Model A

Anthropic

69.8/100

Supported · Public rank #20

90% interval 66.5–73.0

Claude Sonnet 5 vs GLM-5.3

Updated September 8, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

Model B

GLM-5.3

Z.AI

68.34/100

Estimated · Public rank #26

90% interval 60.7–76.0

Decision reading

Claude Sonnet 5 has the higher public score estimate, 69.8 versus 68.34, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.

6 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.

Coding work
Code generation, repair, and software-engineering tasks
Claude Sonnet 5
Claude Sonnet 5 leads on the public coding lane, 64.1 to 61.5, with Supported evidence for both models, although the 90% intervals overlap.
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
GLM-5.3
GLM-5.3 leads on the public agentic lane, 68.4 to 65.8, with Supported evidence for both models, although the 90% intervals overlap.
Confidence: limited

Show secondary and unsupported calls

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.

18 Claude Sonnet 5 only

6 Shared

18 GLM-5.3 only

Shared results: 6
Claude Sonnet 5 only: 18
GLM-5.3 only: 18
Like-for-like categories: 3 / 8

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

Claude Sonnet 5: 65.8; Supported · #11/152
GLM-5.3: 68.4; Supported · #8/152
Basis: BenchAlign lane · 6 vs 9 public rows
Reading: GLM-5.3 leads · intervals overlap

Coding

Like-for-like

Claude Sonnet 5: 64.1; Supported · #13/151
GLM-5.3: 61.5; Supported · #18/151
Basis: BenchAlign lane · 10 vs 13 public rows
Reading: Claude Sonnet 5 leads · intervals overlap

Knowledge

Like-for-like

Claude Sonnet 5: 66.7; Supported · #20/182
GLM-5.3: 61.9; Supported · #28/182
Basis: BenchAlign lane · 6 vs 2 public rows
Reading: Claude Sonnet 5 leads · intervals overlap

Reasoning

Not comparable

Claude Sonnet 5: 77.4; Unranked · 2 rankable rows
GLM-5.3: 75.8; #8/18
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Math

Not comparable

Claude Sonnet 5: Not ranked
GLM-5.3: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Multilingual

Not comparable

Claude Sonnet 5: Not ranked
GLM-5.3: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Multimodal

Not comparable

Claude Sonnet 5: 77.5; #13/48
GLM-5.3: Not ranked
Basis: Provisional lane · 1 vs 0 weighted rows
Reading: Not comparable

Instruction following

Not comparable

Claude Sonnet 5: Not ranked
GLM-5.3: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Category scores with the server-provided evidence basis for Claude Sonnet 5 and GLM-5.3
Category	Claude Sonnet 5	GLM-5.3	Basis	Reading
Agentic	65.8Supported · #11/152	68.4Supported · #8/152	Like-for-likeBenchAlign lane · 6 vs 9 public rows	GLM-5.3 leads · intervals overlap
Coding	64.1Supported · #13/151	61.5Supported · #18/151	Like-for-likeBenchAlign lane · 10 vs 13 public rows	Claude Sonnet 5 leads · intervals overlap
Knowledge	66.7Supported · #20/182	61.9Supported · #28/182	Like-for-likeBenchAlign lane · 6 vs 2 public rows	Claude Sonnet 5 leads · intervals overlap
Reasoning	77.4Unranked · 2 rankable rows	75.8#8/18	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Math	Not ranked	Not ranked	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Multilingual	Not ranked	Not ranked	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Multimodal	77.5#13/48	Not ranked	Not comparableProvisional lane · 1 vs 0 weighted rows	Not comparable
Instruction following	Not ranked	Not ranked	Not comparableProvisional lane · 0 vs 0 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.

LiveCodeBench (Vals)
Coding
Claude Sonnet 5: 82.4%GLM-5.3: 80.5%Normalized gap 1.9Claude Sonnet 5 source GLM-5.3 source
MMLU-Pro (Vals)
Knowledge
Claude Sonnet 5: 87.5%GLM-5.3: 86.8%Normalized gap 0.7Claude Sonnet 5 source GLM-5.3 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

Claude Sonnet 5: $0.007; Fits in one request
GLM-5.3: Self-hosted; infrastructure cost varies; Fits in one request

GLM-5.3 has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

Claude Sonnet 5: $0.13; Fits in one request
GLM-5.3: Self-hosted; infrastructure cost varies; Fits in one request

GLM-5.3 has no comparable published API token rate.

Cache-heavy agent loop

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

Claude Sonnet 5: $0.18; Fits in one request
GLM-5.3: Self-hosted; infrastructure cost varies; Fits in one request; Cached-input rate unavailable

GLM-5.3 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.

SpecificationClaude Sonnet 5GLM-5.3

Context window

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

Claude Sonnet 5

Anthropic model overview

GLM-5.3

Z.AI GLM-5.3 model card

API model ID

Claude Sonnet 5

claude-sonnet-5

Anthropic model overview

GLM-5.3

zai-org/GLM-5.3

Z.AI GLM-5.3 model card

Cached-input rate

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

Claude Sonnet 5

$0.2 per 1M cached input tokens

Claude API pricing

GLM-5.3

No comparable hosted API rate

Z.AI GLM-5.3 model card

Documented inputs

Claude Sonnet 5

text, image

Anthropic model overview

GLM-5.3

Not sourced

Documented outputs

Claude Sonnet 5

text

Anthropic model overview

GLM-5.3

Not sourced

Provider availability

Claude Sonnet 5

Generally Available · Claude API

Anthropic model overview

GLM-5.3

Not sourced

Reasoning profile

Claude Sonnet 5

Reasoning

GLM-5.3

Reasoning

Weight access

Claude Sonnet 5

Proprietary

GLM-5.3

Open Weight

License

Claude Sonnet 5

Proprietary

GLM-5.3

Open Weight

Release date

Claude Sonnet 5

2026-06-30

GLM-5.3

2026-08-14

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: Claude Sonnet 5 has the higher public score estimate, 69.8 versus 68.34, 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 evidence42 rows

Agentic

Terminal-Bench 3.0
Claude Sonnet 514.6%
Source
GLM-5.3—
Not directly comparable
Terminal-Bench 2.0
Claude Sonnet 580.4%
Source
GLM-5.3—
Not directly comparable
BrowseComp
Claude Sonnet 584.7%
Source
GLM-5.3—
Not directly comparable
HLE w/ tools
Claude Sonnet 557.4%
Source
GLM-5.362.5%
Source
GLM-5.3 leads this result
OSWorld-Verified
Claude Sonnet 581.2%
Source
GLM-5.3—
Not directly comparable
Terminal-Bench 2.1 (Vals)
Claude Sonnet 574.5%
Source
GLM-5.371.5%
Source
Claude Sonnet 5 leads this result
Terminal-Bench 2.1
Claude Sonnet 5—
GLM-5.388.2%
Source
Not directly comparable
terminalBench3
Claude Sonnet 5—
GLM-5.328.3%
Source
Not directly comparable
CyberGym
Claude Sonnet 5—
GLM-5.384.5%
Source
Not directly comparable
ExploitGym
Claude Sonnet 5—
GLM-5.315.0%
Source
Not directly comparable
Toolathlon-Verified
Claude Sonnet 5—
GLM-5.373.0%
Source
Not directly comparable
AutomationBench
Claude Sonnet 5—
GLM-5.348.2%
Source
Not directly comparable
Agents' Last Exam
Claude Sonnet 5—
GLM-5.328.5%
Source
Not directly comparable

Coding

SWE-bench Verified
Claude Sonnet 585.2%
Source
GLM-5.3—
Not directly comparable
SWE-bench Pro
Claude Sonnet 563.2%
Source
GLM-5.3—
Not directly comparable
SWE Multilingual
Claude Sonnet 578.3%
Source
GLM-5.3—
Not directly comparable
SWE Multimodal
Claude Sonnet 528.1%
Source
GLM-5.3—
Not directly comparable
Terminal-Bench 2.0
Claude Sonnet 580.4%
Source
GLM-5.3—
Not directly comparable
FrontierCode 1.1 Main
Claude Sonnet 542.7%
Source
GLM-5.3—
Not directly comparable
cursorBench32
Claude Sonnet 561.5%
Source
GLM-5.3—
Not directly comparable
VulcanBench CII v1
Claude Sonnet 589.2%
Source
GLM-5.3—
Not directly comparable
LiveCodeBench (Vals)
Claude Sonnet 582.4%
Source
GLM-5.380.5%
Source
Claude Sonnet 5 leads this result
SWE-bench (Vals)
Claude Sonnet 579.6%
Source
GLM-5.395.4%
Source
GLM-5.3 leads this result
Terminal-Bench 2.1
Claude Sonnet 5—
GLM-5.388.2%
Source
Not directly comparable
terminalBench3
Claude Sonnet 5—
GLM-5.328.3%
Source
Not directly comparable
deepSwe
Claude Sonnet 5—
GLM-5.366.9%
Source
Not directly comparable
NL2Repo
Claude Sonnet 5—
GLM-5.358%
Source
Not directly comparable
ProgramBench
Claude Sonnet 5—
GLM-5.319.0%
Source
Not directly comparable
FrontierSWE
Claude Sonnet 5—
GLM-5.378.1%
Source
Not directly comparable
sweMarathon
Claude Sonnet 5—
GLM-5.342.5%
Source
Not directly comparable
PostTrain Bench
Claude Sonnet 5—
GLM-5.339.8%
Source
Not directly comparable
VulcanBench v3
Claude Sonnet 5—
GLM-5.378.3%
Source
Not directly comparable
OpenHarmony Bench
Claude Sonnet 5—
GLM-5.360.8%
Source
Not directly comparable
FrontierSWE v2
Claude Sonnet 5—
GLM-5.330.2%
Source
Not directly comparable

Knowledge

HLE
Claude Sonnet 557.4%
Source
GLM-5.3—
Not directly comparable
HLE w/o tools
Claude Sonnet 543.2%
Source
GLM-5.3—
Not directly comparable
HLE-Verified
Claude Sonnet 531.0%
Source
GLM-5.3—
Not directly comparable
LABBench2
Claude Sonnet 580.1%
Source
GLM-5.3—
Not directly comparable
GPQA Diamond (Vals)
Claude Sonnet 588.9%
Source
GLM-5.388.1%
Source
Claude Sonnet 5 leads this result
MMLU-Pro (Vals)
Claude Sonnet 587.5%
Source
GLM-5.386.8%
Source
Claude Sonnet 5 leads this result

Multimodal

CharXiv
Claude Sonnet 588.3%
Source
GLM-5.3—
Not directly comparable
CharXiv w/o tools
Claude Sonnet 577%
Source
GLM-5.3—
Not directly comparable

Frequently asked questions

Which is better, Claude Sonnet 5 or GLM-5.3?

Claude Sonnet 5 has the higher public score estimate, 69.8 versus 68.34, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.

Which is better for coding, Claude Sonnet 5 or GLM-5.3?

Claude Sonnet 5 leads the public coding lane, 64.1 to 61.5, with Supported evidence for both models, although the 90% intervals overlap.

Which is better for agentic tasks, Claude Sonnet 5 or GLM-5.3?

GLM-5.3 leads the public agentic tasks lane, 68.4 to 65.8, with Supported evidence for both models, although the 90% intervals overlap.

Which costs less, Claude Sonnet 5 or GLM-5.3?

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, Claude Sonnet 5 or GLM-5.3?

Both models list the same context window, 1M.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated September 8, 2026

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Claude Sonnet 5 vs GLM-5.3

Which one for your work

Coding work

Agentic 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

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

Knowledge

Multimodal

Frequently asked questions

Which is better, Claude Sonnet 5 or GLM-5.3?

Which is better for coding, Claude Sonnet 5 or GLM-5.3?

Which is better for agentic tasks, Claude Sonnet 5 or GLM-5.3?

Which costs less, Claude Sonnet 5 or GLM-5.3?

Which has the larger context window, Claude Sonnet 5 or GLM-5.3?

Related comparisons

Watch Claude Sonnet 5 vs GLM-5.3