GLM-5 vs Qwen3.6-27B

Updated October 19, 2026. Rank says GLM-5 is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.

Decision reading

GLM-5 has the higher public point estimate, 54.85 versus 48.57. Their conditional score ranges overlap. These ranges do not establish rank confidence. 18 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

Z.AI

54.85/100

Supported · Public rank #70

90% interval 44.2–65.5

Model B

Qwen3.6-27B

Alibaba

48.57/100

Estimated · Public rank #104

Conditional range 34.2–62.9

Shared results: 18
GLM-5 only: 20
Qwen3.6-27B only: 22
Like-for-like categories: 0 / 8

Supported: GLM-5 · Estimated: 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.

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-5 is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like.
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
GLM-5 is scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.
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-5 does not fit this workload in one request. GLM-5 has no published cached-input rate, so cached tokens use its listed input rate. Qwen3.6-27B has no comparable published API token rate.
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

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.

38.1GLM-534.8Qwen3.6-27B

Directional only · BenchAlign v5.8

GLM-5 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.

HLEKnowledge
Normalized gap 6.5
GLM-5:30.5%
Qwen3.6-27B:24%
GLM-5 source Qwen3.6-27B source
Terminal-Bench 2.0Agentic
Normalized gap 3.1
GLM-5:56.2%
Qwen3.6-27B:59.3%
GLM-5 source Qwen3.6-27B source
HMMT Feb 2026Math
Normalized gap 2.1
GLM-5:86.4%
Qwen3.6-27B:84.3%
GLM-5 source Qwen3.6-27B source
SWE MultilingualCoding
Normalized gap 2.0
GLM-5:73.3%
Qwen3.6-27B:71.3%
GLM-5 source Qwen3.6-27B source
GPQAKnowledge
Normalized gap 1.8
GLM-5:86%
Qwen3.6-27B:87.8%
GLM-5 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

Directional only

GLM-5: 41.7; Estimated · #65/130
Qwen3.6-27B: 29.1; Supported · #97/130
Basis: BenchAlign v5.8 lane · 13 vs 6 public rows
Reading: Directional only

Coding

Directional only

GLM-5: 38.1; Estimated · #71/151
Qwen3.6-27B: 34.8; Supported · #80/151
Basis: BenchAlign v5.8 lane · 8 vs 7 public rows
Reading: Directional only

Knowledge

Directional only

GLM-5: 50.4; Estimated · #82/186
Qwen3.6-27B: 46.8; Estimated · #92/186
Basis: BenchAlign v5.8 lane · 6 vs 6 public rows
Reading: Directional only

Instruction following

Directional only

GLM-5: 88.6; #35/148
Qwen3.6-27B: 82.4; #55/148
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Directional only

Reasoning

Not comparable

GLM-5: 70.1; Unranked · 4 rankable rows
Qwen3.6-27B: 78.1; Unranked · 2 rankable rows
Basis: Provisional lane · 1 vs 0 weighted rows
Reading: Not comparable

Multimodal

Not comparable

GLM-5: Not ranked
Qwen3.6-27B: 67.3; #39/55
Basis: Provisional lane · 0 vs 2 weighted rows
Reading: Not comparable

Multilingual

Not comparable

GLM-5: 89.5; #6/17
Qwen3.6-27B: Not ranked
Basis: Provisional lane · 1 vs 0 weighted rows
Reading: Not comparable

Math

Not comparable

GLM-5: 83.0; Unranked · 6 rankable rows
Qwen3.6-27B: 65.9; #8/12
Basis: Provisional lane · 2 vs 3 weighted rows
Reading: Not comparable

Category scores with the server-provided evidence basis for GLM-5 and Qwen3.6-27B
Category	GLM-5	Qwen3.6-27B	Basis	Reading
Agentic	41.7Estimated · #65/130	29.1Supported · #97/130	Directional onlyBenchAlign v5.8 lane · 13 vs 6 public rows	Directional only
Coding	38.1Estimated · #71/151	34.8Supported · #80/151	Directional onlyBenchAlign v5.8 lane · 8 vs 7 public rows	Directional only
Knowledge	50.4Estimated · #82/186	46.8Estimated · #92/186	Directional onlyBenchAlign v5.8 lane · 6 vs 6 public rows	Directional only
Instruction following	88.6#35/148	82.4#55/148	Directional onlyProvisional lane · 0 vs 0 weighted rows	Directional only
Reasoning	70.1Unranked · 4 rankable rows	78.1Unranked · 2 rankable rows	Not comparableProvisional lane · 1 vs 0 weighted rows	Not comparable
Multimodal	Not ranked	67.3#39/55	Not comparableProvisional lane · 0 vs 2 weighted rows	Not comparable
Multilingual	89.5#6/17	Not ranked	Not comparableProvisional lane · 1 vs 0 weighted rows	Not comparable
Math	83.0Unranked · 6 rankable rows	65.9#8/12	Not comparableProvisional lane · 2 vs 3 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: $0.0026; Fits in one request
Qwen3.6-27B: Self-hosted; infrastructure cost varies; Fits in one request

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

Repository review

50K fresh input + 3K output tokens

GLM-5: $0.0596; Fits in one request
Qwen3.6-27B: Self-hosted; infrastructure cost varies; Fits in one request

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

Cache-heavy agent loop

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

GLM-5: $0.252; Does not fit in one request; Cached input priced at the published list-input rate
Qwen3.6-27B: Self-hosted; infrastructure cost varies; Fits in one request; Cached-input rate unavailable

GLM-5 does not fit this workload in one request. GLM-5 has no published cached-input rate, so cached tokens use its listed input 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-5Qwen3.6-27B

Context window

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

GLM-5

200K

Qwen3.6-27B

262K

API model ID

GLM-5

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

Not published

Qwen3.6-27B

No comparable hosted API rate

Documented inputs

GLM-5

Not sourced

Qwen3.6-27B

Not sourced

Documented outputs

GLM-5

Not sourced

Qwen3.6-27B

Not sourced

Provider availability

GLM-5

Not sourced

Qwen3.6-27B

Not sourced

Reasoning profile

GLM-5

Non-Reasoning

Qwen3.6-27B

Reasoning

Weight access

GLM-5

Open Weight

Qwen3.6-27B

Open Weight

License

GLM-5

Open Weight

Qwen3.6-27B

Open Weight

Release date

GLM-5

2026-02-12

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 has the higher public point estimate, 54.85 versus 48.57. 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: Qwen3.6-27B has the larger documented window (262K).

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

Questions

Which is better, GLM-5 or Qwen3.6-27B?

GLM-5 has the higher public point estimate, 54.85 versus 48.57. 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 or Qwen3.6-27B?

GLM-5 scores higher for coding on the public lane, 38.1 to 34.8. GLM-5 is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like. BenchLM does not name a winner for coding from a directional reading; read the shared benchmark rows directly and test the models on the same task set.

Which is better for agentic tasks, GLM-5 or Qwen3.6-27B?

GLM-5 scores higher for agentic tasks on the public lane, 41.7 to 29.1. GLM-5 is scored on Estimated evidence for agentic tasks, so the reading is directional rather than like-for-like. BenchLM does not name a winner for agentic tasks from a directional reading; read the shared benchmark rows directly and test the models on the same task set.

Which costs less, GLM-5 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 or Qwen3.6-27B?

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

Self-host vs API cost

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

GLM-5

API / mo$3,150

Self-host / moNot listed

Break-even—

Proprietary model — self-hosting not applicable.

Qwen3.6-27B

API / mo$0

Self-host / mo$298

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 evidence60 rows

Agentic

Terminal-Bench 2.0
GLM-556.2%
Source
Qwen3.6-27B59.3%
Source
Qwen3.6-27B leads this result
BrowseComp
GLM-562%
Source
Qwen3.6-27B—
Not directly comparable
HLE w/ tools
GLM-550.4%
Source
Qwen3.6-27B—
Not directly comparable
Claw-Eval
GLM-557.7%
Source
Qwen3.6-27B72.4%
Source
Qwen3.6-27B leads this result
QwenClawBench
GLM-554.1%
Source
Qwen3.6-27B53.4%
Source
GLM-5 leads this result
τ³-bench results
GLM-565.6%
Source
Qwen3.6-27B—
Not directly comparable
DeepPlanning
GLM-514.6%
Source
Qwen3.6-27B—
Not directly comparable
Toolathlon
GLM-538%
Source
Qwen3.6-27B—
Not directly comparable
MCP Atlas
GLM-531.1%
Source
Qwen3.6-27B—
Not directly comparable
MCP-Tasks
GLM-560.8%
Source
Qwen3.6-27B—
Not directly comparable
WideResearch
GLM-569.8%
Source
Qwen3.6-27B—
Not directly comparable
CyberGym
GLM-543.2%
Source
Qwen3.6-27B—
Not directly comparable
Gert Labs
Shared source
GLM-550.99%
Qwen3.6-27B54.84%
Qwen3.6-27B leads this result
QwenWebBench
GLM-5—
Qwen3.6-27B1487
Source
Not directly comparable
AndroidWorld
GLM-5—
Qwen3.6-27B70.3%
Source
Not directly comparable

Coding

SWE-bench Verified
GLM-577.8%
Source
Qwen3.6-27B77.2%
Source
GLM-5 leads this result
SWE-bench Verified*
GLM-572.8%
Source
Qwen3.6-27B—
Not directly comparable
SWE-bench Pro
GLM-555.1%
Source
Qwen3.6-27B53.5%
Source
GLM-5 leads this result
SWE Multilingual
GLM-573.3%
Source
Qwen3.6-27B71.3%
Source
GLM-5 leads this result
NL2Repo
GLM-535.9%
Source
Qwen3.6-27B36.2%
Source
Qwen3.6-27B leads this result
SWE-Rebench
GLM-562.8%
Source
Qwen3.6-27B—
Not directly comparable
React Native Evals
GLM-574.8%
Source
Qwen3.6-27B—
Not directly comparable
Terminal-Bench 2.0
GLM-556.2%
Source
Qwen3.6-27B59.3%
Source
Qwen3.6-27B leads this result
LiveCodeBench v6
GLM-5—
Qwen3.6-27B83.9%
Source
Not directly comparable
SWE-bench (Vals)
GLM-5—
Qwen3.6-27B70.0%
Source
Not directly comparable

Reasoning

LongBench v2
GLM-560.8%
Source
Qwen3.6-27B—
Not directly comparable
AI-Needle
GLM-563.3%
Source
Qwen3.6-27B—
Not directly comparable

Multimodal

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

Knowledge

GPQA
GLM-586%
Source
Qwen3.6-27B87.8%
Source
Qwen3.6-27B leads this result
GPQA-D
GLM-586.0%
Source
Qwen3.6-27B—
Not directly comparable
SuperGPQA
GLM-566.8%
Source
Qwen3.6-27B66%
Source
GLM-5 leads this result
MMLU-Pro
GLM-585.7%
Source
Qwen3.6-27B86.2%
Source
Qwen3.6-27B leads this result
MMLU-Pro (Arcee)
GLM-585.8%
Source
Qwen3.6-27B—
Not directly comparable
HLE
GLM-530.5%
Source
Qwen3.6-27B24%
Source
GLM-5 leads this result
MMLU-Redux
GLM-5—
Qwen3.6-27B93.5%
Source
Not directly comparable
C-Eval
GLM-5—
Qwen3.6-27B91.4%
Source
Not directly comparable

Multilingual

MMLU-ProX
GLM-583.1%
Source
Qwen3.6-27B—
Not directly comparable
NOVA-63
GLM-555.1%
Source
Qwen3.6-27B—
Not directly comparable

Instruction following

IFEval
GLM-592.6%
Source
Qwen3.6-27B—
Not directly comparable

Math

AIME26
GLM-595.8%
Source
Qwen3.6-27B94.1%
Source
GLM-5 leads this result
AIME25 (Arcee)
GLM-593.3%
Source
Qwen3.6-27B—
Not directly comparable
HMMT Feb 2025
GLM-597.5%
Source
Qwen3.6-27B93.8%
Source
GLM-5 leads this result
HMMT Nov 2025
GLM-596.9%
Source
Qwen3.6-27B90.7%
Source
GLM-5 leads this result
HMMT Feb 2026
GLM-586.4%
Source
Qwen3.6-27B84.3%
Source
GLM-5 leads this result
MMAnswerBench
GLM-582.5%
Source
Qwen3.6-27B80.8%
Source
GLM-5 leads this result
FrontierMath v2 (Tiers 1-3)
GLM-5—
Qwen3.6-27B35.088%
Source
Not directly comparable

60 public results · 18 shared

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Last updated October 19, 2026

GLM-5 vs Qwen3.6-27B

Decision reading

Which one for your work

Long documents

Coding work

Agentic work

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

Knowledge

Instruction following

Reasoning

Multimodal

Multilingual

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 or Qwen3.6-27B?

Which is better for coding, GLM-5 or Qwen3.6-27B?

Which is better for agentic tasks, GLM-5 or Qwen3.6-27B?

Which costs less, GLM-5 or Qwen3.6-27B?

Which has the larger context window, GLM-5 or Qwen3.6-27B?

Self-host vs API cost

Benchmark evidence

Agentic

Coding

Reasoning

Multimodal

Knowledge

Multilingual

Instruction following

Math

Related comparisons

Watch GLM-5 vs Qwen3.6-27B