Model A

Z.AI

—

Evidence status unavailable

90% interval unavailable

GLM-5.3-Flash vs Kimi K2.7 Code

Updated August 26, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

Model B

Kimi K2.7 Code

Moonshot AI

54.3/100

Estimated · Public rank #106

90% interval 42.9–65.7

Decision reading

The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. Use the documented cost, context, and runtime rows instead.

0 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
GLM-5.3-Flash
GLM-5.3-Flash 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: rate-fallback
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.

13 GLM-5.3-Flash only

8 Kimi K2.7 Code only

Shared results: 0
GLM-5.3-Flash only: 13
Kimi K2.7 Code only: 8
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.

Agentic

Not comparable

GLM-5.3-Flash: Not measured
Kimi K2.7 Code: Not measured
Weighted basis: 0 vs 0 rows
Reading: Not comparable

Coding

Not comparable

GLM-5.3-Flash: Not measured
Kimi K2.7 Code: Not measured
Weighted basis: 0 vs 0 rows
Reading: Not comparable

Reasoning

Not comparable

GLM-5.3-Flash: Not measured
Kimi K2.7 Code: Not measured
Weighted basis: 0 vs 0 rows
Reading: Not comparable

Knowledge

Not comparable

GLM-5.3-Flash: Not measured
Kimi K2.7 Code: Not measured
Weighted basis: 0 vs 0 rows
Reading: Not comparable

Math

Not comparable

GLM-5.3-Flash: Not measured
Kimi K2.7 Code: Not measured
Weighted basis: 0 vs 0 rows
Reading: Not comparable

Multilingual

Not comparable

GLM-5.3-Flash: Not measured
Kimi K2.7 Code: Not measured
Weighted basis: 0 vs 0 rows
Reading: Not comparable

Multimodal

Not comparable

GLM-5.3-Flash: 74.7
Kimi K2.7 Code: Not measured
Weighted basis: 2 vs 0 rows
Reading: Not comparable

Instruction following

Not comparable

GLM-5.3-Flash: Not measured
Kimi K2.7 Code: Not measured
Weighted basis: 0 vs 0 rows
Reading: Not comparable

Category averages with the server-provided evidence basis for GLM-5.3-Flash and Kimi K2.7 Code
Category	GLM-5.3-Flash	Kimi K2.7 Code	Weighted basis	Reading
Agentic	Not measured	Not measured	Not comparable0 vs 0 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	Not measured	Not comparable0 vs 0 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	74.7	Not measured	Not comparable2 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.

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-Flash: Self-hosted; infrastructure cost varies; Fits in one request
Kimi K2.7 Code: $0.00295; Fits in one request

GLM-5.3-Flash has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

GLM-5.3-Flash: Self-hosted; infrastructure cost varies; Fits in one request
Kimi K2.7 Code: $0.0595; Fits in one request

GLM-5.3-Flash has no comparable published API token rate.

Cache-heavy agent loop

200K cached + 20K fresh input + 10K output tokens

GLM-5.3-Flash: Self-hosted; infrastructure cost varies; Fits in one request; Cached-input rate unavailable
Kimi K2.7 Code: $0.249; Fits in one request; Cached input priced at the published list-input rate

Kimi K2.7 Code has no published cached-input rate, so cached tokens use its listed input rate. GLM-5.3-Flash 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-5.3-FlashKimi K2.7 Code

Context window

Maximum documented context; output-token limits may be lower.

GLM-5.3-Flash

GLM-5.3-Flash model card

Kimi K2.7 Code

256K

API model ID

GLM-5.3-Flash

glm-5.3-flash

GLM-5.3-Flash model card

Kimi K2.7 Code

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-Flash

No comparable hosted API rate

GLM-5.3-Flash model card

Kimi K2.7 Code

Not published

Documented inputs

GLM-5.3-Flash

Not sourced

Kimi K2.7 Code

Not sourced

Documented outputs

GLM-5.3-Flash

Not sourced

Kimi K2.7 Code

Not sourced

Provider availability

GLM-5.3-Flash

Not sourced

Kimi K2.7 Code

Not sourced

Reasoning profile

GLM-5.3-Flash

Reasoning

Kimi K2.7 Code

Reasoning

Weight access

GLM-5.3-Flash

Open Weight

Kimi K2.7 Code

Open Weight

License

GLM-5.3-Flash

Open Weight

Kimi K2.7 Code

Open Weight

Release date

GLM-5.3-Flash

2026-08-26

Kimi K2.7 Code

2026-06-12

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: The public evidence has no benchmark result shared by both models, so it does not support a quality verdict.
Workload cost: A complete comparable API-rate estimate is not available for both models.
Context tradeoff: GLM-5.3-Flash has the larger documented window (1M).

Run the same representative tasks against both endpoints before changing production traffic.

Self-host vs API cost

Estimates at 50,000 req/day · 1000 tokens/req average.

GLM-5.3-Flash

API / mo$0

Self-host / moNot listed

Break-even—

Proprietary model — self-hosting not applicable.

Kimi K2.7 Code

API / mo$3,713

Self-host / mo$18,221

Break-even326M/day

Model the full break-even

Benchmark evidence

The full public result ledger is available for audit without forcing a wide desktop table onto a phone.

Browse raw public benchmark evidence21 rows

Agentic

Terminal-Bench 2.1
GLM-5.3-Flash84.3%
Source
Kimi K2.7 Code—
Not directly comparable
Toolathlon-Verified
GLM-5.3-Flash78.4%
Source
Kimi K2.7 Code—
Not directly comparable
AutomationBench
GLM-5.3-Flash48.8%
Source
Kimi K2.7 Code—
Not directly comparable
Agents' Last Exam
GLM-5.3-Flash26.3%
Source
Kimi K2.7 Code—
Not directly comparable
HLE w/ tools
GLM-5.3-Flash55.3%
Source
Kimi K2.7 Code—
Not directly comparable
Kimi Claw 24/7
GLM-5.3-Flash—
Kimi K2.7 Code46.9%
Source
Not directly comparable
MCP Atlas
GLM-5.3-Flash—
Kimi K2.7 Code76%
Source
Not directly comparable
MCP Mark Verified
GLM-5.3-Flash—
Kimi K2.7 Code81.1%
Source
Not directly comparable

Coding

Terminal-Bench 2.1
GLM-5.3-Flash84.3%
Source
Kimi K2.7 Code—
Not directly comparable
deepSwe
GLM-5.3-Flash63.4%
Source
Kimi K2.7 Code—
Not directly comparable
NL2Repo
GLM-5.3-Flash56.3%
Source
Kimi K2.7 Code—
Not directly comparable
Kimi Code Bench v2
GLM-5.3-Flash—
Kimi K2.7 Code62.0%
Source
Not directly comparable
ProgramBench
GLM-5.3-Flash—
Kimi K2.7 Code53.6%
Source
Not directly comparable
MLS-Bench Lite
GLM-5.3-Flash—
Kimi K2.7 Code35.1%
Source
Not directly comparable
cursorBench32
GLM-5.3-Flash—
Kimi K2.7 Code49.7%
Source
Not directly comparable
OpenHarmony Bench
GLM-5.3-Flash—
Kimi K2.7 Code52.1%
Source
Not directly comparable

Multimodal

OfficeQA Pro
GLM-5.3-Flash62.4%
Source
Kimi K2.7 Code—
Not directly comparable
CharXiv
GLM-5.3-Flash89.4%
Source
Kimi K2.7 Code—
Not directly comparable
Chartography (tools)
GLM-5.3-Flash78.0%
Source
Kimi K2.7 Code—
Not directly comparable
BabyVision
GLM-5.3-Flash53.4%
Source
Kimi K2.7 Code—
Not directly comparable
MMVU
GLM-5.3-Flash80.5%
Source
Kimi K2.7 Code—
Not directly comparable

Frequently asked questions

Which is better, GLM-5.3-Flash or Kimi K2.7 Code?

The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. The page therefore keeps the decision tied to the specific documented workload.

Which is better for coding, GLM-5.3-Flash or Kimi K2.7 Code?

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-Flash or Kimi K2.7 Code?

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-Flash or Kimi K2.7 Code?

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-Flash or Kimi K2.7 Code?

GLM-5.3-Flash has the larger documented context window: 1M, compared with 256K.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated August 26, 2026

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GLM-5.3-Flash vs Kimi K2.7 Code

Which one for your work

Long documents

Coding work

Agentic work

Chat turn cost

Cache-heavy agent loop cost

Repository review cost

What is actually comparable

Category results, on a stated basis

Agentic

Coding

Reasoning

Knowledge

Math

Multilingual

Multimodal

Instruction following

Shape of the matched evidence

What each workload costs

Chat turn

Repository review

Cache-heavy agent loop

Specification differences

Context window

API model ID

Cached-input rate

Documented inputs

Documented outputs

Provider availability

Reasoning profile

Weight access

License

Release date

Self-host vs API cost

Benchmark evidence

Agentic

Coding

Multimodal

Frequently asked questions

Which is better, GLM-5.3-Flash or Kimi K2.7 Code?

Which is better for coding, GLM-5.3-Flash or Kimi K2.7 Code?

Which is better for agentic tasks, GLM-5.3-Flash or Kimi K2.7 Code?

Which costs less, GLM-5.3-Flash or Kimi K2.7 Code?

Which has the larger context window, GLM-5.3-Flash or Kimi K2.7 Code?

Related comparisons

Watch GLM-5.3-Flash vs Kimi K2.7 Code