Model A

Anthropic

82.9/100

Supported · Public rank #2

90% interval 78.7–87.1

Claude Opus 5 vs GLM-5.3

Updated August 14, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

Model B

GLM-5.3

Z.AI

—

Evidence status unavailable

90% interval unavailable

Decision reading

At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner.

5 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.

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
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
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.

58 Claude Opus 5 only

5 Shared

11 GLM-5.3 only

Shared results: 5
Claude Opus 5 only: 58
GLM-5.3 only: 11
Like-for-like categories: 0 / 8

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.

Agentic

Not comparable

Claude Opus 5: 90.8
GLM-5.3: Not measured
Weighted basis: 1 vs 0 rows
Reading: Not comparable

Coding

Not comparable

Claude Opus 5: 89.5
GLM-5.3: Not measured
Weighted basis: 2 vs 0 rows
Reading: Not comparable

Reasoning

Not comparable

Claude Opus 5: 90.4
GLM-5.3: Not measured
Weighted basis: 1 vs 0 rows
Reading: Not comparable

Knowledge

Not comparable

Claude Opus 5: 64.7
GLM-5.3: Not measured
Weighted basis: 1 vs 0 rows
Reading: Not comparable

Math

Not comparable

Claude Opus 5: Not measured
GLM-5.3: Not measured
Weighted basis: 0 vs 0 rows
Reading: Not comparable

Multilingual

Not comparable

Claude Opus 5: Not measured
GLM-5.3: Not measured
Weighted basis: 0 vs 0 rows
Reading: Not comparable

Multimodal

Not comparable

Claude Opus 5: 66.9
GLM-5.3: Not measured
Weighted basis: 1 vs 0 rows
Reading: Not comparable

Instruction following

Not comparable

Claude Opus 5: Not measured
GLM-5.3: Not measured
Weighted basis: 0 vs 0 rows
Reading: Not comparable

Category averages with the server-provided evidence basis for Claude Opus 5 and GLM-5.3
Category	Claude Opus 5	GLM-5.3	Weighted basis	Reading
Agentic	90.8	Not measured	Not comparable1 vs 0 rows	Not comparable
Coding	89.5	Not measured	Not comparable2 vs 0 rows	Not comparable
Reasoning	90.4	Not measured	Not comparable1 vs 0 rows	Not comparable
Knowledge	64.7	Not measured	Not comparable1 vs 0 rows	Not comparable
Math	Not measured	Not measured	Not comparable0 vs 0 rows	Not comparable
Multilingual	Not measured	Not measured	Not comparable0 vs 0 rows	Not comparable
Multimodal	66.9	Not measured	Not comparable1 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.

A shared-evidence shape is not available.

BenchLM does not draw a radar or infer missing axes when the matched evidence is too sparse.

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 Opus 5: $0.0175; Fits in one request
GLM-5.3: API rate not published; Fits in one request

GLM-5.3 has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

Claude Opus 5: $0.325; Fits in one request
GLM-5.3: API rate not published; 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 Opus 5: $0.45; Fits in one request
GLM-5.3: API rate not published; 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 Opus 5GLM-5.3

Context window

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

Claude Opus 5

Anthropic model overview

GLM-5.3

Z.AI GLM-5.3 launch post

API model ID

Claude Opus 5

claude-opus-5

Anthropic model overview

GLM-5.3

Not sourced

Cached-input rate

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

Claude Opus 5

$0.5 per 1M cached input tokens

Claude API pricing

GLM-5.3

No comparable hosted API rate

Z.AI GLM-5.3 launch post

Documented inputs

Claude Opus 5

text, image

Anthropic model overview

GLM-5.3

Not sourced

Documented outputs

Claude Opus 5

text

Anthropic model overview

GLM-5.3

Not sourced

Provider availability

Claude Opus 5

Generally Available · Claude API

Anthropic model overview

GLM-5.3

Not sourced

Reasoning profile

Claude Opus 5

Reasoning

GLM-5.3

Reasoning

Weight access

Claude Opus 5

Proprietary

GLM-5.3

Proprietary

License

Claude Opus 5

Proprietary

GLM-5.3

Proprietary

Release date

Claude Opus 5

2026-07-24

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: At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner.
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 evidence74 rows

Agentic

Terminal-Bench 3.0
Claude Opus 542.7%
Source
GLM-5.3—
Not directly comparable
BrowseComp
Claude Opus 590.8%
Source
GLM-5.3—
Not directly comparable
HLE w/ tools
Claude Opus 564.7%
Source
GLM-5.362.5%
Source
Claude Opus 5 leads this result
DeepSearchQA
Claude Opus 595.0%
Source
GLM-5.3—
Not directly comparable
DRACO
Claude Opus 588.6%
Source
GLM-5.3—
Not directly comparable
BrowseComp (10-agent, prerelease)
Claude Opus 593.6%
Source
GLM-5.3—
Not directly comparable
OSWorld 2.0
Claude Opus 570.6%
Source
GLM-5.3—
Not directly comparable
MCP Atlas
Claude Opus 585.8%
Source
GLM-5.3—
Not directly comparable
MCP-Atlas claim coverage
Claude Opus 589.1%
Source
GLM-5.3—
Not directly comparable
LAB all-pass (Anthropic harness)
Claude Opus 523.58%
Source
GLM-5.3—
Not directly comparable
LAB criterion-pass (Anthropic harness)
Claude Opus 593.74%
Source
GLM-5.3—
Not directly comparable
LAB all-pass (Harvey held-out)
Claude Opus 511.7%
Source
GLM-5.3—
Not directly comparable
LAB criterion-pass (Harvey held-out)
Claude Opus 594.1%
Source
GLM-5.3—
Not directly comparable
Toolathlon-Verified
Claude Opus 580.6%
Source
GLM-5.373.0%
Source
Claude Opus 5 leads this result
Toolathlon Verified Pass@3
Claude Opus 587.0%
Source
GLM-5.3—
Not directly comparable
Toolathlon Verified Pass³
Claude Opus 573.1%
Source
GLM-5.3—
Not directly comparable
Toolathlon Verified avg. turns
Claude Opus 523.5 turns
Source
GLM-5.3—
Not directly comparable
AutomationBench
Claude Opus 526.0%
Source
GLM-5.348.2%
Source
GLM-5.3 leads this result
Terminal-Bench 2.1
Claude Opus 5—
GLM-5.388.2%
Source
Not directly comparable
terminalBench3
Claude Opus 5—
GLM-5.328.3%
Source
Not directly comparable
CyberGym
Claude Opus 5—
GLM-5.384.5%
Source
Not directly comparable
ExploitGym
Claude Opus 5—
GLM-5.315.0%
Source
Not directly comparable
Agents' Last Exam
Claude Opus 5—
GLM-5.328.5%
Source
Not directly comparable

Coding

SWE-bench Verified
Claude Opus 596%
Source
GLM-5.3—
Not directly comparable
SWE-bench Pro
Claude Opus 579.2%
Source
GLM-5.3—
Not directly comparable
SWE Multilingual
Claude Opus 589.5%
Source
GLM-5.3—
Not directly comparable
SWE Multimodal
Claude Opus 559.4%
Source
GLM-5.3—
Not directly comparable
deepSwe
Claude Opus 568.8%
Source
GLM-5.366.9%
Source
Claude Opus 5 leads this result
FrontierCode 1.1 Main
Claude Opus 553.4%
Source
GLM-5.3—
Not directly comparable
FrontierCode 1.1 Extended
Claude Opus 563.6%
Source
GLM-5.3—
Not directly comparable
ProgramBench (episode 1)
Claude Opus 583.0%
Source
GLM-5.3—
Not directly comparable
ProgramBench
Claude Opus 593.0%
Source
GLM-5.319.0%
Source
Claude Opus 5 leads this result
cursorBench32
Claude Opus 570.0%
Source
GLM-5.3—
Not directly comparable
VulcanBench v3
Claude Opus 587.0%
Source
GLM-5.3—
Not directly comparable
Terminal-Bench 2.1
Claude Opus 5—
GLM-5.388.2%
Source
Not directly comparable
terminalBench3
Claude Opus 5—
GLM-5.328.3%
Source
Not directly comparable
NL2Repo
Claude Opus 5—
GLM-5.358%
Source
Not directly comparable
FrontierSWE
Claude Opus 5—
GLM-5.378.1%
Source
Not directly comparable
sweMarathon
Claude Opus 5—
GLM-5.342.5%
Source
Not directly comparable
PostTrain Bench
Claude Opus 5—
GLM-5.339.8%
Source
Not directly comparable

Reasoning

ARC-AGI-1
Claude Opus 597.50%
Source
GLM-5.3—
Not directly comparable
ARC-AGI-2
Claude Opus 590.4%
Source
GLM-5.3—
Not directly comparable
ARC-AGI-3
Claude Opus 530.2%
Source
GLM-5.3—
Not directly comparable

Knowledge

HLE
Claude Opus 564.7%
Source
GLM-5.3—
Not directly comparable
HLE w/o tools
Claude Opus 556.3%
Source
GLM-5.3—
Not directly comparable
HealthBench (raw)
Claude Opus 567.1%
Source
GLM-5.3—
Not directly comparable
HealthBench (length-adjusted)
Claude Opus 557.8%
Source
GLM-5.3—
Not directly comparable
HealthBench Professional
Claude Opus 559.8%
Source
GLM-5.3—
Not directly comparable
HealthBench Professional (raw)
Claude Opus 573.4%
Source
GLM-5.3—
Not directly comparable
BioMysteryBench (human-solvable)
Claude Opus 590.1%
Source
GLM-5.3—
Not directly comparable
BioMysteryBench (human-difficult)
Claude Opus 549.4%
Source
GLM-5.3—
Not directly comparable
SpatialBench Verified
Claude Opus 572.5%
Source
GLM-5.3—
Not directly comparable
SingleCellBench
Claude Opus 560.6%
Source
GLM-5.3—
Not directly comparable
ProteinGym Hard
Claude Opus 547.7%
Source
GLM-5.3—
Not directly comparable
Protein Design
Claude Opus 542.5%
Source
GLM-5.3—
Not directly comparable
Organic chemistry V2
Claude Opus 561.6%
Source
GLM-5.3—
Not directly comparable
Protocols (troubleshooting)
Claude Opus 561.1%
Source
GLM-5.3—
Not directly comparable
Protocols (understanding)
Claude Opus 578.4%
Source
GLM-5.3—
Not directly comparable

Math

IMO 2026
Claude Opus 542/42
Source
GLM-5.3—
Not directly comparable
RiemannBench (no tools)
Claude Opus 560.0%
Source
GLM-5.3—
Not directly comparable
RiemannBench (tools)
Claude Opus 579.0%
Source
GLM-5.3—
Not directly comparable
ArXivMath Jun. 2026 (no tools)
Claude Opus 590.8%
Source
GLM-5.3—
Not directly comparable
ArXivMath Jun. 2026 (tools)
Claude Opus 591.3%
Source
GLM-5.3—
Not directly comparable

Multilingual

GMMLU
Claude Opus 592.5%
Source
GLM-5.3—
Not directly comparable
MILU
Claude Opus 592.1%
Source
GLM-5.3—
Not directly comparable
INCLUDE
Claude Opus 589.8%
Source
GLM-5.3—
Not directly comparable

Multimodal

Chartography (no tools)
Claude Opus 529.6%
Source
GLM-5.3—
Not directly comparable
Chartography (tools)
Claude Opus 583.0%
Source
GLM-5.3—
Not directly comparable
BenchCAD Vision2Code (no tools)
Claude Opus 50.366
Source
GLM-5.3—
Not directly comparable
BenchCAD Vision2Code (tools)
Claude Opus 50.821
Source
GLM-5.3—
Not directly comparable
GDP.pdf (no tools)
Claude Opus 583.4%
Source
GLM-5.3—
Not directly comparable
GDP.pdf (tools)
Claude Opus 585.5%
Source
GLM-5.3—
Not directly comparable
OfficeQA
Claude Opus 578.1%
Source
GLM-5.3—
Not directly comparable
OfficeQA Pro
Claude Opus 566.9%
Source
GLM-5.3—
Not directly comparable

Frequently asked questions

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

At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner. The page therefore keeps the decision tied to the specific documented workload.

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

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, Claude Opus 5 or GLM-5.3?

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, Claude Opus 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 Opus 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 August 14, 2026

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Claude Opus 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

Reasoning

Knowledge

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

Multilingual

Multimodal

Frequently asked questions

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

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

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

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

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

Related comparisons

Watch Claude Opus 5 vs GLM-5.3