%SourceNot directly comparable
APEX-Agents
GLM-4.7—Kimi K337.6%SourceNot directly comparable
SpreadsheetBench 2
GLM-4.7—Kimi K334.8%SourceNot directly comparable
DECK-Bench
GLM-4.7—Kimi K373.5%SourceNot directly comparable
Coding
SWE-bench Verified
GLM-4.773.8%SourceKimi K3—Not directly comparable
LiveCodeBench
GLM-4.784.9%SourceKimi K3—Not directly comparable
SWE-Rebench
GLM-4.758.7%SourceKimi K3—Not directly comparable
deepSwe
GLM-4.7—Kimi K367.5%SourceNot directly comparable
cursorBench32
GLM-4.7—Kimi K360.8%SourceNot directly comparable
FrontierSWE
GLM-4.7—Kimi K381.2%SourceNot directly comparable
ProgramBench
GLM-4.7—Kimi K377.8%SourceNot directly comparable
Kimi Code Bench v2
GLM-4.7—Kimi K372.9%SourceNot directly comparable
sweMarathon
GLM-4.7—Kimi K342%SourceNot directly comparable
PostTrain Bench
GLM-4.7—Kimi K336.6%SourceNot directly comparable
MLS-Bench Lite
GLM-4.7—Kimi K348.3%SourceNot directly comparable
VulcanBench v3
GLM-4.7—Kimi K373.9%SourceNot directly comparable
APEX-SWE
GLM-4.7—Kimi K348.0%SourceNot directly comparable
EEBench
GLM-4.7—Kimi K338.3%SourceNot directly comparable
InferenceEval
GLM-4.7—Kimi K339.8%SourceNot directly comparable
KernelBench Internal
GLM-4.7—Kimi K368.5%SourceNot directly comparable
Knowledge
GPQA
GLM-4.785.7%SourceKimi K393.5%SourceKimi K3 leads this result
MMLU-Pro
GLM-4.784.3%SourceKimi K3—Not directly comparable
HLE
GLM-4.724.8%SourceKimi K356%SourceKimi K3 leads this result
GPQA-D
GLM-4.7—Kimi K393.5%SourceNot directly comparable
HLE w/o tools
GLM-4.7—Kimi K343.5%SourceNot directly comparable
Math
Multimodal
OfficeQA Pro
GLM-4.7—Kimi K363.3%SourceNot directly comparable
MMMU-Pro
GLM-4.7—Kimi K381.6%SourceNot directly comparable
MMMU-Pro w/ Python
GLM-4.7—Kimi K383.4%SourceNot directly comparable
CharXiv w/o tools
GLM-4.7—Kimi K384.8%SourceNot directly comparable
CharXiv
GLM-4.7—Kimi K391.3%SourceNot directly comparable
MathVision
GLM-4.7—Kimi K394.3%SourceNot directly comparable
MathVision w/ Python
GLM-4.7—Kimi K397.8%SourceNot directly comparable
BabyVision w/ Python
GLM-4.7—Kimi K385.7%SourceNot directly comparable
ZeroBench
GLM-4.7—Kimi K323.0%SourceNot directly comparable
ZeroBench w/ Python
GLM-4.7—Kimi K341.0%SourceNot directly comparable
WorldVQA ForceAnswer
GLM-4.7—Kimi K351.0%SourceNot directly comparable
OmniDocBench
GLM-4.7—Kimi K391.1%SourceNot directly comparable
PerceptionBench
GLM-4.7—Kimi K358.5%SourceNot directly comparable
Frequently asked questions
Which is better, GLM-4.7 or Kimi K3?
Kimi K3 has the higher public score, 80.53 versus 60.68, and the 90% score intervals do not overlap. That is the clearest overall quality signal in the current public data.
Which is better for coding, GLM-4.7 or Kimi K3?
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, GLM-4.7 or Kimi K3?
Kimi K3 leads the like-for-like agentic tasks comparison across 2 shared weighted benchmark rows.
Which costs less, GLM-4.7 or Kimi K3?
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, GLM-4.7 or Kimi K3?
Kimi K3 has the larger documented context window: 1.05M, compared with 200K.
Related comparisons
Last updated August 19, 2026
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