Model comparison

Gemma 4 E4B vs GLM-5.2

Updated July 31, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

21 confirmed releases in the last 30 daystrack changes

Gemma 4 E4B

Google

42.0/100

Estimated · Public rank #167

90% interval 30.5–53.6

GLM-5.2

Z.AI

62.9/100

Estimated · Public rank #41

90% interval 47.7–78.2

GLM-5.2 has the higher public score estimate, 62.94 versus 42.04, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.

1 results are shared. Category rows based on different benchmark sets are marked directional and do not name a winner.

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
GLM-5.2
GLM-5.2 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
No shared weighted benchmark basis supports a winner.
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
No shared weighted benchmark basis supports a winner.
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. Gemma 4 E4B does not fit this workload in one request. GLM-5.2 has no published cached-input rate, so cached tokens use its listed input rate. Gemma 4 E4B 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.

1 Gemma 4 E4B only

1 Shared

17 GLM-5.2 only

Shared results: 1
Gemma 4 E4B only: 1
GLM-5.2 only: 17
Like-for-like categories: 0 / 8

1 category uses different evidence sets. Those rows remain visible for coverage context but do not name a winner.

Category results, on a stated basis

Each row states whether both averages use the same weighted benchmark set. Directional and not-comparable rows remain visible, but they never receive a winner in this template.

Knowledge

Directional only

Gemma 4 E4B: 67.4
GLM-5.2: 59.6
Weighted basis: 2 vs 2 rows
Reading: Directional only

Agentic

Not comparable

Gemma 4 E4B: Not measured
GLM-5.2: 81.0
Weighted basis: 0 vs 1 rows
Reading: Not comparable

Coding

Not comparable

Gemma 4 E4B: Not measured
GLM-5.2: 62.1
Weighted basis: 0 vs 1 rows
Reading: Not comparable

Reasoning

Not comparable

Gemma 4 E4B: Not measured
GLM-5.2: Not measured
Weighted basis: 0 vs 0 rows
Reading: Not comparable

Math

Not comparable

Gemma 4 E4B: Not measured
GLM-5.2: 95.9
Weighted basis: 0 vs 2 rows
Reading: Not comparable

Multilingual

Not comparable

Gemma 4 E4B: Not measured
GLM-5.2: Not measured
Weighted basis: 0 vs 0 rows
Reading: Not comparable

Multimodal

Not comparable

Gemma 4 E4B: Not measured
GLM-5.2: Not measured
Weighted basis: 0 vs 0 rows
Reading: Not comparable

Instruction following

Not comparable

Gemma 4 E4B: Not measured
GLM-5.2: Not measured
Weighted basis: 0 vs 0 rows
Reading: Not comparable

Category averages with the server-provided evidence basis for Gemma 4 E4B and GLM-5.2
Category	Gemma 4 E4B	GLM-5.2	Weighted basis	Reading
Knowledge	67.4	59.6	Directional only2 vs 2 rows	Directional only
Agentic	Not measured	81.0	Not comparable0 vs 1 rows	Not comparable
Coding	Not measured	62.1	Not comparable0 vs 1 rows	Not comparable
Reasoning	Not measured	Not measured	Not comparable0 vs 0 rows	Not comparable
Math	Not measured	95.9	Not comparable0 vs 2 rows	Not comparable
Multilingual	Not measured	Not measured	Not comparable0 vs 0 rows	Not comparable
Multimodal	Not measured	Not measured	Not comparable0 vs 0 rows	Not comparable
Instruction following	Not measured	Not measured	Not comparable0 vs 0 rows	Not comparable

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.

GPQA
Knowledge
Gemma 4 E4B: 58.6%GLM-5.2: 91.2%Normalized gap 32.6Gemma 4 E4B source GLM-5.2 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

Gemma 4 E4B: Self-hosted; infrastructure cost varies; Fits in one request
GLM-5.2: $0.0036; Fits in one request

Gemma 4 E4B has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

Gemma 4 E4B: Self-hosted; infrastructure cost varies; Fits in one request
GLM-5.2: $0.0832; Fits in one request

Gemma 4 E4B has no comparable published API token rate.

Cache-heavy agent loop

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

Gemma 4 E4B: Self-hosted; infrastructure cost varies; Does not fit in one request; Cached-input rate unavailable
GLM-5.2: $0.352; Fits in one request; Cached input priced at the published list-input rate

Gemma 4 E4B does not fit this workload in one request. GLM-5.2 has no published cached-input rate, so cached tokens use its listed input rate. Gemma 4 E4B 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.

SpecificationGemma 4 E4BGLM-5.2

Context window

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

Gemma 4 E4B

128K

Google Gemma 4 model documentation

GLM-5.2

Gemma 4 E4B

Reasoning

GLM-5.2

Reasoning

Weight access

Gemma 4 E4B

Open Weight

GLM-5.2

Open Weight

License

Gemma 4 E4B

Open Weight

GLM-5.2

Open Weight

Release date

Gemma 4 E4B

2026-04-02

GLM-5.2

2026-06-16

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-5.2 has the higher public score estimate, 62.94 versus 42.04, but the 90% score intervals overlap.
Workload cost: A complete comparable API-rate estimate is not available for both models.
Context tradeoff: GLM-5.2 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 evidence19 rows

Agentic

Terminal-Bench 2.0
Gemma 4 E4B—
GLM-5.281%
Source
Not directly comparable
MCP Atlas
Gemma 4 E4B—
GLM-5.276.8%
Source
Not directly comparable
Toolathlon
Gemma 4 E4B—
GLM-5.248.2%
Source
Not directly comparable
ResearchClawBench
Gemma 4 E4B—
GLM-5.220.7%
Source
Not directly comparable

Coding

SWE-bench Pro
Gemma 4 E4B—
GLM-5.262.1%
Source
Not directly comparable
NL2Repo
Gemma 4 E4B—
GLM-5.248.9%
Source
Not directly comparable
Terminal-Bench 2.0
Gemma 4 E4B—
GLM-5.281.0%
Source
Not directly comparable
ProgramBench
Gemma 4 E4B—
GLM-5.263.7%
Source
Not directly comparable
cursorBench32
Gemma 4 E4B—
GLM-5.255.0%
Source
Not directly comparable

Reasoning

CritPt
Gemma 4 E4B—
GLM-5.220.9%
Source
Not directly comparable

Knowledge

GPQA
Gemma 4 E4B58.6%
Source
GLM-5.291.2%
Source
GLM-5.2 leads this result
MMLU-Pro
Gemma 4 E4B69.4%
Source
GLM-5.2—
Not directly comparable
GPQA-D
Gemma 4 E4B—
GLM-5.291.2%
Source
Not directly comparable
HLE
Gemma 4 E4B—
GLM-5.254.7%
Source
Not directly comparable
HLE w/o tools
Gemma 4 E4B—
GLM-5.240.5%
Source
Not directly comparable

Math

AIME26
Gemma 4 E4B—
GLM-5.299.2%
Source
Not directly comparable
HMMT Nov 2025
Gemma 4 E4B—
GLM-5.294.4%
Source
Not directly comparable
HMMT Feb 2026
Gemma 4 E4B—
GLM-5.292.5%
Source
Not directly comparable
MMAnswerBench
Gemma 4 E4B—
GLM-5.291.0%
Source
Not directly comparable

Frequently asked questions

Which is better, Gemma 4 E4B or GLM-5.2?

GLM-5.2 has the higher public score estimate, 62.94 versus 42.04, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.

Which is better for coding, Gemma 4 E4B or GLM-5.2?

The published evidence does not provide a shared weighted coding basis for both models, so BenchLM does not name a coding winner.

Which is better for agentic tasks, Gemma 4 E4B or GLM-5.2?

The published evidence does not provide a shared weighted agentic tasks basis for both models, so BenchLM does not name a agentic tasks winner.

Which costs less, Gemma 4 E4B or GLM-5.2?

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, Gemma 4 E4B or GLM-5.2?

GLM-5.2 has the larger documented context window: 1M, compared with 128K.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated July 31, 2026

Watch Gemma 4 E4B vs GLM-5.2

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

Read a sample issue

Join 2,000+ readers.

Gemma 4 E4B vs GLM-5.2

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

Knowledge

Agentic

Coding

Reasoning

Math

Multilingual

Multimodal

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

Frequently asked questions

Which is better, Gemma 4 E4B or GLM-5.2?

Which is better for coding, Gemma 4 E4B or GLM-5.2?

Which is better for agentic tasks, Gemma 4 E4B or GLM-5.2?

Which costs less, Gemma 4 E4B or GLM-5.2?

Which has the larger context window, Gemma 4 E4B or GLM-5.2?

Related comparisons

Watch Gemma 4 E4B vs GLM-5.2