GLM-5.3-Flash vs Qwen3.6-27B

Updated October 10, 2026. Rank says GLM-5.3-Flash is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.

Decision reading

GLM-5.3-Flash has the higher public point estimate, 57.36 versus 47.95. Their conditional score ranges overlap. These ranges do not establish rank confidence. 3 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

Model A

GLM-5.3-Flash

Z.AI

57.36/100

Estimated · Public rank #55

Conditional range 47.6–67.1

Model B

Qwen3.6-27B

Alibaba

47.95/100

Estimated · Public rank #102

Conditional range 33.6–62.3

Shared results: 3
GLM-5.3-Flash only: 18
Qwen3.6-27B only: 36
Like-for-like categories: 2 / 8

Estimated: GLM-5.3-Flash and Qwen3.6-27B. Conditional ranges do not establish rank confidence.How the comparison works

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.

Coding work
Code generation, repair, and software-engineering tasks
GLM-5.3-Flash
GLM-5.3-Flash has the higher public coding point estimate, 49.3 to 35.8, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
GLM-5.3-Flash
GLM-5.3-Flash has the higher public agentic point estimate, 55.7 to 28, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
Confidence: limited
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

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

Which one for a specific job

Choose a job from the LLM Selector's task catalog to see the category row it rests on, under the same basis rules as the table below. A directional row stays directional; choosing a job never creates a winner.

Pick the job you would use them for

The same task catalog as the LLM Selector. Each job names the evidence surface it rests on; nothing here adds a new score.

49.3GLM-5.3-Flash35.8Qwen3.6-27B

Like-for-like · BenchAlign v5.8

GLM-5.3-Flash has the higher coding point estimate. Conditional score ranges do not establish rank confidence.

Coding scores combine specific tasks and setups. Match the editor, harness, and effort to your workflow.

Open the coding leaderboard Shortlist models for this job

Same basis rules as the category table below

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.

CharXivMultimodal
Normalized gap 11.0
GLM-5.3-Flash:89.4%
Qwen3.6-27B:78.4%
GLM-5.3-Flash source Qwen3.6-27B source

Bars run 0–100 on each benchmark’s normalized display scale

Category results, on a stated basis

Each row shows the public-lane category score for both models: the BenchAlign v5.8 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

Like-for-like

GLM-5.3-Flash: 55.7; Supported · #34/123
Qwen3.6-27B: 28.0; Supported · #92/123
Basis: BenchAlign v5.8 lane · 6 vs 6 public rows
Reading: GLM-5.3-Flash leads

Coding

Like-for-like

GLM-5.3-Flash: 49.3; Supported · #42/146
Qwen3.6-27B: 35.8; Supported · #74/146
Basis: BenchAlign v5.8 lane · 8 vs 7 public rows
Reading: GLM-5.3-Flash leads · intervals overlap

Multimodal

Directional only

GLM-5.3-Flash: 84.0; #14/54
Qwen3.6-27B: 67.3; #38/54
Basis: Provisional lane · 2 vs 2 weighted rows
Reading: Directional only

Knowledge

Directional only

GLM-5.3-Flash: 60.5; Supported · #43/177
Qwen3.6-27B: 46.9; Estimated · #86/177
Basis: BenchAlign v5.8 lane · 2 vs 6 public rows
Reading: Directional only

Reasoning

Not comparable

GLM-5.3-Flash: 81.1; #11/28
Qwen3.6-27B: 79.2; Unranked · 2 rankable rows
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Multilingual

Not comparable

GLM-5.3-Flash: Not ranked
Qwen3.6-27B: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Instruction following

Not comparable

GLM-5.3-Flash: Not ranked
Qwen3.6-27B: 82.4; #52/127
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Math

Not comparable

GLM-5.3-Flash: Not ranked
Qwen3.6-27B: 73.7; Unranked · 5 rankable rows
Basis: Provisional lane · 0 vs 2 weighted rows
Reading: Not comparable

Category scores with the server-provided evidence basis for GLM-5.3-Flash and Qwen3.6-27B
Category	GLM-5.3-Flash	Qwen3.6-27B	Basis	Reading
Agentic	55.7Supported · #34/123	28.0Supported · #92/123	Like-for-likeBenchAlign v5.8 lane · 6 vs 6 public rows	GLM-5.3-Flash leads
Coding	49.3Supported · #42/146	35.8Supported · #74/146	Like-for-likeBenchAlign v5.8 lane · 8 vs 7 public rows	GLM-5.3-Flash leads · intervals overlap
Multimodal	84.0#14/54	67.3#38/54	Directional onlyProvisional lane · 2 vs 2 weighted rows	Directional only
Knowledge	60.5Supported · #43/177	46.9Estimated · #86/177	Directional onlyBenchAlign v5.8 lane · 2 vs 6 public rows	Directional only
Reasoning	81.1#11/28	79.2Unranked · 2 rankable rows	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Multilingual	Not ranked	Not ranked	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Instruction following	Not ranked	82.4#52/127	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Math	Not ranked	73.7Unranked · 5 rankable rows	Not comparableProvisional lane · 0 vs 2 weighted rows	Not comparable

Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign v5.8) differ from the provisional-lane categories. Unranked scores sit on the provisional lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.

Supported evidence per lane · bars run 0–100Methodology

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

GLM-5.3-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-5.3-Flash: Self-hosted; infrastructure cost varies; Fits in one request
Qwen3.6-27B: Self-hosted; infrastructure cost varies; Fits in one request

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

Cached input falls back to the list input rate only where a cached rate is unpublished

Specification differences

Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.

SpecificationGLM-5.3-FlashQwen3.6-27B

Context window

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

GLM-5.3-Flash

GLM-5.3-Flash model card

Qwen3.6-27B

262K

API model ID

GLM-5.3-Flash

glm-5.3-flash

GLM-5.3-Flash model card

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-Flash

No comparable hosted API rate

GLM-5.3-Flash model card

Qwen3.6-27B

No comparable hosted API rate

Documented inputs

GLM-5.3-Flash

Not sourced

Qwen3.6-27B

Not sourced

Documented outputs

GLM-5.3-Flash

Not sourced

Qwen3.6-27B

Not sourced

Provider availability

GLM-5.3-Flash

Not sourced

Qwen3.6-27B

Not sourced

Reasoning profile

GLM-5.3-Flash

Reasoning

Qwen3.6-27B

Reasoning

Weight access

GLM-5.3-Flash

Open Weight

Qwen3.6-27B

Open Weight

License

GLM-5.3-Flash

Open Weight

Qwen3.6-27B

Open Weight

Release date

GLM-5.3-Flash

2026-08-26

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: GLM-5.3-Flash has the higher public point estimate, 57.36 versus 47.95. Their conditional score ranges overlap. These ranges do not establish rank confidence.
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.

Questions

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

GLM-5.3-Flash has the higher public point estimate, 57.36 versus 47.95. Their conditional score ranges overlap. These ranges do not establish rank confidence. The page therefore keeps the decision tied to the specific documented workload.

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

GLM-5.3-Flash has the higher public coding point estimate, 49.3 to 35.8, with Supported evidence for both models. The conditional ranges do not establish rank confidence.

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

GLM-5.3-Flash has the higher public agentic tasks point estimate, 55.7 to 28, with Supported evidence for both models. The conditional ranges do not establish rank confidence.

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

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

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.

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 evidence57 rows

Agentic

Terminal-Bench 2.1
GLM-5.3-Flash84.3%
Source
Qwen3.6-27B—
Not directly comparable
Toolathlon-Verified
GLM-5.3-Flash78.4%
Source
Qwen3.6-27B—
Not directly comparable
AutomationBench
GLM-5.3-Flash48.8%
Source
Qwen3.6-27B—
Not directly comparable
Agents' Last Exam
GLM-5.3-Flash26.3%
Source
Qwen3.6-27B—
Not directly comparable
HLE w/ tools
GLM-5.3-Flash55.3%
Source
Qwen3.6-27B—
Not directly comparable
Terminal-Bench 2.1 (Vals)
GLM-5.3-Flash62.9%
Source
Qwen3.6-27B—
Not directly comparable
Terminal-Bench 2.0
GLM-5.3-Flash—
Qwen3.6-27B59.3%
Source
Not directly comparable
Claw-Eval
GLM-5.3-Flash—
Qwen3.6-27B72.4%
Source
Not directly comparable
QwenClawBench
GLM-5.3-Flash—
Qwen3.6-27B53.4%
Source
Not directly comparable
QwenWebBench
GLM-5.3-Flash—
Qwen3.6-27B1487
Source
Not directly comparable
AndroidWorld
GLM-5.3-Flash—
Qwen3.6-27B70.3%
Source
Not directly comparable
Gert Labs
GLM-5.3-Flash—
Qwen3.6-27B54.84%
Source
Not directly comparable

Coding

Terminal-Bench 2.1
GLM-5.3-Flash84.3%
Source
Qwen3.6-27B—
Not directly comparable
DeepSWE
GLM-5.3-Flash63.4%
Source
Qwen3.6-27B—
Not directly comparable
NL2Repo
GLM-5.3-Flash56.3%
Source
Qwen3.6-27B36.2%
Source
GLM-5.3-Flash leads this result
LiveCodeBench (Vals)
GLM-5.3-Flash80.5%
Source
Qwen3.6-27B—
Not directly comparable
SWE-bench (Vals)
GLM-5.3-Flash92.0%
Source
Qwen3.6-27B70.0%
Source
GLM-5.3-Flash leads this result
OpenHarmony Bench
GLM-5.3-Flash57.3%
Source
Qwen3.6-27B—
Not directly comparable
FrontierSWE v2
GLM-5.3-Flash18.1%
Source
Qwen3.6-27B—
Not directly comparable
Bug Hunt Bench
GLM-5.3-Flash17.7 fixes
Source
Qwen3.6-27B—
Not directly comparable
SWE-bench Verified
GLM-5.3-Flash—
Qwen3.6-27B77.2%
Source
Not directly comparable
SWE Multilingual
GLM-5.3-Flash—
Qwen3.6-27B71.3%
Source
Not directly comparable
SWE-bench Pro
GLM-5.3-Flash—
Qwen3.6-27B53.5%
Source
Not directly comparable
Terminal-Bench 2.0
GLM-5.3-Flash—
Qwen3.6-27B59.3%
Source
Not directly comparable
LiveCodeBench v6
GLM-5.3-Flash—
Qwen3.6-27B83.9%
Source
Not directly comparable

Multimodal

OfficeQA Pro
GLM-5.3-Flash62.4%
Source
Qwen3.6-27B—
Not directly comparable
CharXiv
GLM-5.3-Flash89.4%
Source
Qwen3.6-27B78.4%
Source
GLM-5.3-Flash leads this result
Chartography (tools)
GLM-5.3-Flash78.0%
Source
Qwen3.6-27B—
Not directly comparable
BabyVision
GLM-5.3-Flash53.4%
Source
Qwen3.6-27B—
Not directly comparable
MMVU
GLM-5.3-Flash80.5%
Source
Qwen3.6-27B—
Not directly comparable
MMMU
GLM-5.3-Flash—
Qwen3.6-27B82.9%
Source
Not directly comparable
MMMU-Pro
GLM-5.3-Flash—
Qwen3.6-27B75.8%
Source
Not directly comparable
RealWorldQA
GLM-5.3-Flash—
Qwen3.6-27B84.1%
Source
Not directly comparable
DynaMath
GLM-5.3-Flash—
Qwen3.6-27B85.6%
Source
Not directly comparable
MStar
GLM-5.3-Flash—
Qwen3.6-27B81.4%
Source
Not directly comparable
SimpleVQA
GLM-5.3-Flash—
Qwen3.6-27B56.1%
Source
Not directly comparable
CC-OCR
GLM-5.3-Flash—
Qwen3.6-27B81.2%
Source
Not directly comparable
CountBench
GLM-5.3-Flash—
Qwen3.6-27B97.8%
Source
Not directly comparable
RefCOCO (avg)
GLM-5.3-Flash—
Qwen3.6-27B92.5%
Source
Not directly comparable
ERQA
GLM-5.3-Flash—
Qwen3.6-27B62.5%
Source
Not directly comparable
Video-MME (with subtitle)
GLM-5.3-Flash—
Qwen3.6-27B87.7%
Source
Not directly comparable
VideoMMMU
GLM-5.3-Flash—
Qwen3.6-27B84.4%
Source
Not directly comparable
MLVU (M-Avg)
GLM-5.3-Flash—
Qwen3.6-27B86.6%
Source
Not directly comparable
V*
GLM-5.3-Flash—
Qwen3.6-27B94.7%
Source
Not directly comparable

Knowledge

GPQA Diamond (Vals)
GLM-5.3-Flash86.4%
Source
Qwen3.6-27B—
Not directly comparable
MMLU-Pro (Vals)
GLM-5.3-Flash86.1%
Source
Qwen3.6-27B—
Not directly comparable
MMLU-Pro
GLM-5.3-Flash—
Qwen3.6-27B86.2%
Source
Not directly comparable
MMLU-Redux
GLM-5.3-Flash—
Qwen3.6-27B93.5%
Source
Not directly comparable
SuperGPQA
GLM-5.3-Flash—
Qwen3.6-27B66%
Source
Not directly comparable
C-Eval
GLM-5.3-Flash—
Qwen3.6-27B91.4%
Source
Not directly comparable
GPQA
GLM-5.3-Flash—
Qwen3.6-27B87.8%
Source
Not directly comparable
HLE
GLM-5.3-Flash—
Qwen3.6-27B24%
Source
Not directly comparable

Math

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

57 public results · 3 shared

Related comparisons

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Last updated October 10, 2026

GLM-5.3-Flash vs Qwen3.6-27B

Decision reading

Which one for your work

Coding work

Agentic work

Long documents

Chat turn cost

Cache-heavy agent loop cost

Repository review cost

Which one for a specific job

What is actually comparable

Shape of the matched evidence

Category results, on a stated basis

Agentic

Coding

Multimodal

Knowledge

Reasoning

Multilingual

Instruction following

Math

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

If you already use one of these models

Questions

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

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

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

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

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

Self-host vs API cost

Benchmark evidence

Agentic

Coding

Multimodal

Knowledge

Math

Related comparisons

Watch GLM-5.3-Flash vs Qwen3.6-27B