GLM-4.7 vs GLM-5

Updated October 17, 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 score estimate, 54.84 versus 48.85, 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.

Model A

GLM-4.7

Z.AI

48.85/100

Supported · Public rank #99

90% interval 36.7–61.0

Model B

GLM-5

Z.AI

54.84/100

Supported · Public rank #69

90% interval 44.1–65.5

Shared results: 10
GLM-4.7 only: 7
GLM-5 only: 28
Like-for-like categories: 0 / 8

Supported: GLM-4.7 and GLM-5How 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.

No workload recommendation clears the current evidence threshold.

Use the matched evidence, workload costs, and sourced specifications below instead of treating a point score as a universal answer.

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-4.7 and GLM-5 are scored on Estimated evidence for agentic, 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
The page does not recommend a cost winner because at least one model cannot fit the stated workload in one request. GLM-4.7 does not fit this 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. GLM-4.7 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.

37.2GLM-4.738.0GLM-5

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.

Terminal-Bench 2.0Agentic
Normalized gap 15.2
GLM-4.7:41%
GLM-5:56.2%
GLM-4.7 source GLM-5 source
BrowseCompAgentic
Normalized gap 10.0
GLM-4.7:52%
GLM-5:62%
GLM-4.7 source GLM-5 source
HLEKnowledge
Normalized gap 5.7
GLM-4.7:24.8%
GLM-5:30.5%
GLM-4.7 source GLM-5 source
SWE-RebenchCoding
Normalized gap 4.1
GLM-4.7:58.7%
GLM-5:62.8%
GLM-4.7 source GLM-5 source
SWE-bench VerifiedCoding
Normalized gap 4.0
GLM-4.7:73.8%
GLM-5:77.8%
GLM-4.7 source GLM-5 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-4.7: 31.6; Estimated · #91/128
GLM-5: 41.5; Estimated · #63/128
Basis: BenchAlign v5.8 lane · 4 vs 13 public rows
Reading: Directional only

Coding

Directional only

GLM-4.7: 37.2; Supported · #72/150
GLM-5: 38.0; Estimated · #70/150
Basis: BenchAlign v5.8 lane · 6 vs 8 public rows
Reading: Directional only

Knowledge

Directional only

GLM-4.7: 44.1; Supported · #100/184
GLM-5: 50.4; Estimated · #79/184
Basis: BenchAlign v5.8 lane · 5 vs 6 public rows
Reading: Directional only

Instruction following

Directional only

GLM-4.7: 82.8; #54/148
GLM-5: 88.6; #35/148
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Directional only

Reasoning

Not comparable

GLM-4.7: 74.6; Unranked · 2 rankable rows
GLM-5: 70.9; Unranked · 4 rankable rows
Basis: Provisional lane · 0 vs 1 weighted rows
Reading: Not comparable

Multimodal

Not comparable

GLM-4.7: Not ranked
GLM-5: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Multilingual

Not comparable

GLM-4.7: Not ranked
GLM-5: 89.5; #6/17
Basis: Provisional lane · 0 vs 1 weighted rows
Reading: Not comparable

Math

Not comparable

GLM-4.7: Not ranked
GLM-5: 83.0; Unranked · 6 rankable rows
Basis: Provisional lane · 0 vs 2 weighted rows
Reading: Not comparable

Category scores with the server-provided evidence basis for GLM-4.7 and GLM-5
Category	GLM-4.7	GLM-5	Basis	Reading
Agentic	31.6Estimated · #91/128	41.5Estimated · #63/128	Directional onlyBenchAlign v5.8 lane · 4 vs 13 public rows	Directional only
Coding	37.2Supported · #72/150	38.0Estimated · #70/150	Directional onlyBenchAlign v5.8 lane · 6 vs 8 public rows	Directional only
Knowledge	44.1Supported · #100/184	50.4Estimated · #79/184	Directional onlyBenchAlign v5.8 lane · 5 vs 6 public rows	Directional only
Instruction following	82.8#54/148	88.6#35/148	Directional onlyProvisional lane · 0 vs 0 weighted rows	Directional only
Reasoning	74.6Unranked · 2 rankable rows	70.9Unranked · 4 rankable rows	Not comparableProvisional lane · 0 vs 1 weighted rows	Not comparable
Multimodal	Not ranked	Not ranked	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Multilingual	Not ranked	89.5#6/17	Not comparableProvisional lane · 0 vs 1 weighted rows	Not comparable
Math	Not ranked	83.0Unranked · 6 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-4.7: Self-hosted; infrastructure cost varies; Fits in one request
GLM-5: $0.0026; Fits in one request

GLM-4.7 has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

GLM-4.7: Self-hosted; infrastructure cost varies; Fits in one request
GLM-5: $0.0596; Fits in one request

GLM-4.7 has no comparable published API token rate.

Cache-heavy agent loop

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

GLM-4.7: Self-hosted; infrastructure cost varies; Does not fit in one request; Cached-input rate unavailable
GLM-5: $0.252; Does not fit in one request; Cached input priced at the published list-input rate

GLM-4.7 does not fit this 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. GLM-4.7 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-4.7GLM-5

Context window

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

GLM-4.7

200K

GLM-5

200K

API model ID

GLM-4.7

Not sourced

GLM-5

Not sourced

Cached-input rate

A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.

GLM-4.7

No comparable hosted API rate

GLM-5

Not published

Documented inputs

GLM-4.7

Not sourced

GLM-5

Not sourced

Documented outputs

GLM-4.7

Not sourced

GLM-5

Not sourced

Provider availability

GLM-4.7

Not sourced

GLM-5

Not sourced

Reasoning profile

GLM-4.7

Reasoning

GLM-5

Non-Reasoning

Weight access

GLM-4.7

Open Weight

GLM-5

Open Weight

License

GLM-4.7

Open Weight

GLM-5

Open Weight

Release date

GLM-4.7

2025-12-22

GLM-5

2026-02-12

If you already use one of these models

Deployment change: Both entries list Z.AI as the provider. Confirm endpoint, model ID, limits, and feature support before switching.
Quality signal: GLM-5 has the higher public score estimate, 54.84 versus 48.85, 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 200K.

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

Questions

Which is better, GLM-4.7 or GLM-5?

GLM-5 has the higher public score estimate, 54.84 versus 48.85, 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-4.7 or GLM-5?

GLM-5 scores higher for coding on the public lane, 38 to 37.2. 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-4.7 or GLM-5?

GLM-5 scores higher for agentic tasks on the public lane, 41.5 to 31.6. GLM-4.7 and GLM-5 are 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-4.7 or GLM-5?

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-4.7 or GLM-5?

Both models list the same context window, 200K.

Benchmark evidence

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

Browse raw public benchmark evidence45 rows

Agentic

Terminal-Bench 2.0
GLM-4.741%
Source
GLM-556.2%
Source
GLM-5 leads this result
BrowseComp
GLM-4.752%
Source
GLM-562%
Source
GLM-5 leads this result
VITA-Bench
GLM-4.715.5%
Source
GLM-5—
Not directly comparable
Gert Labs
Shared source
GLM-4.739.95%
GLM-550.99%
GLM-5 leads this result
HLE w/ tools
GLM-4.7—
GLM-550.4%
Source
Not directly comparable
Claw-Eval
GLM-4.7—
GLM-557.7%
Source
Not directly comparable
QwenClawBench
GLM-4.7—
GLM-554.1%
Source
Not directly comparable
τ³-bench results
GLM-4.7—
GLM-565.6%
Source
Not directly comparable
DeepPlanning
GLM-4.7—
GLM-514.6%
Source
Not directly comparable
Toolathlon
GLM-4.7—
GLM-538%
Source
Not directly comparable
MCP Atlas
GLM-4.7—
GLM-531.1%
Source
Not directly comparable
MCP-Tasks
GLM-4.7—
GLM-560.8%
Source
Not directly comparable
WideResearch
GLM-4.7—
GLM-569.8%
Source
Not directly comparable
CyberGym
GLM-4.7—
GLM-543.2%
Source
Not directly comparable

Coding

SWE-bench Verified
GLM-4.773.8%
Source
GLM-577.8%
Source
GLM-5 leads this result
LiveCodeBench v6
GLM-4.784.9%
Source
GLM-5—
Not directly comparable
SWE-Rebench
GLM-4.758.7%
Source
GLM-562.8%
Source
GLM-5 leads this result
Terminal-Bench 2.0
GLM-4.741.0%
Source
GLM-556.2%
Source
GLM-5 leads this result
LiveCodeBench (Vals)
GLM-4.782.2%
Source
GLM-5—
Not directly comparable
SWE-bench (Vals)
GLM-4.769.4%
Source
GLM-5—
Not directly comparable
SWE-bench Verified*
GLM-4.7—
GLM-572.8%
Source
Not directly comparable
SWE-bench Pro
GLM-4.7—
GLM-555.1%
Source
Not directly comparable
SWE Multilingual
GLM-4.7—
GLM-573.3%
Source
Not directly comparable
NL2Repo
GLM-4.7—
GLM-535.9%
Source
Not directly comparable
React Native Evals
GLM-4.7—
GLM-574.8%
Source
Not directly comparable

Reasoning

LongBench v2
GLM-4.7—
GLM-560.8%
Source
Not directly comparable
AI-Needle
GLM-4.7—
GLM-563.3%
Source
Not directly comparable

Knowledge

GPQA
GLM-4.785.7%
Source
GLM-586%
Source
GLM-5 leads this result
MMLU-Pro
GLM-4.784.3%
Source
GLM-585.7%
Source
GLM-5 leads this result
HLE
GLM-4.724.8%
Source
GLM-530.5%
Source
GLM-5 leads this result
GPQA Diamond (Vals)
GLM-4.780.1%
Source
GLM-5—
Not directly comparable
MMLU-Pro (Vals)
GLM-4.782.7%
Source
GLM-5—
Not directly comparable
GPQA-D
GLM-4.7—
GLM-586.0%
Source
Not directly comparable
SuperGPQA
GLM-4.7—
GLM-566.8%
Source
Not directly comparable
MMLU-Pro (Arcee)
GLM-4.7—
GLM-585.8%
Source
Not directly comparable

Multilingual

MMLU-ProX
GLM-4.7—
GLM-583.1%
Source
Not directly comparable
NOVA-63
GLM-4.7—
GLM-555.1%
Source
Not directly comparable

Instruction following

IFEval
GLM-4.7—
GLM-592.6%
Source
Not directly comparable

Math

AIME 2025
GLM-4.795.7%
Source
GLM-5—
Not directly comparable
HMMT Feb 2025
GLM-4.797.1%
Source
GLM-597.5%
Source
GLM-5 leads this result
AIME26
GLM-4.7—
GLM-595.8%
Source
Not directly comparable
AIME25 (Arcee)
GLM-4.7—
GLM-593.3%
Source
Not directly comparable
HMMT Nov 2025
GLM-4.7—
GLM-596.9%
Source
Not directly comparable
HMMT Feb 2026
GLM-4.7—
GLM-586.4%
Source
Not directly comparable
MMAnswerBench
GLM-4.7—
GLM-582.5%
Source
Not directly comparable

45 public results · 10 shared

Related comparisons

Compare API pricing Read the methodology Open the model selector

Watch GLM-4.7 vs GLM-5

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

Read a sample issue

Join 5,600+ readers.

Last updated October 17, 2026

GLM-4.7 vs GLM-5

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

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-4.7 or GLM-5?

Which is better for coding, GLM-4.7 or GLM-5?

Which is better for agentic tasks, GLM-4.7 or GLM-5?

Which costs less, GLM-4.7 or GLM-5?

Which has the larger context window, GLM-4.7 or GLM-5?

Benchmark evidence

Agentic

Coding

Reasoning

Knowledge

Multilingual

Instruction following

Math

Related comparisons

Watch GLM-4.7 vs GLM-5