Model A

Z.AI

66.06/100

Supported · Public rank #35

90% interval 57.1–75.0

GLM-5.3-Flash vs Qwen3.7 Plus

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

Model B

Qwen3.7 Plus

Alibaba

61.64/100

Supported · Public rank #54

90% interval 50.8–72.5

Decision reading

GLM-5.3-Flash has the higher public score estimate, 66.06 versus 61.64, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.

3 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.

Agentic work
Tool use, computer use, and multi-step task completion
GLM-5.3-Flash
GLM-5.3-Flash leads on the public agentic lane, 60.1 to 37.3, with Supported evidence for both models and non-overlapping 90% intervals.
Confidence: stronger

Show secondary and unsupported calls

Coding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
Qwen3.7 Plus is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like.
Confidence: limited
Long documents
Prompts that approach the documented context limit
No clear pick
The documented context windows are equal.
Confidence: documented
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.

16 GLM-5.3-Flash only

3 Shared

49 Qwen3.7 Plus only

Shared results: 3
GLM-5.3-Flash only: 16
Qwen3.7 Plus only: 49
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.

Agentic

Like-for-like

GLM-5.3-Flash: 60.1; Supported · #21/153
Qwen3.7 Plus: 37.3; Supported · #127/153
Basis: BenchAlign lane · 6 vs 11 public rows
Reading: GLM-5.3-Flash leads

Coding

Directional only

GLM-5.3-Flash: 58.1; Supported · #28/152
Qwen3.7 Plus: 52.6; Estimated · #46/152
Basis: BenchAlign lane · 6 vs 7 public rows
Reading: Directional only

Knowledge

Directional only

GLM-5.3-Flash: 66.0; Supported · #22/183
Qwen3.7 Plus: 56.2; Estimated · #49/183
Basis: BenchAlign lane · 2 vs 7 public rows
Reading: Directional only

Multimodal

Directional only

GLM-5.3-Flash: 80.5; #10/48
Qwen3.7 Plus: 72.5; #18/48
Basis: Provisional lane · 2 vs 2 weighted rows
Reading: Directional only

Reasoning

Not comparable

GLM-5.3-Flash: Not ranked
Qwen3.7 Plus: 74.0; Unranked · 3 rankable rows
Basis: Provisional lane · 0 vs 1 weighted rows
Reading: Not comparable

Math

Not comparable

GLM-5.3-Flash: Not ranked
Qwen3.7 Plus: 78.3; Unranked · 3 rankable rows
Basis: Provisional lane · 0 vs 1 weighted rows
Reading: Not comparable

Multilingual

Not comparable

GLM-5.3-Flash: Not ranked
Qwen3.7 Plus: 78.9; #3/12
Basis: Provisional lane · 0 vs 1 weighted rows
Reading: Not comparable

Instruction following

Not comparable

GLM-5.3-Flash: Not ranked
Qwen3.7 Plus: 91.1; #17/123
Basis: Provisional lane · 0 vs 1 weighted rows
Reading: Not comparable

Category scores with the server-provided evidence basis for GLM-5.3-Flash and Qwen3.7 Plus
Category	GLM-5.3-Flash	Qwen3.7 Plus	Basis	Reading
Agentic	60.1Supported · #21/153	37.3Supported · #127/153	Like-for-likeBenchAlign lane · 6 vs 11 public rows	GLM-5.3-Flash leads
Coding	58.1Supported · #28/152	52.6Estimated · #46/152	Directional onlyBenchAlign lane · 6 vs 7 public rows	Directional only
Knowledge	66.0Supported · #22/183	56.2Estimated · #49/183	Directional onlyBenchAlign lane · 2 vs 7 public rows	Directional only
Multimodal	80.5#10/48	72.5#18/48	Directional onlyProvisional lane · 2 vs 2 weighted rows	Directional only
Reasoning	Not ranked	74.0Unranked · 3 rankable rows	Not comparableProvisional lane · 0 vs 1 weighted rows	Not comparable
Math	Not ranked	78.3Unranked · 3 rankable rows	Not comparableProvisional lane · 0 vs 1 weighted rows	Not comparable
Multilingual	Not ranked	78.9#3/12	Not comparableProvisional lane · 0 vs 1 weighted rows	Not comparable
Instruction following	Not ranked	91.1#17/123	Not comparableProvisional lane · 0 vs 1 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.

CharXiv
Multimodal
GLM-5.3-Flash: 89.4%Qwen3.7 Plus: 85.9%Normalized gap 3.5GLM-5.3-Flash source Qwen3.7 Plus 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-5.3-Flash: Self-hosted; infrastructure cost varies; Fits in one request
Qwen3.7 Plus: API rate not published; Fits in one request

GLM-5.3-Flash has no comparable published API token rate. Qwen3.7 Plus 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
Qwen3.7 Plus: API rate not published; Fits in one request

GLM-5.3-Flash has no comparable published API token rate. Qwen3.7 Plus 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
Qwen3.7 Plus: API rate not published; Fits in one request; Cached-input rate unavailable

GLM-5.3-Flash has no comparable published API token rate. Qwen3.7 Plus 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-5.3-FlashQwen3.7 Plus

Context window

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

GLM-5.3-Flash

GLM-5.3-Flash model card

Qwen3.7 Plus

API model ID

GLM-5.3-Flash

glm-5.3-flash

GLM-5.3-Flash model card

Qwen3.7 Plus

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

Qwen3.7 Plus

No comparable hosted API rate

Documented inputs

GLM-5.3-Flash

Not sourced

Qwen3.7 Plus

Not sourced

Documented outputs

GLM-5.3-Flash

Not sourced

Qwen3.7 Plus

Not sourced

Provider availability

GLM-5.3-Flash

Not sourced

Qwen3.7 Plus

Not sourced

Reasoning profile

GLM-5.3-Flash

Reasoning

Qwen3.7 Plus

Reasoning

Weight access

GLM-5.3-Flash

Open Weight

Qwen3.7 Plus

Proprietary

License

GLM-5.3-Flash

Open Weight

Qwen3.7 Plus

Proprietary

Release date

GLM-5.3-Flash

2026-08-26

Qwen3.7 Plus

2026-06-03

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 score estimate, 66.06 versus 61.64, but the 90% score intervals overlap.
Workload cost: A complete comparable API-rate estimate is not available for both models.
Context tradeoff: Both models list 1M.

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

Benchmark evidence

The full public result ledger is available for audit without forcing a wide desktop table onto a phone.

Browse raw public benchmark evidence68 rows

Agentic

Terminal-Bench 2.1
GLM-5.3-Flash84.3%
Source
Qwen3.7 Plus—
Not directly comparable
Toolathlon-Verified
GLM-5.3-Flash78.4%
Source
Qwen3.7 Plus—
Not directly comparable
AutomationBench
GLM-5.3-Flash48.8%
Source
Qwen3.7 Plus—
Not directly comparable
Agents' Last Exam
GLM-5.3-Flash26.3%
Source
Qwen3.7 Plus—
Not directly comparable
HLE w/ tools
GLM-5.3-Flash55.3%
Source
Qwen3.7 Plus—
Not directly comparable
Terminal-Bench 2.1 (Vals)
GLM-5.3-Flash62.9%
Source
Qwen3.7 Plus52.8%
Source
GLM-5.3-Flash leads this result
Terminal-Bench 2.0
GLM-5.3-Flash—
Qwen3.7 Plus70.3%
Source
Not directly comparable
QwenClawBench
GLM-5.3-Flash—
Qwen3.7 Plus61.8%
Source
Not directly comparable
Claw-Eval
GLM-5.3-Flash—
Qwen3.7 Plus62.7%
Source
Not directly comparable
BFCL v4
GLM-5.3-Flash—
Qwen3.7 Plus72.9%
Source
Not directly comparable
MCP Atlas
GLM-5.3-Flash—
Qwen3.7 Plus73.2%
Source
Not directly comparable
VITA-Bench
GLM-5.3-Flash—
Qwen3.7 Plus45.6%
Source
Not directly comparable
DeepPlanning
GLM-5.3-Flash—
Qwen3.7 Plus62.3%
Source
Not directly comparable
OSWorld-Verified
GLM-5.3-Flash—
Qwen3.7 Plus73.3%
Source
Not directly comparable
AndroidWorld
GLM-5.3-Flash—
Qwen3.7 Plus81.0%
Source
Not directly comparable
OSWorld 2.0
GLM-5.3-Flash—
Qwen3.7 Plus2.8%
Source
Not directly comparable

Coding

Terminal-Bench 2.1
GLM-5.3-Flash84.3%
Source
Qwen3.7 Plus—
Not directly comparable
DeepSWE
GLM-5.3-Flash63.4%
Source
Qwen3.7 Plus—
Not directly comparable
NL2Repo
GLM-5.3-Flash56.3%
Source
Qwen3.7 Plus41.1%
Source
GLM-5.3-Flash leads this result
LiveCodeBench (Vals)
GLM-5.3-Flash80.5%
Source
Qwen3.7 Plus—
Not directly comparable
SWE-bench (Vals)
GLM-5.3-Flash92.0%
Source
Qwen3.7 Plus—
Not directly comparable
OpenHarmony Bench
GLM-5.3-Flash57.3%
Source
Qwen3.7 Plus—
Not directly comparable
Terminal-Bench 2.0
GLM-5.3-Flash—
Qwen3.7 Plus70.3%
Source
Not directly comparable
SWE-bench Verified
GLM-5.3-Flash—
Qwen3.7 Plus77.7%
Source
Not directly comparable
SWE-bench Pro
GLM-5.3-Flash—
Qwen3.7 Plus57.6%
Source
Not directly comparable
SWE Multilingual
GLM-5.3-Flash—
Qwen3.7 Plus75.8%
Source
Not directly comparable
SciCode
GLM-5.3-Flash—
Qwen3.7 Plus51.3%
Source
Not directly comparable
LiveCodeBench
GLM-5.3-Flash—
Qwen3.7 Plus89.6%
Source
Not directly comparable

Reasoning

CritPt
GLM-5.3-Flash—
Qwen3.7 Plus9.1%
Source
Not directly comparable
MRCRv2
GLM-5.3-Flash—
Qwen3.7 Plus91.7%
Source
Not directly comparable

Knowledge

GPQA Diamond (Vals)
GLM-5.3-Flash86.4%
Source
Qwen3.7 Plus—
Not directly comparable
MMLU-Pro (Vals)
GLM-5.3-Flash86.1%
Source
Qwen3.7 Plus—
Not directly comparable
GPQA
GLM-5.3-Flash—
Qwen3.7 Plus90.3%
Source
Not directly comparable
GPQA-D
GLM-5.3-Flash—
Qwen3.7 Plus90.3%
Source
Not directly comparable
HLE
GLM-5.3-Flash—
Qwen3.7 Plus34.7%
Source
Not directly comparable
MMLU-Pro
GLM-5.3-Flash—
Qwen3.7 Plus88.5%
Source
Not directly comparable
MMLU-Redux
GLM-5.3-Flash—
Qwen3.7 Plus94.5%
Source
Not directly comparable
SuperGPQA
GLM-5.3-Flash—
Qwen3.7 Plus71.4%
Source
Not directly comparable
MMMLU
GLM-5.3-Flash—
Qwen3.7 Plus89.0%
Source
Not directly comparable

Math

HMMT Feb 2026
GLM-5.3-Flash—
Qwen3.7 Plus92.9%
Source
Not directly comparable
IMOAnswerBench
GLM-5.3-Flash—
Qwen3.7 Plus86.0%
Source
Not directly comparable
Apex
GLM-5.3-Flash—
Qwen3.7 Plus22.7%
Source
Not directly comparable

Multilingual

MMLU-ProX
GLM-5.3-Flash—
Qwen3.7 Plus85.4%
Source
Not directly comparable
NOVA-63
GLM-5.3-Flash—
Qwen3.7 Plus58.8%
Source
Not directly comparable
INCLUDE
GLM-5.3-Flash—
Qwen3.7 Plus83.0%
Source
Not directly comparable
MAXIFE
GLM-5.3-Flash—
Qwen3.7 Plus88.8%
Source
Not directly comparable
PolyMath
GLM-5.3-Flash—
Qwen3.7 Plus84.0%
Source
Not directly comparable

Multimodal

OfficeQA Pro
GLM-5.3-Flash62.4%
Source
Qwen3.7 Plus—
Not directly comparable
CharXiv
GLM-5.3-Flash89.4%
Source
Qwen3.7 Plus85.9%
Source
GLM-5.3-Flash leads this result
Chartography (tools)
GLM-5.3-Flash78.0%
Source
Qwen3.7 Plus—
Not directly comparable
BabyVision
GLM-5.3-Flash53.4%
Source
Qwen3.7 Plus—
Not directly comparable
MMVU
GLM-5.3-Flash80.5%
Source
Qwen3.7 Plus—
Not directly comparable
MMMU-Pro
GLM-5.3-Flash—
Qwen3.7 Plus79%
Source
Not directly comparable
MathVision
GLM-5.3-Flash—
Qwen3.7 Plus90.3%
Source
Not directly comparable
ERQA
GLM-5.3-Flash—
Qwen3.7 Plus69.8%
Source
Not directly comparable
MedXpertQA (MM)
GLM-5.3-Flash—
Qwen3.7 Plus71.0%
Source
Not directly comparable
ScreenSpot Pro
GLM-5.3-Flash—
Qwen3.7 Plus79.0%
Source
Not directly comparable
SimpleVQA
GLM-5.3-Flash—
Qwen3.7 Plus81.7%
Source
Not directly comparable
MMSearch-Plus
GLM-5.3-Flash—
Qwen3.7 Plus41.4%
Source
Not directly comparable
RealWorldQA
GLM-5.3-Flash—
Qwen3.7 Plus86.9%
Source
Not directly comparable
OmniDocBench 1.5
GLM-5.3-Flash—
Qwen3.7 Plus91.4%
Source
Not directly comparable
OCRBench V2
GLM-5.3-Flash—
Qwen3.7 Plus70.7%
Source
Not directly comparable
ODINW13
GLM-5.3-Flash—
Qwen3.7 Plus51.1%
Source
Not directly comparable
Video-MME (with subtitle)
GLM-5.3-Flash—
Qwen3.7 Plus88.0%
Source
Not directly comparable
VideoMMMU
GLM-5.3-Flash—
Qwen3.7 Plus85.4%
Source
Not directly comparable
MLVU (M-Avg)
GLM-5.3-Flash—
Qwen3.7 Plus87.4%
Source
Not directly comparable

Instruction following

IFEval
GLM-5.3-Flash—
Qwen3.7 Plus94.6%
Source
Not directly comparable
IFBench
GLM-5.3-Flash—
Qwen3.7 Plus79.1%
Source
Not directly comparable

Frequently asked questions

Which is better, GLM-5.3-Flash or Qwen3.7 Plus?

GLM-5.3-Flash has the higher public score estimate, 66.06 versus 61.64, 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-5.3-Flash or Qwen3.7 Plus?

GLM-5.3-Flash scores higher for coding on the public lane, 58.1 to 52.6. Qwen3.7 Plus 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-5.3-Flash or Qwen3.7 Plus?

GLM-5.3-Flash leads the public agentic tasks lane, 60.1 to 37.3, with Supported evidence for both models and non-overlapping 90% intervals.

Which costs less, GLM-5.3-Flash or Qwen3.7 Plus?

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 Qwen3.7 Plus?

Both models list the same context window, 1M.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated September 14, 2026

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GLM-5.3-Flash vs Qwen3.7 Plus

Which one for your work

Agentic work

Coding work

Long documents

Chat turn cost

Cache-heavy agent loop cost

Repository review cost

What is actually comparable

Category results, on a stated basis

Agentic

Coding

Knowledge

Multimodal

Reasoning

Math

Multilingual

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

Benchmark evidence

Agentic

Coding

Reasoning

Knowledge

Math

Multilingual

Multimodal

Instruction following

Frequently asked questions

Which is better, GLM-5.3-Flash or Qwen3.7 Plus?

Which is better for coding, GLM-5.3-Flash or Qwen3.7 Plus?

Which is better for agentic tasks, GLM-5.3-Flash or Qwen3.7 Plus?

Which costs less, GLM-5.3-Flash or Qwen3.7 Plus?

Which has the larger context window, GLM-5.3-Flash or Qwen3.7 Plus?

Related comparisons

Watch GLM-5.3-Flash vs Qwen3.7 Plus