GLM-5.3 vs Hy4 preview

Updated October 13, 2026. Rank says GLM-5.3 is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.

Decision reading

GLM-5.3 has the higher public point estimate, 67.24 versus 60.86. Their conditional score ranges overlap. These ranges do not establish rank confidence. 13 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-5.3

Z.AI

67.24/100

Supported · Public rank #25

90% interval 62.6–71.9

Model B

Hy4 preview

Tencent

60.86/100

Estimated · Public rank #48

Conditional range 46.5–75.2

Shared results: 13
GLM-5.3 only: 15
Hy4 preview only: 17
Like-for-like categories: 1 / 8

Supported: GLM-5.3 · Estimated: Hy4 preview. Conditional ranges do not establish rank confidence.How 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
No clear pick
The like-for-like coding result is a practical tie on the public lane (within 0.5 points).
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
Hy4 preview is 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
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

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.

56.7GLM-5.357.1Hy4 preview

Like-for-like · BenchAlign v5.8

Hy4 preview 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.

AutomationBenchAgentic
Normalized gap 16.1
GLM-5.3:48.2%
Hy4 preview:32.1%
GLM-5.3 source Hy4 preview 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.

Coding

Like-for-like

GLM-5.3: 56.7; Supported · #27/148
Hy4 preview: 57.1; Supported · #26/148
Basis: BenchAlign v5.8 lane · 14 vs 8 public rows
Reading: Practical tie

Agentic

Directional only

GLM-5.3: 67.6; Supported · #11/123
Hy4 preview: 54.5; Estimated · #37/123
Basis: BenchAlign v5.8 lane · 9 vs 15 public rows
Reading: Directional only

Knowledge

Directional only

GLM-5.3: 62.0; Supported · #42/180
Hy4 preview: 63.6; Estimated · #39/180
Basis: BenchAlign v5.8 lane · 2 vs 4 public rows
Reading: Directional only

Reasoning

Not comparable

GLM-5.3: 80.6; #13/29
Hy4 preview: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Multimodal

Not comparable

GLM-5.3: Not ranked
Hy4 preview: 88.3; Unranked · 1 rankable row
Basis: Provisional lane · 0 vs 1 weighted rows
Reading: Not comparable

Multilingual

Not comparable

GLM-5.3: Not ranked
Hy4 preview: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Instruction following

Not comparable

GLM-5.3: Not ranked
Hy4 preview: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Math

Not comparable

GLM-5.3: 71.5; Unranked · 2 rankable rows
Hy4 preview: Not ranked
Basis: Provisional lane · 2 vs 0 weighted rows
Reading: Not comparable

Category scores with the server-provided evidence basis for GLM-5.3 and Hy4 preview
Category	GLM-5.3	Hy4 preview	Basis	Reading
Coding	56.7Supported · #27/148	57.1Supported · #26/148	Like-for-likeBenchAlign v5.8 lane · 14 vs 8 public rows	Practical tie
Agentic	67.6Supported · #11/123	54.5Estimated · #37/123	Directional onlyBenchAlign v5.8 lane · 9 vs 15 public rows	Directional only
Knowledge	62.0Supported · #42/180	63.6Estimated · #39/180	Directional onlyBenchAlign v5.8 lane · 2 vs 4 public rows	Directional only
Reasoning	80.6#13/29	Not ranked	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Multimodal	Not ranked	88.3Unranked · 1 rankable row	Not comparableProvisional lane · 0 vs 1 weighted rows	Not comparable
Multilingual	Not ranked	Not ranked	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Instruction following	Not ranked	Not ranked	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Math	71.5Unranked · 2 rankable rows	Not ranked	Not comparableProvisional lane · 2 vs 0 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-5.3: Self-hosted; infrastructure cost varies; Fits in one request
Hy4 preview: Self-hosted; infrastructure cost varies; Fits in one request

GLM-5.3 has no comparable published API token rate. Hy4 preview has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

GLM-5.3: Self-hosted; infrastructure cost varies; Fits in one request
Hy4 preview: Self-hosted; infrastructure cost varies; Fits in one request

GLM-5.3 has no comparable published API token rate. Hy4 preview has no comparable published API token rate.

Cache-heavy agent loop

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

GLM-5.3: Self-hosted; infrastructure cost varies; Fits in one request; Cached-input rate unavailable
Hy4 preview: Self-hosted; infrastructure cost varies; Fits in one request; Cached-input rate unavailable

GLM-5.3 has no comparable published API token rate. Hy4 preview 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-5.3Hy4 preview

Context window

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

GLM-5.3

Z.AI GLM-5.3 model card

Hy4 preview

Tencent Hy4 preview model card

API model ID

GLM-5.3

zai-org/GLM-5.3

Z.AI GLM-5.3 model card

Hy4 preview

tencent/Hy4-preview

Tencent Hy4 preview model card

Cached-input rate

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

GLM-5.3

No comparable hosted API rate

Z.AI GLM-5.3 model card

Hy4 preview

No comparable hosted API rate

Tencent Hy4 preview model card

Documented inputs

GLM-5.3

Not sourced

Hy4 preview

Not sourced

Documented outputs

GLM-5.3

Not sourced

Hy4 preview

Not sourced

Provider availability

GLM-5.3

Not sourced

Hy4 preview

Not sourced

Reasoning profile

GLM-5.3

Reasoning

Hy4 preview

Reasoning

Weight access

GLM-5.3

Open Weight

Hy4 preview

Open Weight

License

GLM-5.3

Open Weight

Hy4 preview

Open Weight

Release date

GLM-5.3

2026-08-14

Hy4 preview

2026-08-28

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 has the higher public point estimate, 67.24 versus 60.86. Their conditional score ranges overlap. These ranges do not establish rank confidence.
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.

Questions

Which is better, GLM-5.3 or Hy4 preview?

GLM-5.3 has the higher public point estimate, 67.24 versus 60.86. Their conditional score ranges overlap. These ranges do not establish rank confidence. The page therefore keeps the decision tied to the specific documented workload.

Which is better for coding, GLM-5.3 or Hy4 preview?

The like-for-like coding row is a practical tie on the public lane, 56.7 against 57.1, inside the 0.5-point band BenchLM treats as level.

Which is better for agentic tasks, GLM-5.3 or Hy4 preview?

GLM-5.3 scores higher for agentic tasks on the public lane, 67.6 to 54.5. Hy4 preview 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-5.3 or Hy4 preview?

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 or Hy4 preview?

Both models list the same context window, 1M.

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 2.1
GLM-5.388.2%
Source
Hy4 preview85.4%
Source
GLM-5.3 leads this result
terminalBench3
GLM-5.328.3%
Source
Hy4 preview—
Not directly comparable
CyberGym
GLM-5.384.5%
Source
Hy4 preview78.4%
Source
GLM-5.3 leads this result
ExploitGym
GLM-5.315.0%
Source
Hy4 preview—
Not directly comparable
Toolathlon-Verified
GLM-5.373.0%
Source
Hy4 preview74.1%
Source
Hy4 preview leads this result
AutomationBench
GLM-5.348.2%
Source
Hy4 preview32.1%
Source
GLM-5.3 leads this result
Agents' Last Exam
GLM-5.328.5%
Source
Hy4 preview22.8%
Source
GLM-5.3 leads this result
HLE w/ tools
GLM-5.362.5%
Source
Hy4 preview55.4%
Source
GLM-5.3 leads this result
Terminal-Bench 2.1 (Vals)
GLM-5.371.5%
Source
Hy4 preview55.1%
Source
GLM-5.3 leads this result
WideResearch
GLM-5.3—
Hy4 preview83.9%
Source
Not directly comparable
DRACO
GLM-5.3—
Hy4 preview77.2%
Source
Not directly comparable
MCP Atlas
GLM-5.3—
Hy4 preview83.7%
Source
Not directly comparable
APEX-Agents
GLM-5.3—
Hy4 preview37.1%
Source
Not directly comparable
skillsBench
GLM-5.3—
Hy4 preview62.9%
Source
Not directly comparable
JobBench
GLM-5.3—
Hy4 preview61.7%
Source
Not directly comparable
BankerToolBench
GLM-5.3—
Hy4 preview78.6%
Source
Not directly comparable
CWE-bench v1
GLM-5.3—
Hy4 preview53.0%
Source
Not directly comparable

Coding

Terminal-Bench 2.1
GLM-5.388.2%
Source
Hy4 preview85.4%
Source
GLM-5.3 leads this result
terminalBench3
GLM-5.328.3%
Source
Hy4 preview—
Not directly comparable
DeepSWE
GLM-5.366.9%
Source
Hy4 preview64.3%
Source
GLM-5.3 leads this result
NL2Repo
GLM-5.358%
Source
Hy4 preview58.9%
Source
Hy4 preview leads this result
ProgramBench
GLM-5.319.0%
Source
Hy4 preview17.5%
Source
GLM-5.3 leads this result
FrontierSWE
GLM-5.378.1%
Source
Hy4 preview—
Not directly comparable
sweMarathon
GLM-5.342.5%
Source
Hy4 preview31.9%
Source
GLM-5.3 leads this result
PostTrain Bench
GLM-5.339.8%
Source
Hy4 preview35.6%
Source
GLM-5.3 leads this result
VulcanBench v3
GLM-5.378.3%
Source
Hy4 preview—
Not directly comparable
OpenHarmony Bench
GLM-5.360.8%
Source
Hy4 preview—
Not directly comparable
FrontierSWE v2
GLM-5.330.2%
Source
Hy4 preview—
Not directly comparable
LiveCodeBench (Vals)
GLM-5.380.5%
Source
Hy4 preview—
Not directly comparable
SWE-bench (Vals)
GLM-5.395.4%
Source
Hy4 preview—
Not directly comparable
Bug Hunt Bench
GLM-5.319.0 fixes
Source
Hy4 preview—
Not directly comparable
SWE-bench Pro
GLM-5.3—
Hy4 preview65.7%
Source
Not directly comparable
SWE Multilingual
GLM-5.3—
Hy4 preview82.9%
Source
Not directly comparable

Reasoning

CritPt
GLM-5.3—
Hy4 preview16.9%
Source
Not directly comparable

Multimodal

OfficeQA Pro
GLM-5.3—
Hy4 preview66.2%
Source
Not directly comparable

Knowledge

GPQA Diamond (Vals)
GLM-5.388.1%
Source
Hy4 preview—
Not directly comparable
MMLU-Pro (Vals)
GLM-5.386.8%
Source
Hy4 preview—
Not directly comparable
GPQA
GLM-5.3—
Hy4 preview92.3%
Source
Not directly comparable
GPQA-D
GLM-5.3—
Hy4 preview92.3%
Source
Not directly comparable
HLE
GLM-5.3—
Hy4 preview55.4%
Source
Not directly comparable
HLE w/o tools
GLM-5.3—
Hy4 preview43.4%
Source
Not directly comparable

Instruction following

Gray Swan IPI (15 attempts)
GLM-5.331.5%
Source
Hy4 preview—
Not directly comparable

Math

FrontierMath v2 (Tiers 1-3)
GLM-5.368.772%
Source
Hy4 preview—
Not directly comparable
FrontierMath v2 (Tier 4)
GLM-5.329.268%
Source
Hy4 preview—
Not directly comparable
Apex
GLM-5.3—
Hy4 preview74.2%
Source
Not directly comparable

45 public results · 13 shared

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Last updated October 13, 2026

GLM-5.3 vs Hy4 preview

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

Coding

Agentic

Knowledge

Reasoning

Multimodal

Multilingual

Instruction following

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-5.3 or Hy4 preview?

Which is better for coding, GLM-5.3 or Hy4 preview?

Which is better for agentic tasks, GLM-5.3 or Hy4 preview?

Which costs less, GLM-5.3 or Hy4 preview?

Which has the larger context window, GLM-5.3 or Hy4 preview?

Benchmark evidence

Agentic

Coding

Reasoning

Multimodal

Knowledge

Instruction following

Math

Related comparisons

Watch GLM-5.3 vs Hy4 preview