Agentic
Not comparable- GLM-5.3
- Not measured
- Kimi K3
- 89.5
- Weighted basis
- 0 vs 2 rows
- Reading
- Not comparable
GLM-5.3 vs Kimi K3
Updated August 14, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
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.
7 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
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
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.
9 GLM-5.3 only
7 Shared
33 Kimi K3 only
- Shared results
- 7
- GLM-5.3 only
- 9
- Kimi K3 only
- 33
- Like-for-like categories
- 0 / 8
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.
Coding
Not comparable- GLM-5.3
- Not measured
- Kimi K3
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Reasoning
Not comparable- GLM-5.3
- Not measured
- Kimi K3
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Knowledge
Not comparable- GLM-5.3
- Not measured
- Kimi K3
- 61.0
- Weighted basis
- 0 vs 2 rows
- Reading
- Not comparable
Math
Not comparable- GLM-5.3
- Not measured
- Kimi K3
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Multilingual
Not comparable- GLM-5.3
- Not measured
- Kimi K3
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Multimodal
Not comparable- GLM-5.3
- Not measured
- Kimi K3
- 78.5
- Weighted basis
- 0 vs 3 rows
- Reading
- Not comparable
Instruction following
Not comparable- GLM-5.3
- Not measured
- Kimi K3
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
| Category | GLM-5.3 | Kimi K3 | Weighted basis | Reading |
|---|---|---|---|---|
| Agentic | Not measured | 89.5 | Not comparable0 vs 2 rows | Not comparable |
| Coding | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Reasoning | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Knowledge | Not measured | 61.0 | Not comparable0 vs 2 rows | Not comparable |
| Math | Not measured | Not measured | Not comparable0 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.
A shared-evidence shape is not available.
BenchLM does not draw a radar or infer missing axes when the matched evidence is too sparse.
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-5.3
- API rate not published
- Fits in one request
- Kimi K3
- $0.0105
- Fits in one request
GLM-5.3 has no comparable published API token rate.
Repository review
50K fresh input + 3K output tokens
- GLM-5.3
- API rate not published
- Fits in one request
- Kimi K3
- $0.195
- Fits in one request
GLM-5.3 has no comparable published API token rate.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- GLM-5.3
- API rate not published
- Fits in one request
- Cached-input rate unavailable
- Kimi K3
- $0.27
- Fits in one request
GLM-5.3 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-5.3Kimi K3
Context window
Maximum documented context; output-token limits may be lower.
GLM-5.3
Kimi K3
1.05M
API model ID
GLM-5.3
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-5.3
No comparable hosted API rate
Z.AI GLM-5.3 launch postKimi K3
$0.3 per 1M cached input tokens
Documented inputs
GLM-5.3
Not sourced
Kimi K3
Not sourced
Documented outputs
GLM-5.3
Not sourced
Kimi K3
Not sourced
Provider availability
GLM-5.3
Not sourced
Kimi K3
Not sourced
Reasoning profile
GLM-5.3
Reasoning
Kimi K3
Reasoning
Weight access
GLM-5.3
Proprietary
Kimi K3
Pending
License
GLM-5.3
Proprietary
Kimi K3
Pending
Release date
GLM-5.3
2026-08-14
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
- 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
- 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 2.1
GLM-5.388.2%SourceKimi K3—Not directly comparable
terminalBench3
GLM-5.328.3%SourceKimi K3—Not directly comparable
CyberGym
GLM-5.384.5%SourceKimi K3—Not directly comparable
ExploitGym
GLM-5.315.0%SourceKimi K3—Not directly comparable
Toolathlon-Verified
GLM-5.373.0%SourceKimi K373.2%SourceKimi K3 leads this result
AutomationBench
GLM-5.348.2%SourceKimi K330.8%SourceGLM-5.3 leads this result
Agents' Last Exam
GLM-5.328.5%SourceKimi K3—Not directly comparable
HLE w/ tools
GLM-5.362.5%SourceKimi K3—Not directly comparable
Terminal-Bench 2.0
GLM-5.3—Kimi K388.3%SourceNot directly comparable
BrowseComp
GLM-5.3—Kimi K391.2%SourceNot directly comparable
DeepSearchQA
GLM-5.3—Kimi K395.0%SourceNot directly comparable
MCP Atlas
GLM-5.3—Kimi K384.2%SourceNot directly comparable
JobBench
GLM-5.3—Kimi K352.9%SourceNot directly comparable
APEX-Agents
GLM-5.3—Kimi K337.6%SourceNot directly comparable
SpreadsheetBench 2
GLM-5.3—Kimi K334.8%SourceNot directly comparable
DECK-Bench
GLM-5.3—Kimi K373.5%SourceNot directly comparable
Coding
Terminal-Bench 2.1
GLM-5.388.2%SourceKimi K3—Not directly comparable
terminalBench3
GLM-5.328.3%SourceKimi K3—Not directly comparable
deepSwe
GLM-5.366.9%SourceKimi K367.5%SourceKimi K3 leads this result
NL2Repo
GLM-5.358%SourceKimi K3—Not directly comparable
ProgramBench
GLM-5.319.0%SourceKimi K377.8%SourceKimi K3 leads this result
FrontierSWE
GLM-5.378.1%SourceKimi K381.2%SourceKimi K3 leads this result
sweMarathon
GLM-5.342.5%SourceKimi K342%SourceGLM-5.3 leads this result
PostTrain Bench
GLM-5.339.8%SourceKimi K336.6%SourceGLM-5.3 leads this result
cursorBench32
GLM-5.3—Kimi K360.8%SourceNot directly comparable
Kimi Code Bench v2
GLM-5.3—Kimi K372.9%SourceNot directly comparable
MLS-Bench Lite
GLM-5.3—Kimi K348.3%SourceNot directly comparable
VulcanBench v3
GLM-5.3—Kimi K373.9%SourceNot directly comparable
APEX-SWE
GLM-5.3—Kimi K348.0%SourceNot directly comparable
EEBench
GLM-5.3—Kimi K338.3%SourceNot directly comparable
InferenceEval
GLM-5.3—Kimi K339.8%SourceNot directly comparable
KernelBench Internal
GLM-5.3—Kimi K368.5%SourceNot directly comparable
Knowledge
Multimodal
OfficeQA Pro
GLM-5.3—Kimi K363.3%SourceNot directly comparable
MMMU-Pro
GLM-5.3—Kimi K381.6%SourceNot directly comparable
MMMU-Pro w/ Python
GLM-5.3—Kimi K383.4%SourceNot directly comparable
CharXiv w/o tools
GLM-5.3—Kimi K384.8%SourceNot directly comparable
CharXiv
GLM-5.3—Kimi K391.3%SourceNot directly comparable
MathVision
GLM-5.3—Kimi K394.3%SourceNot directly comparable
MathVision w/ Python
GLM-5.3—Kimi K397.8%SourceNot directly comparable
BabyVision w/ Python
GLM-5.3—Kimi K385.7%SourceNot directly comparable
ZeroBench
GLM-5.3—Kimi K323.0%SourceNot directly comparable
ZeroBench w/ Python
GLM-5.3—Kimi K341.0%SourceNot directly comparable
WorldVQA ForceAnswer
GLM-5.3—Kimi K351.0%SourceNot directly comparable
OmniDocBench
GLM-5.3—Kimi K391.1%SourceNot directly comparable
PerceptionBench
GLM-5.3—Kimi K358.5%SourceNot directly comparable
Frequently asked questions
Which is better, GLM-5.3 or Kimi K3?
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, GLM-5.3 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-5.3 or Kimi K3?
The published evidence does not provide a shared weighted agentic tasks basis for both models, so BenchLM does not name a agentic tasks winner.
Which costs less, GLM-5.3 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-5.3 or Kimi K3?
Kimi K3 has the larger documented context window: 1.05M, compared with 1M.
Related comparisons
Last updated August 14, 2026
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