Model A

Z.AI

62.16/100

Supported · Public rank #62

90% interval 51.5–72.8

GLM-5 vs Qwen3.7 Plus

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

Model B

Qwen3.7 Plus

Alibaba

62.29/100

Supported · Public rank #60

90% interval 52.4–72.2

Decision reading

Qwen3.7 Plus has the higher public score estimate, 62.29 versus 62.16, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.

17 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
Qwen3.7 Plus
Qwen3.7 Plus 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
Qwen3.7 Plus 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. Qwen3.7 Plus 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.

19 GLM-5 only

17 Shared

35 Qwen3.7 Plus only

Shared results: 17
GLM-5 only: 19
Qwen3.7 Plus only: 35
Like-for-like categories: 1 / 8

4 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.

Multilingual

Like-for-like

GLM-5: 48.7; #6/12
Qwen3.7 Plus: 78.9; #3/12
Basis: Provisional lane · 1 vs 1 weighted rows
Reading: Qwen3.7 Plus leads

Agentic

Directional only

GLM-5: 50.0; Estimated · #65/151
Qwen3.7 Plus: 37.7; Supported · #126/151
Basis: BenchAlign lane · 11 vs 11 public rows
Reading: Directional only

Coding

Directional only

GLM-5: 49.1; Supported · #80/183
Qwen3.7 Plus: 53.1; Estimated · #51/183
Basis: BenchAlign lane · 6 vs 7 public rows
Reading: Directional only

Knowledge

Directional only

GLM-5: 54.9; Estimated · #62/181
Qwen3.7 Plus: 56.8; Estimated · #50/181
Basis: BenchAlign lane · 6 vs 7 public rows
Reading: Directional only

Instruction following

Directional only

GLM-5: 88.3; #31/120
Qwen3.7 Plus: 91.1; #17/120
Basis: Provisional lane · 0 vs 1 weighted rows
Reading: Directional only

Reasoning

Not comparable

GLM-5: 51.6; Unranked · 4 rankable rows
Qwen3.7 Plus: 73.7; Unranked · 3 rankable rows
Basis: Provisional lane · 1 vs 1 weighted rows
Reading: Not comparable

Math

Not comparable

GLM-5: 56.9; #7/7
Qwen3.7 Plus: 78.3; Unranked · 3 rankable rows
Basis: Provisional lane · 4 vs 1 weighted rows
Reading: Not comparable

Multimodal

Not comparable

GLM-5: Not ranked
Qwen3.7 Plus: 72.5; #18/48
Basis: Provisional lane · 0 vs 2 weighted rows
Reading: Not comparable

Category scores with the server-provided evidence basis for GLM-5 and Qwen3.7 Plus
Category	GLM-5	Qwen3.7 Plus	Basis	Reading
Multilingual	48.7#6/12	78.9#3/12	Like-for-likeProvisional lane · 1 vs 1 weighted rows	Qwen3.7 Plus leads
Agentic	50.0Estimated · #65/151	37.7Supported · #126/151	Directional onlyBenchAlign lane · 11 vs 11 public rows	Directional only
Coding	49.1Supported · #80/183	53.1Estimated · #51/183	Directional onlyBenchAlign lane · 6 vs 7 public rows	Directional only
Knowledge	54.9Estimated · #62/181	56.8Estimated · #50/181	Directional onlyBenchAlign lane · 6 vs 7 public rows	Directional only
Instruction following	88.3#31/120	91.1#17/120	Directional onlyProvisional lane · 0 vs 1 weighted rows	Directional only
Reasoning	51.6Unranked · 4 rankable rows	73.7Unranked · 3 rankable rows	Not comparableProvisional lane · 1 vs 1 weighted rows	Not comparable
Math	56.9#7/7	78.3Unranked · 3 rankable rows	Not comparableProvisional lane · 4 vs 1 weighted rows	Not comparable
Multimodal	Not ranked	72.5#18/48	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: 50.4%Qwen3.7 Plus: 34.7%Normalized gap 15.7GLM-5 source Qwen3.7 Plus source
Terminal-Bench 2.0
Agentic
GLM-5: 56.2%Qwen3.7 Plus: 70.3%Normalized gap 14.1GLM-5 source Qwen3.7 Plus source
HMMT Feb 2026
Math
GLM-5: 86.4%Qwen3.7 Plus: 92.9%Normalized gap 6.5GLM-5 source Qwen3.7 Plus source
SuperGPQA
Knowledge
GLM-5: 66.8%Qwen3.7 Plus: 71.4%Normalized gap 4.6GLM-5 source Qwen3.7 Plus source
GPQA
Knowledge
GLM-5: 86%Qwen3.7 Plus: 90.3%Normalized gap 4.3GLM-5 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: $0.0026; Fits in one request
Qwen3.7 Plus: API rate not published; Fits in one request

Qwen3.7 Plus has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

GLM-5: $0.0596; Fits in one request
Qwen3.7 Plus: API rate not published; Fits in one request

Qwen3.7 Plus 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
Qwen3.7 Plus: API rate not published; 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. Qwen3.7 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-5Qwen3.7 Plus

Context window

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

GLM-5

200K

Qwen3.7 Plus

API model ID

GLM-5

Not sourced

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

Not published

Qwen3.7 Plus

No comparable hosted API rate

Documented inputs

GLM-5

Not sourced

Qwen3.7 Plus

Not sourced

Documented outputs

GLM-5

Not sourced

Qwen3.7 Plus

Not sourced

Provider availability

GLM-5

Not sourced

Qwen3.7 Plus

Not sourced

Reasoning profile

GLM-5

Non-Reasoning

Qwen3.7 Plus

Reasoning

Weight access

GLM-5

Open Weight

Qwen3.7 Plus

Proprietary

License

GLM-5

Open Weight

Qwen3.7 Plus

Proprietary

Release date

GLM-5

2026-03-01

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: Qwen3.7 Plus has the higher public score estimate, 62.29 versus 62.16, but the 90% score intervals overlap.
Workload cost: A complete comparable API-rate estimate is not available for both models.
Context tradeoff: Qwen3.7 Plus has the larger documented window (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 evidence71 rows

Agentic

Terminal-Bench 2.0
GLM-556.2%
Source
Qwen3.7 Plus70.3%
Source
Qwen3.7 Plus leads this result
Claw-Eval
GLM-557.7%
Source
Qwen3.7 Plus62.7%
Source
Qwen3.7 Plus leads this result
QwenClawBench
GLM-554.1%
Source
Qwen3.7 Plus61.8%
Source
Qwen3.7 Plus leads this result
τ³-bench results
GLM-565.6%
Source
Qwen3.7 Plus—
Not directly comparable
DeepPlanning
GLM-514.6%
Source
Qwen3.7 Plus62.3%
Source
Qwen3.7 Plus leads this result
Toolathlon
GLM-538%
Source
Qwen3.7 Plus—
Not directly comparable
MCP Atlas
GLM-531.1%
Source
Qwen3.7 Plus73.2%
Source
Qwen3.7 Plus leads this result
MCP-Tasks
GLM-560.8%
Source
Qwen3.7 Plus—
Not directly comparable
WideResearch
GLM-569.8%
Source
Qwen3.7 Plus—
Not directly comparable
CyberGym
GLM-543.2%
Source
Qwen3.7 Plus—
Not directly comparable
Gert Labs
GLM-550.99%
Source
Qwen3.7 Plus—
Not directly comparable
BFCL v4
GLM-5—
Qwen3.7 Plus72.9%
Source
Not directly comparable
VITA-Bench
GLM-5—
Qwen3.7 Plus45.6%
Source
Not directly comparable
OSWorld-Verified
GLM-5—
Qwen3.7 Plus73.3%
Source
Not directly comparable
AndroidWorld
GLM-5—
Qwen3.7 Plus81.0%
Source
Not directly comparable
OSWorld 2.0
GLM-5—
Qwen3.7 Plus2.8%
Source
Not directly comparable
Terminal-Bench 2.1 (Vals)
GLM-5—
Qwen3.7 Plus52.8%
Source
Not directly comparable

Coding

SWE-bench Verified
GLM-577.8%
Source
Qwen3.7 Plus77.7%
Source
GLM-5 leads this result
SWE-bench Verified*
GLM-572.8%
Source
Qwen3.7 Plus—
Not directly comparable
SWE-bench Pro
GLM-555.1%
Source
Qwen3.7 Plus57.6%
Source
Qwen3.7 Plus leads this result
SWE Multilingual
GLM-573.3%
Source
Qwen3.7 Plus75.8%
Source
Qwen3.7 Plus leads this result
SWE-Rebench
GLM-562.8%
Source
Qwen3.7 Plus—
Not directly comparable
React Native Evals
GLM-574.8%
Source
Qwen3.7 Plus—
Not directly comparable
Terminal-Bench 2.0
GLM-5—
Qwen3.7 Plus70.3%
Source
Not directly comparable
NL2Repo
GLM-5—
Qwen3.7 Plus41.1%
Source
Not directly comparable
SciCode
GLM-5—
Qwen3.7 Plus51.3%
Source
Not directly comparable
LiveCodeBench
GLM-5—
Qwen3.7 Plus89.6%
Source
Not directly comparable

Reasoning

LongBench v2
GLM-560.8%
Source
Qwen3.7 Plus—
Not directly comparable
AI-Needle
GLM-563.3%
Source
Qwen3.7 Plus—
Not directly comparable
CritPt
GLM-5—
Qwen3.7 Plus9.1%
Source
Not directly comparable
MRCRv2
GLM-5—
Qwen3.7 Plus91.7%
Source
Not directly comparable

Knowledge

GPQA
GLM-586%
Source
Qwen3.7 Plus90.3%
Source
Qwen3.7 Plus leads this result
GPQA-D
GLM-586.0%
Source
Qwen3.7 Plus90.3%
Source
Qwen3.7 Plus leads this result
SuperGPQA
GLM-566.8%
Source
Qwen3.7 Plus71.4%
Source
Qwen3.7 Plus leads this result
MMLU-Pro
GLM-585.7%
Source
Qwen3.7 Plus88.5%
Source
Qwen3.7 Plus leads this result
MMLU-Pro (Arcee)
GLM-585.8%
Source
Qwen3.7 Plus—
Not directly comparable
HLE
GLM-550.4%
Source
Qwen3.7 Plus34.7%
Source
GLM-5 leads this result
MMLU-Redux
GLM-5—
Qwen3.7 Plus94.5%
Source
Not directly comparable
MMMLU
GLM-5—
Qwen3.7 Plus89.0%
Source
Not directly comparable

Math

AIME26
GLM-595.8%
Source
Qwen3.7 Plus—
Not directly comparable
AIME25 (Arcee)
GLM-593.3%
Source
Qwen3.7 Plus—
Not directly comparable
HMMT Feb 2025
GLM-597.5%
Source
Qwen3.7 Plus—
Not directly comparable
HMMT Nov 2025
GLM-596.9%
Source
Qwen3.7 Plus—
Not directly comparable
HMMT Feb 2026
GLM-586.4%
Source
Qwen3.7 Plus92.9%
Source
Qwen3.7 Plus leads this result
MMAnswerBench
GLM-582.5%
Source
Qwen3.7 Plus—
Not directly comparable
FrontierMath v2 (Tiers 1-3)
GLM-516.434%
Source
Qwen3.7 Plus—
Not directly comparable
FrontierMath v2 (Tier 4)
GLM-52.100%
Source
Qwen3.7 Plus—
Not directly comparable
IMOAnswerBench
GLM-5—
Qwen3.7 Plus86.0%
Source
Not directly comparable
Apex
GLM-5—
Qwen3.7 Plus22.7%
Source
Not directly comparable

Multilingual

MMLU-ProX
GLM-583.1%
Source
Qwen3.7 Plus85.4%
Source
Qwen3.7 Plus leads this result
NOVA-63
GLM-555.1%
Source
Qwen3.7 Plus58.8%
Source
Qwen3.7 Plus leads this result
INCLUDE
GLM-5—
Qwen3.7 Plus83.0%
Source
Not directly comparable
MAXIFE
GLM-5—
Qwen3.7 Plus88.8%
Source
Not directly comparable
PolyMath
GLM-5—
Qwen3.7 Plus84.0%
Source
Not directly comparable

Multimodal

MMMU-Pro
GLM-5—
Qwen3.7 Plus79%
Source
Not directly comparable
MathVision
GLM-5—
Qwen3.7 Plus90.3%
Source
Not directly comparable
CharXiv
GLM-5—
Qwen3.7 Plus85.9%
Source
Not directly comparable
ERQA
GLM-5—
Qwen3.7 Plus69.8%
Source
Not directly comparable
MedXpertQA (MM)
GLM-5—
Qwen3.7 Plus71.0%
Source
Not directly comparable
ScreenSpot Pro
GLM-5—
Qwen3.7 Plus79.0%
Source
Not directly comparable
SimpleVQA
GLM-5—
Qwen3.7 Plus81.7%
Source
Not directly comparable
MMSearch-Plus
GLM-5—
Qwen3.7 Plus41.4%
Source
Not directly comparable
RealWorldQA
GLM-5—
Qwen3.7 Plus86.9%
Source
Not directly comparable
OmniDocBench 1.5
GLM-5—
Qwen3.7 Plus91.4%
Source
Not directly comparable
OCRBench V2
GLM-5—
Qwen3.7 Plus70.7%
Source
Not directly comparable
ODINW13
GLM-5—
Qwen3.7 Plus51.1%
Source
Not directly comparable
Video-MME (with subtitle)
GLM-5—
Qwen3.7 Plus88.0%
Source
Not directly comparable
VideoMMMU
GLM-5—
Qwen3.7 Plus85.4%
Source
Not directly comparable
MLVU (M-Avg)
GLM-5—
Qwen3.7 Plus87.4%
Source
Not directly comparable

Instruction following

IFEval
GLM-592.6%
Source
Qwen3.7 Plus94.6%
Source
Qwen3.7 Plus leads this result
IFBench
GLM-5—
Qwen3.7 Plus79.1%
Source
Not directly comparable

Frequently asked questions

Which is better, GLM-5 or Qwen3.7 Plus?

Qwen3.7 Plus has the higher public score estimate, 62.29 versus 62.16, 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 or Qwen3.7 Plus?

Qwen3.7 Plus scores higher for coding on the public lane, 53.1 to 49.1. 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 or Qwen3.7 Plus?

GLM-5 scores higher for agentic tasks on the public lane, 50 to 37.7. 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 Qwen3.7 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 or Qwen3.7 Plus?

Qwen3.7 Plus has the larger documented context window: 1M, compared with 200K.

Related comparisons

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Last updated September 4, 2026

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GLM-5 vs Qwen3.7 Plus

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

Multilingual

Agentic

Coding

Knowledge

Instruction following

Reasoning

Math

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

Benchmark evidence

Agentic

Coding

Reasoning

Knowledge

Math

Multilingual

Multimodal

Instruction following

Frequently asked questions

Which is better, GLM-5 or Qwen3.7 Plus?

Which is better for coding, GLM-5 or Qwen3.7 Plus?

Which is better for agentic tasks, GLM-5 or Qwen3.7 Plus?

Which costs less, GLM-5 or Qwen3.7 Plus?

Which has the larger context window, GLM-5 or Qwen3.7 Plus?

Related comparisons

Watch GLM-5 vs Qwen3.7 Plus