Model A

Z.AI

—

Evidence status unavailable

90% interval unavailable

GLM-5.3 vs Qwen3.6-27B

Updated August 14, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

Model B

Qwen3.6-27B

Alibaba

53.5/100

Estimated · Public rank #103

90% interval 42.0–65.1

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.

1 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
GLM-5.3 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.

15 GLM-5.3 only

1 Shared

37 Qwen3.6-27B only

Shared results: 1
GLM-5.3 only: 15
Qwen3.6-27B only: 37
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: Not measured
Qwen3.6-27B: 59.3
Weighted basis: 0 vs 1 rows
Reading: Not comparable

Coding

Not comparable

GLM-5.3: Not measured
Qwen3.6-27B: 77.5
Weighted basis: 0 vs 3 rows
Reading: Not comparable

Reasoning

Not comparable

GLM-5.3: Not measured
Qwen3.6-27B: Not measured
Weighted basis: 0 vs 0 rows
Reading: Not comparable

Knowledge

Not comparable

GLM-5.3: Not measured
Qwen3.6-27B: 53.3
Weighted basis: 0 vs 4 rows
Reading: Not comparable

Math

Not comparable

GLM-5.3: Not measured
Qwen3.6-27B: 89.2
Weighted basis: 0 vs 2 rows
Reading: Not comparable

Multilingual

Not comparable

GLM-5.3: Not measured
Qwen3.6-27B: Not measured
Weighted basis: 0 vs 0 rows
Reading: Not comparable

Multimodal

Not comparable

GLM-5.3: Not measured
Qwen3.6-27B: 76.7
Weighted basis: 0 vs 2 rows
Reading: Not comparable

Instruction following

Not comparable

GLM-5.3: Not measured
Qwen3.6-27B: Not measured
Weighted basis: 0 vs 0 rows
Reading: Not comparable

Category averages with the server-provided evidence basis for GLM-5.3 and Qwen3.6-27B
Category	GLM-5.3	Qwen3.6-27B	Weighted basis	Reading
Agentic	Not measured	59.3	Not comparable0 vs 1 rows	Not comparable
Coding	Not measured	77.5	Not comparable0 vs 3 rows	Not comparable
Reasoning	Not measured	Not measured	Not comparable0 vs 0 rows	Not comparable
Knowledge	Not measured	53.3	Not comparable0 vs 4 rows	Not comparable
Math	Not measured	89.2	Not comparable0 vs 2 rows	Not comparable
Multilingual	Not measured	Not measured	Not comparable0 vs 0 rows	Not comparable
Multimodal	Not measured	76.7	Not comparable0 vs 2 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
Qwen3.6-27B: Self-hosted; infrastructure cost varies; Fits in one request

GLM-5.3 has no comparable published API token rate. Qwen3.6-27B 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
Qwen3.6-27B: Self-hosted; infrastructure cost varies; Fits in one request

GLM-5.3 has no comparable published API token rate. Qwen3.6-27B 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
Qwen3.6-27B: Self-hosted; infrastructure cost varies; Fits in one request; Cached-input rate unavailable

GLM-5.3 has no comparable published API token rate. Qwen3.6-27B 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-5.3Qwen3.6-27B

Context window

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

GLM-5.3

Z.AI GLM-5.3 launch post

Qwen3.6-27B

262K

API model ID

GLM-5.3

Not sourced

Qwen3.6-27B

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 post

Qwen3.6-27B

No comparable hosted API rate

Documented inputs

GLM-5.3

Not sourced

Qwen3.6-27B

Not sourced

Documented outputs

GLM-5.3

Not sourced

Qwen3.6-27B

Not sourced

Provider availability

GLM-5.3

Not sourced

Qwen3.6-27B

Not sourced

Reasoning profile

GLM-5.3

Reasoning

Qwen3.6-27B

Reasoning

Weight access

GLM-5.3

Proprietary

Qwen3.6-27B

Open Weight

License

GLM-5.3

Proprietary

Qwen3.6-27B

Open Weight

Release date

GLM-5.3

2026-08-14

Qwen3.6-27B

2026-04-21

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: GLM-5.3 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

API / mo$0

Self-host / moNot listed

Break-even—

Proprietary model — self-hosting not applicable.

Qwen3.6-27B

API / mo$0

Self-host / mo$429

Break-even—

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 evidence53 rows

Agentic

Terminal-Bench 2.1
GLM-5.388.2%
Source
Qwen3.6-27B—
Not directly comparable
terminalBench3
GLM-5.328.3%
Source
Qwen3.6-27B—
Not directly comparable
CyberGym
GLM-5.384.5%
Source
Qwen3.6-27B—
Not directly comparable
ExploitGym
GLM-5.315.0%
Source
Qwen3.6-27B—
Not directly comparable
Toolathlon-Verified
GLM-5.373.0%
Source
Qwen3.6-27B—
Not directly comparable
AutomationBench
GLM-5.348.2%
Source
Qwen3.6-27B—
Not directly comparable
Agents' Last Exam
GLM-5.328.5%
Source
Qwen3.6-27B—
Not directly comparable
HLE w/ tools
GLM-5.362.5%
Source
Qwen3.6-27B—
Not directly comparable
Terminal-Bench 2.0
GLM-5.3—
Qwen3.6-27B59.3%
Source
Not directly comparable
Claw-Eval
GLM-5.3—
Qwen3.6-27B72.4%
Source
Not directly comparable
QwenClawBench
GLM-5.3—
Qwen3.6-27B53.4%
Source
Not directly comparable
QwenWebBench
GLM-5.3—
Qwen3.6-27B1487
Source
Not directly comparable
AndroidWorld
GLM-5.3—
Qwen3.6-27B70.3%
Source
Not directly comparable
Gert Labs
GLM-5.3—
Qwen3.6-27B54.84%
Source
Not directly comparable

Coding

Terminal-Bench 2.1
GLM-5.388.2%
Source
Qwen3.6-27B—
Not directly comparable
terminalBench3
GLM-5.328.3%
Source
Qwen3.6-27B—
Not directly comparable
deepSwe
GLM-5.366.9%
Source
Qwen3.6-27B—
Not directly comparable
NL2Repo
GLM-5.358%
Source
Qwen3.6-27B36.2%
Source
GLM-5.3 leads this result
ProgramBench
GLM-5.319.0%
Source
Qwen3.6-27B—
Not directly comparable
FrontierSWE
GLM-5.378.1%
Source
Qwen3.6-27B—
Not directly comparable
sweMarathon
GLM-5.342.5%
Source
Qwen3.6-27B—
Not directly comparable
PostTrain Bench
GLM-5.339.8%
Source
Qwen3.6-27B—
Not directly comparable
SWE-bench Verified
GLM-5.3—
Qwen3.6-27B77.2%
Source
Not directly comparable
SWE Multilingual
GLM-5.3—
Qwen3.6-27B71.3%
Source
Not directly comparable
SWE-bench Pro
GLM-5.3—
Qwen3.6-27B53.5%
Source
Not directly comparable
Terminal-Bench 2.0
GLM-5.3—
Qwen3.6-27B59.3%
Source
Not directly comparable
LiveCodeBench
GLM-5.3—
Qwen3.6-27B83.9%
Source
Not directly comparable

Knowledge

MMLU-Pro
GLM-5.3—
Qwen3.6-27B86.2%
Source
Not directly comparable
MMLU-Redux
GLM-5.3—
Qwen3.6-27B93.5%
Source
Not directly comparable
SuperGPQA
GLM-5.3—
Qwen3.6-27B66%
Source
Not directly comparable
C-Eval
GLM-5.3—
Qwen3.6-27B91.4%
Source
Not directly comparable
GPQA
GLM-5.3—
Qwen3.6-27B87.8%
Source
Not directly comparable
HLE
GLM-5.3—
Qwen3.6-27B24%
Source
Not directly comparable

Math

HMMT Feb 2025
GLM-5.3—
Qwen3.6-27B93.8%
Source
Not directly comparable
HMMT Nov 2025
GLM-5.3—
Qwen3.6-27B90.7%
Source
Not directly comparable
HMMT Feb 2026
GLM-5.3—
Qwen3.6-27B84.3%
Source
Not directly comparable
MMAnswerBench
GLM-5.3—
Qwen3.6-27B80.8%
Source
Not directly comparable
AIME26
GLM-5.3—
Qwen3.6-27B94.1%
Source
Not directly comparable

Multimodal

MMMU
GLM-5.3—
Qwen3.6-27B82.9%
Source
Not directly comparable
MMMU-Pro
GLM-5.3—
Qwen3.6-27B75.8%
Source
Not directly comparable
RealWorldQA
GLM-5.3—
Qwen3.6-27B84.1%
Source
Not directly comparable
DynaMath
GLM-5.3—
Qwen3.6-27B85.6%
Source
Not directly comparable
MStar
GLM-5.3—
Qwen3.6-27B81.4%
Source
Not directly comparable
SimpleVQA
GLM-5.3—
Qwen3.6-27B56.1%
Source
Not directly comparable
CharXiv
GLM-5.3—
Qwen3.6-27B78.4%
Source
Not directly comparable
CC-OCR
GLM-5.3—
Qwen3.6-27B81.2%
Source
Not directly comparable
CountBench
GLM-5.3—
Qwen3.6-27B97.8%
Source
Not directly comparable
RefCOCO (avg)
GLM-5.3—
Qwen3.6-27B92.5%
Source
Not directly comparable
ERQA
GLM-5.3—
Qwen3.6-27B62.5%
Source
Not directly comparable
Video-MME (with subtitle)
GLM-5.3—
Qwen3.6-27B87.7%
Source
Not directly comparable
VideoMMMU
GLM-5.3—
Qwen3.6-27B84.4%
Source
Not directly comparable
MLVU (M-Avg)
GLM-5.3—
Qwen3.6-27B86.6%
Source
Not directly comparable
V*
GLM-5.3—
Qwen3.6-27B94.7%
Source
Not directly comparable

Frequently asked questions

Which is better, GLM-5.3 or Qwen3.6-27B?

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 Qwen3.6-27B?

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 Qwen3.6-27B?

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 Qwen3.6-27B?

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 Qwen3.6-27B?

GLM-5.3 has the larger documented context window: 1M, compared with 262K.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated August 14, 2026

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GLM-5.3 vs Qwen3.6-27B

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

Knowledge

Math

Multimodal

Frequently asked questions

Which is better, GLM-5.3 or Qwen3.6-27B?

Which is better for coding, GLM-5.3 or Qwen3.6-27B?

Which is better for agentic tasks, GLM-5.3 or Qwen3.6-27B?

Which costs less, GLM-5.3 or Qwen3.6-27B?

Which has the larger context window, GLM-5.3 or Qwen3.6-27B?

Related comparisons

Watch GLM-5.3 vs Qwen3.6-27B