Model A

Z.AI

64.41/100

Supported · Public rank #47

90% interval 55.6–73.3

GLM-5.1 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

GLM-5.1 has the higher public score estimate, 64.41 versus 52.73, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.

10 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

Share or export

Share on X LinkedInSocial card CSV JSON

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-5.1
GLM-5.1 leads on the public coding lane, 56.8 to 42.6, with Supported evidence for both models, although the 90% intervals overlap.
Confidence: limited
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

Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
GLM-5.1 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-5.1 does not fit this workload in one request. GLM-5.1 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

What is actually comparable

Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.

16 GLM-5.1 only

10 Shared

28 Qwen3.6-27B only

Shared results: 10
GLM-5.1 only: 16
Qwen3.6-27B only: 28
Like-for-like categories: 1 / 8

3 categories rest on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.

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.

Coding

Like-for-like

GLM-5.1: 56.8; Supported · #39/183
Qwen3.6-27B: 42.6; Supported · #128/183
Basis: BenchAlign lane · 7 vs 6 public rows
Reading: GLM-5.1 leads · intervals overlap

Agentic

Directional only

GLM-5.1: 50.1; Estimated · #63/151
Qwen3.6-27B: 33.9; Supported · #133/151
Basis: BenchAlign lane · 9 vs 6 public rows
Reading: Directional only

Knowledge

Directional only

GLM-5.1: 55.4; Supported · #58/181
Qwen3.6-27B: 49.0; Estimated · #95/181
Basis: BenchAlign lane · 4 vs 6 public rows
Reading: Directional only

Instruction following

Directional only

GLM-5.1: 93.5; #5/120
Qwen3.6-27B: 82.2; #50/120
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Directional only

Reasoning

Not comparable

GLM-5.1: 69.9; Unranked · 2 rankable rows
Qwen3.6-27B: 73.7; Unranked · 2 rankable rows
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Math

Not comparable

GLM-5.1: 64.1; #3/7
Qwen3.6-27B: 72.8; Unranked · 5 rankable rows
Basis: Provisional lane · 4 vs 2 weighted rows
Reading: Not comparable

Multilingual

Not comparable

GLM-5.1: Not ranked
Qwen3.6-27B: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Multimodal

Not comparable

GLM-5.1: Not ranked
Qwen3.6-27B: 51.5; #35/48
Basis: Provisional lane · 0 vs 2 weighted rows
Reading: Not comparable

Category scores with the server-provided evidence basis for GLM-5.1 and Qwen3.6-27B
Category	GLM-5.1	Qwen3.6-27B	Basis	Reading
Coding	56.8Supported · #39/183	42.6Supported · #128/183	Like-for-likeBenchAlign lane · 7 vs 6 public rows	GLM-5.1 leads · intervals overlap
Agentic	50.1Estimated · #63/151	33.9Supported · #133/151	Directional onlyBenchAlign lane · 9 vs 6 public rows	Directional only
Knowledge	55.4Supported · #58/181	49.0Estimated · #95/181	Directional onlyBenchAlign lane · 4 vs 6 public rows	Directional only
Instruction following	93.5#5/120	82.2#50/120	Directional onlyProvisional lane · 0 vs 0 weighted rows	Directional only
Reasoning	69.9Unranked · 2 rankable rows	73.7Unranked · 2 rankable rows	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Math	64.1#3/7	72.8Unranked · 5 rankable rows	Not comparableProvisional lane · 4 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

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.

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.

HLE
Knowledge
GLM-5.1: 52.3%Qwen3.6-27B: 24%Normalized gap 28.3GLM-5.1 source Qwen3.6-27B source
SWE-bench Pro
Coding
GLM-5.1: 58.4%Qwen3.6-27B: 53.5%Normalized gap 4.9GLM-5.1 source Qwen3.6-27B source
Terminal-Bench 2.0
Agentic
GLM-5.1: 63.5%Qwen3.6-27B: 59.3%Normalized gap 4.2GLM-5.1 source Qwen3.6-27B source
HMMT Feb 2026
Math
GLM-5.1: 82.6%Qwen3.6-27B: 84.3%Normalized gap 1.7GLM-5.1 source Qwen3.6-27B source
AIME26
Math
GLM-5.1: 95.3%Qwen3.6-27B: 94.1%Normalized gap 1.2GLM-5.1 source Qwen3.6-27B source

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-5.1: $0.0036; 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-5.1: $0.0832; 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-5.1: $0.352; 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-5.1 does not fit this workload in one request. GLM-5.1 has no published cached-input rate, so cached tokens use its listed input 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-5.1Qwen3.6-27B

Context window

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

GLM-5.1

203K

Qwen3.6-27B

262K

API model ID

GLM-5.1

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-5.1

Not published

Qwen3.6-27B

No comparable hosted API rate

Documented inputs

GLM-5.1

Not sourced

Qwen3.6-27B

Not sourced

Documented outputs

GLM-5.1

Not sourced

Qwen3.6-27B

Not sourced

Provider availability

GLM-5.1

Not sourced

Qwen3.6-27B

Not sourced

Reasoning profile

GLM-5.1

Reasoning

Qwen3.6-27B

Reasoning

Weight access

GLM-5.1

Open Weight

Qwen3.6-27B

Open Weight

License

GLM-5.1

Open Weight

Qwen3.6-27B

Open Weight

Release date

GLM-5.1

2026-04-07

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-5.1 has the higher public score estimate, 64.41 versus 52.73, but the 90% score intervals overlap.
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-5.1

API / mo$4,350

Self-host / mo$18,221

Break-even264M/day

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 evidence54 rows

Agentic

Terminal-Bench 2.0
GLM-5.163.5%
Source
Qwen3.6-27B59.3%
Source
GLM-5.1 leads this result
BrowseComp
GLM-5.168%
Source
Qwen3.6-27B—
Not directly comparable
τ³-bench results
GLM-5.170.6%
Source
Qwen3.6-27B—
Not directly comparable
MCP Atlas
GLM-5.171.8%
Source
Qwen3.6-27B—
Not directly comparable
CyberGym
GLM-5.168.7%
Source
Qwen3.6-27B—
Not directly comparable
Claw-Eval
GLM-5.162.3%
Source
Qwen3.6-27B72.4%
Source
Qwen3.6-27B leads this result
Gert Labs
Shared source
GLM-5.160.11%
Qwen3.6-27B54.84%
GLM-5.1 leads this result
ResearchClawBench
GLM-5.118.2%
Source
Qwen3.6-27B—
Not directly comparable
Terminal-Bench 2.1 (Vals)
GLM-5.156.9%
Source
Qwen3.6-27B—
Not directly comparable
QwenClawBench
GLM-5.1—
Qwen3.6-27B53.4%
Source
Not directly comparable
QwenWebBench
GLM-5.1—
Qwen3.6-27B1487
Source
Not directly comparable
AndroidWorld
GLM-5.1—
Qwen3.6-27B70.3%
Source
Not directly comparable

Coding

SWE-bench Pro
GLM-5.158.4%
Source
Qwen3.6-27B53.5%
Source
GLM-5.1 leads this result
NL2Repo
GLM-5.142.7%
Source
Qwen3.6-27B36.2%
Source
GLM-5.1 leads this result
SWE-Rebench
GLM-5.162.7%
Source
Qwen3.6-27B—
Not directly comparable
Vibe Code Bench
GLM-5.131.46%
Source
Qwen3.6-27B—
Not directly comparable
OpenHarmony Bench
GLM-5.152.3%
Source
Qwen3.6-27B—
Not directly comparable
LiveCodeBench (Vals)
GLM-5.181.4%
Source
Qwen3.6-27B—
Not directly comparable
SWE-bench (Vals)
GLM-5.176.4%
Source
Qwen3.6-27B—
Not directly comparable
SWE-bench Verified
GLM-5.1—
Qwen3.6-27B77.2%
Source
Not directly comparable
SWE Multilingual
GLM-5.1—
Qwen3.6-27B71.3%
Source
Not directly comparable
Terminal-Bench 2.0
GLM-5.1—
Qwen3.6-27B59.3%
Source
Not directly comparable
LiveCodeBench
GLM-5.1—
Qwen3.6-27B83.9%
Source
Not directly comparable

Knowledge

GPQA-D
GLM-5.186.2%
Source
Qwen3.6-27B—
Not directly comparable
HLE
GLM-5.152.3%
Source
Qwen3.6-27B24%
Source
GLM-5.1 leads this result
GPQA Diamond (Vals)
GLM-5.184.5%
Source
Qwen3.6-27B—
Not directly comparable
MMLU-Pro (Vals)
GLM-5.186.9%
Source
Qwen3.6-27B—
Not directly comparable
MMLU-Pro
GLM-5.1—
Qwen3.6-27B86.2%
Source
Not directly comparable
MMLU-Redux
GLM-5.1—
Qwen3.6-27B93.5%
Source
Not directly comparable
SuperGPQA
GLM-5.1—
Qwen3.6-27B66%
Source
Not directly comparable
C-Eval
GLM-5.1—
Qwen3.6-27B91.4%
Source
Not directly comparable
GPQA
GLM-5.1—
Qwen3.6-27B87.8%
Source
Not directly comparable

Math

AIME26
GLM-5.195.3%
Source
Qwen3.6-27B94.1%
Source
GLM-5.1 leads this result
HMMT Nov 2025
GLM-5.194.0%
Source
Qwen3.6-27B90.7%
Source
GLM-5.1 leads this result
HMMT Feb 2026
GLM-5.182.6%
Source
Qwen3.6-27B84.3%
Source
Qwen3.6-27B leads this result
MMAnswerBench
GLM-5.183.8%
Source
Qwen3.6-27B80.8%
Source
GLM-5.1 leads this result
FrontierMath v2 (Tiers 1-3)
GLM-5.133.448%
Source
Qwen3.6-27B—
Not directly comparable
FrontierMath v2 (Tier 4)
GLM-5.112.500%
Source
Qwen3.6-27B—
Not directly comparable
HMMT Feb 2025
GLM-5.1—
Qwen3.6-27B93.8%
Source
Not directly comparable

Multimodal

MMMU
GLM-5.1—
Qwen3.6-27B82.9%
Source
Not directly comparable
MMMU-Pro
GLM-5.1—
Qwen3.6-27B75.8%
Source
Not directly comparable
RealWorldQA
GLM-5.1—
Qwen3.6-27B84.1%
Source
Not directly comparable
DynaMath
GLM-5.1—
Qwen3.6-27B85.6%
Source
Not directly comparable
MStar
GLM-5.1—
Qwen3.6-27B81.4%
Source
Not directly comparable
SimpleVQA
GLM-5.1—
Qwen3.6-27B56.1%
Source
Not directly comparable
CharXiv
GLM-5.1—
Qwen3.6-27B78.4%
Source
Not directly comparable
CC-OCR
GLM-5.1—
Qwen3.6-27B81.2%
Source
Not directly comparable
CountBench
GLM-5.1—
Qwen3.6-27B97.8%
Source
Not directly comparable
RefCOCO (avg)
GLM-5.1—
Qwen3.6-27B92.5%
Source
Not directly comparable
ERQA
GLM-5.1—
Qwen3.6-27B62.5%
Source
Not directly comparable
Video-MME (with subtitle)
GLM-5.1—
Qwen3.6-27B87.7%
Source
Not directly comparable
VideoMMMU
GLM-5.1—
Qwen3.6-27B84.4%
Source
Not directly comparable
MLVU (M-Avg)
GLM-5.1—
Qwen3.6-27B86.6%
Source
Not directly comparable
V*
GLM-5.1—
Qwen3.6-27B94.7%
Source
Not directly comparable

Frequently asked questions

Which is better, GLM-5.1 or Qwen3.6-27B?

GLM-5.1 has the higher public score estimate, 64.41 versus 52.73, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.

Which is better for coding, GLM-5.1 or Qwen3.6-27B?

GLM-5.1 leads the public coding lane, 56.8 to 42.6, with Supported evidence for both models, although the 90% intervals overlap.

Which is better for agentic tasks, GLM-5.1 or Qwen3.6-27B?

GLM-5.1 scores higher for agentic tasks on the public lane, 50.1 to 33.9. GLM-5.1 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-5.1 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-5.1 or Qwen3.6-27B?

Qwen3.6-27B has the larger documented context window: 262K, compared with 203K.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated September 4, 2026

Watch GLM-5.1 vs Qwen3.6-27B

One weekly email when material rank, price, or benchmark evidence changes make this matchup worth revisiting.

Read a sample issue

Join 2,000+ readers.

GLM-5.1 vs Qwen3.6-27B

Which one for your work

Coding work

Long documents

Agentic work

Chat turn cost

Cache-heavy agent loop cost

Repository review cost

What is actually comparable

Category results, on a stated basis

Coding

Agentic

Knowledge

Instruction following

Reasoning

Math

Multilingual

Multimodal

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-5.1 or Qwen3.6-27B?

Which is better for coding, GLM-5.1 or Qwen3.6-27B?

Which is better for agentic tasks, GLM-5.1 or Qwen3.6-27B?

Which costs less, GLM-5.1 or Qwen3.6-27B?

Which has the larger context window, GLM-5.1 or Qwen3.6-27B?

Related comparisons

Watch GLM-5.1 vs Qwen3.6-27B