Model A

Z.AI

49.41/100

Supported · Public rank #142

90% interval 36.4–62.4

GLM-4.7-Flash vs Qwen3.6-27B

Updated September 4, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

Model B

Qwen3.6-27B

Alibaba

52.73/100

Estimated · Public rank #120

90% interval 47.0–58.5

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 resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

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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
Qwen3.6-27B
Qwen3.6-27B 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
GLM-4.7-Flash is not ranked on the public lane for coding, so no winner is named for coding.
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
GLM-4.7-Flash is not ranked on the public lane for agentic, so no winner is named for agentic.
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-Flash does not fit this workload in one request. GLM-4.7-Flash has no comparable published API token rate. Qwen3.6-27B 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.

38 Qwen3.6-27B only

Shared results: 0
GLM-4.7-Flash only: 0
Qwen3.6-27B only: 38
Like-for-like categories: 0 / 8

Category results, on a stated basis

Each row shows the public-lane category score for both models: the BenchAlign 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.

Agentic

Not comparable

GLM-4.7-Flash: Not ranked
Qwen3.6-27B: 33.9; Supported · #133/151
Basis: BenchAlign lane · 0 vs 6 public rows
Reading: Not comparable

Coding

Not comparable

GLM-4.7-Flash: Not ranked
Qwen3.6-27B: 42.6; Supported · #128/183
Basis: BenchAlign lane · 0 vs 6 public rows
Reading: Not comparable

Reasoning

Not comparable

GLM-4.7-Flash: Not ranked
Qwen3.6-27B: 73.7; Unranked · 2 rankable rows
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Knowledge

Not comparable

GLM-4.7-Flash: Not ranked
Qwen3.6-27B: 49.0; Estimated · #95/181
Basis: BenchAlign lane · 0 vs 6 public rows
Reading: Not comparable

Math

Not comparable

GLM-4.7-Flash: Not ranked
Qwen3.6-27B: 72.8; Unranked · 5 rankable rows
Basis: Provisional lane · 0 vs 2 weighted rows
Reading: Not comparable

Multilingual

Not comparable

GLM-4.7-Flash: Not ranked
Qwen3.6-27B: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Multimodal

Not comparable

GLM-4.7-Flash: Not ranked
Qwen3.6-27B: 51.5; #35/48
Basis: Provisional lane · 0 vs 2 weighted rows
Reading: Not comparable

Instruction following

Not comparable

GLM-4.7-Flash: Not ranked
Qwen3.6-27B: 82.2; #50/120
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Category scores with the server-provided evidence basis for GLM-4.7-Flash and Qwen3.6-27B
Category	GLM-4.7-Flash	Qwen3.6-27B	Basis	Reading
Agentic	Not ranked	33.9Supported · #133/151	Not comparableBenchAlign lane · 0 vs 6 public rows	Not comparable
Coding	Not ranked	42.6Supported · #128/183	Not comparableBenchAlign lane · 0 vs 6 public rows	Not comparable
Reasoning	Not ranked	73.7Unranked · 2 rankable rows	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Knowledge	Not ranked	49.0Estimated · #95/181	Not comparableBenchAlign lane · 0 vs 6 public rows	Not comparable
Math	Not ranked	72.8Unranked · 5 rankable rows	Not comparableProvisional lane · 0 vs 2 weighted rows	Not comparable
Multilingual	Not ranked	Not ranked	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Multimodal	Not ranked	51.5#35/48	Not comparableProvisional lane · 0 vs 2 weighted rows	Not comparable
Instruction following	Not ranked	82.2#50/120	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable

Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign) differ from the provisional-lane categories. Unranked scores sit on the lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.

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-4.7-Flash: Self-hosted; infrastructure cost varies; Fits in one request
Qwen3.6-27B: Self-hosted; infrastructure cost varies; Fits in one request

GLM-4.7-Flash 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-4.7-Flash: Self-hosted; infrastructure cost varies; Fits in one request
Qwen3.6-27B: Self-hosted; infrastructure cost varies; Fits in one request

GLM-4.7-Flash 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-4.7-Flash: Self-hosted; infrastructure cost varies; Does not fit in one request; Cached-input rate unavailable
Qwen3.6-27B: Self-hosted; infrastructure cost varies; Fits in one request; Cached-input rate unavailable

GLM-4.7-Flash does not fit this workload in one request. GLM-4.7-Flash 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-4.7-FlashQwen3.6-27B

Context window

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

GLM-4.7-Flash

200K

Qwen3.6-27B

262K

API model ID

GLM-4.7-Flash

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-4.7-Flash

No comparable hosted API rate

Qwen3.6-27B

No comparable hosted API rate

Documented inputs

GLM-4.7-Flash

Not sourced

Qwen3.6-27B

Not sourced

Documented outputs

GLM-4.7-Flash

Not sourced

Qwen3.6-27B

Not sourced

Provider availability

GLM-4.7-Flash

Not sourced

Qwen3.6-27B

Not sourced

Reasoning profile

GLM-4.7-Flash

Reasoning

Qwen3.6-27B

Reasoning

Weight access

GLM-4.7-Flash

Open Weight

Qwen3.6-27B

Open Weight

License

GLM-4.7-Flash

Open Weight

Qwen3.6-27B

Open Weight

Release date

GLM-4.7-Flash

2025-10-01

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: 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: Qwen3.6-27B has the larger documented window (262K).

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-4.7-Flash

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 evidence38 rows

Agentic

Terminal-Bench 2.0
GLM-4.7-Flash—
Qwen3.6-27B59.3%
Source
Not directly comparable
Claw-Eval
GLM-4.7-Flash—
Qwen3.6-27B72.4%
Source
Not directly comparable
QwenClawBench
GLM-4.7-Flash—
Qwen3.6-27B53.4%
Source
Not directly comparable
QwenWebBench
GLM-4.7-Flash—
Qwen3.6-27B1487
Source
Not directly comparable
AndroidWorld
GLM-4.7-Flash—
Qwen3.6-27B70.3%
Source
Not directly comparable
Gert Labs
GLM-4.7-Flash—
Qwen3.6-27B54.84%
Source
Not directly comparable

Coding

SWE-bench Verified
GLM-4.7-Flash—
Qwen3.6-27B77.2%
Source
Not directly comparable
SWE Multilingual
GLM-4.7-Flash—
Qwen3.6-27B71.3%
Source
Not directly comparable
SWE-bench Pro
GLM-4.7-Flash—
Qwen3.6-27B53.5%
Source
Not directly comparable
Terminal-Bench 2.0
GLM-4.7-Flash—
Qwen3.6-27B59.3%
Source
Not directly comparable
LiveCodeBench
GLM-4.7-Flash—
Qwen3.6-27B83.9%
Source
Not directly comparable
NL2Repo
GLM-4.7-Flash—
Qwen3.6-27B36.2%
Source
Not directly comparable

Knowledge

MMLU-Pro
GLM-4.7-Flash—
Qwen3.6-27B86.2%
Source
Not directly comparable
MMLU-Redux
GLM-4.7-Flash—
Qwen3.6-27B93.5%
Source
Not directly comparable
SuperGPQA
GLM-4.7-Flash—
Qwen3.6-27B66%
Source
Not directly comparable
C-Eval
GLM-4.7-Flash—
Qwen3.6-27B91.4%
Source
Not directly comparable
GPQA
GLM-4.7-Flash—
Qwen3.6-27B87.8%
Source
Not directly comparable
HLE
GLM-4.7-Flash—
Qwen3.6-27B24%
Source
Not directly comparable

Math

HMMT Feb 2025
GLM-4.7-Flash—
Qwen3.6-27B93.8%
Source
Not directly comparable
HMMT Nov 2025
GLM-4.7-Flash—
Qwen3.6-27B90.7%
Source
Not directly comparable
HMMT Feb 2026
GLM-4.7-Flash—
Qwen3.6-27B84.3%
Source
Not directly comparable
MMAnswerBench
GLM-4.7-Flash—
Qwen3.6-27B80.8%
Source
Not directly comparable
AIME26
GLM-4.7-Flash—
Qwen3.6-27B94.1%
Source
Not directly comparable

Multimodal

MMMU
GLM-4.7-Flash—
Qwen3.6-27B82.9%
Source
Not directly comparable
MMMU-Pro
GLM-4.7-Flash—
Qwen3.6-27B75.8%
Source
Not directly comparable
RealWorldQA
GLM-4.7-Flash—
Qwen3.6-27B84.1%
Source
Not directly comparable
DynaMath
GLM-4.7-Flash—
Qwen3.6-27B85.6%
Source
Not directly comparable
MStar
GLM-4.7-Flash—
Qwen3.6-27B81.4%
Source
Not directly comparable
SimpleVQA
GLM-4.7-Flash—
Qwen3.6-27B56.1%
Source
Not directly comparable
CharXiv
GLM-4.7-Flash—
Qwen3.6-27B78.4%
Source
Not directly comparable
CC-OCR
GLM-4.7-Flash—
Qwen3.6-27B81.2%
Source
Not directly comparable
CountBench
GLM-4.7-Flash—
Qwen3.6-27B97.8%
Source
Not directly comparable
RefCOCO (avg)
GLM-4.7-Flash—
Qwen3.6-27B92.5%
Source
Not directly comparable
ERQA
GLM-4.7-Flash—
Qwen3.6-27B62.5%
Source
Not directly comparable
Video-MME (with subtitle)
GLM-4.7-Flash—
Qwen3.6-27B87.7%
Source
Not directly comparable
VideoMMMU
GLM-4.7-Flash—
Qwen3.6-27B84.4%
Source
Not directly comparable
MLVU (M-Avg)
GLM-4.7-Flash—
Qwen3.6-27B86.6%
Source
Not directly comparable
V*
GLM-4.7-Flash—
Qwen3.6-27B94.7%
Source
Not directly comparable

Frequently asked questions

Which is better, GLM-4.7-Flash or Qwen3.6-27B?

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-4.7-Flash or Qwen3.6-27B?

GLM-4.7-Flash is not ranked on the public lane for coding, so no winner is named for coding.

Which is better for agentic tasks, GLM-4.7-Flash or Qwen3.6-27B?

GLM-4.7-Flash is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.

Which costs less, GLM-4.7-Flash 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-4.7-Flash or Qwen3.6-27B?

Qwen3.6-27B has the larger documented context window: 262K, compared with 200K.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated September 4, 2026

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GLM-4.7-Flash 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-4.7-Flash or Qwen3.6-27B?

Which is better for coding, GLM-4.7-Flash or Qwen3.6-27B?

Which is better for agentic tasks, GLM-4.7-Flash or Qwen3.6-27B?

Which costs less, GLM-4.7-Flash or Qwen3.6-27B?

Which has the larger context window, GLM-4.7-Flash or Qwen3.6-27B?

Related comparisons

Watch GLM-4.7-Flash vs Qwen3.6-27B