Claude Opus 4.6 vs GLM-5.3

Updated September 28, 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 score estimate, 65.44 versus 63.61, but the 90% score intervals overlap. Treat that as a lead, not a settled winner. 1 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

Model A

Claude Opus 4.6

Anthropic

63.61/100

Supported · Public rank #36

90% interval 51.6–75.7

Model B

GLM-5.3

Z.AI

65.44/100

Estimated · Public rank #29

90% interval 59.7–71.2

Shared results: 1
Claude Opus 4.6 only: 33
GLM-5.3 only: 23
Like-for-like categories: 2 / 8

Supported: Claude Opus 4.6 · Estimated: GLM-5.3How 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.

Coding work
Code generation, repair, and software-engineering tasks
GLM-5.3
GLM-5.3 leads on the public coding lane, 56.8 to 49.5, 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, 67.4 to 44.4, with Supported evidence for both models and non-overlapping 90% intervals.
Confidence: stronger

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: 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.

49.5Claude Opus 4.656.8GLM-5.3

Like-for-like · BenchAlign v5.7

GLM-5.3 leads the like-for-like coding row, although the 90% intervals overlap.

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.

A shared-evidence shape is not available.

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

Category results, on a stated basis

Each row shows the public-lane category score for both models: the BenchAlign v5.7 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 Opus 4.6: 44.4; Supported · #44/117
GLM-5.3: 67.4; Supported · #9/117
Basis: BenchAlign v5.7 lane · 10 vs 9 public rows
Reading: GLM-5.3 leads

Coding

Like-for-like

Claude Opus 4.6: 49.5; Supported · #41/142
GLM-5.3: 56.8; Supported · #24/142
Basis: BenchAlign v5.7 lane · 8 vs 13 public rows
Reading: GLM-5.3 leads · intervals overlap

Knowledge

Directional only

Claude Opus 4.6: 58.7; Estimated · #42/168
GLM-5.3: 61.9; Supported · #35/168
Basis: BenchAlign v5.7 lane · 9 vs 2 public rows
Reading: Directional only

Reasoning

Not comparable

Claude Opus 4.6: 68.3; Unranked · 2 rankable rows
GLM-5.3: 77.1; #11/27
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Multimodal

Not comparable

Claude Opus 4.6: 60.6; #30/50
GLM-5.3: Not ranked
Basis: Provisional lane · 1 vs 0 weighted rows
Reading: Not comparable

Multilingual

Not comparable

Claude Opus 4.6: Not ranked
GLM-5.3: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Instruction following

Not comparable

Claude Opus 4.6: 51.0; #79/124
GLM-5.3: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Math

Not comparable

Claude Opus 4.6: 58.5; Unranked · 3 rankable rows
GLM-5.3: Not ranked
Basis: Provisional lane · 2 vs 0 weighted rows
Reading: Not comparable

Category scores with the server-provided evidence basis for Claude Opus 4.6 and GLM-5.3
Category	Claude Opus 4.6	GLM-5.3	Basis	Reading
Agentic	44.4Supported · #44/117	67.4Supported · #9/117	Like-for-likeBenchAlign v5.7 lane · 10 vs 9 public rows	GLM-5.3 leads
Coding	49.5Supported · #41/142	56.8Supported · #24/142	Like-for-likeBenchAlign v5.7 lane · 8 vs 13 public rows	GLM-5.3 leads · intervals overlap
Knowledge	58.7Estimated · #42/168	61.9Supported · #35/168	Directional onlyBenchAlign v5.7 lane · 9 vs 2 public rows	Directional only
Reasoning	68.3Unranked · 2 rankable rows	77.1#11/27	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Multimodal	60.6#30/50	Not ranked	Not comparableProvisional lane · 1 vs 0 weighted rows	Not comparable
Multilingual	Not ranked	Not ranked	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Instruction following	51.0#79/124	Not ranked	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Math	58.5Unranked · 3 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 v5.7) 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

Claude Opus 4.6: $0.0175; 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 Opus 4.6: $0.325; 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 Opus 4.6: $1.35; Fits in one request; Cached input priced at the published list-input rate
GLM-5.3: Self-hosted; infrastructure cost varies; Fits in one request; Cached-input rate unavailable

Claude Opus 4.6 has no published cached-input rate, so cached tokens use its listed input rate. GLM-5.3 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.

SpecificationClaude Opus 4.6GLM-5.3

Context window

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

Claude Opus 4.6

GLM-5.3

Z.AI GLM-5.3 model card

API model ID

Claude Opus 4.6

Not sourced

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 Opus 4.6

Not published

GLM-5.3

No comparable hosted API rate

Z.AI GLM-5.3 model card

Documented inputs

Claude Opus 4.6

Not sourced

GLM-5.3

Not sourced

Documented outputs

Claude Opus 4.6

Not sourced

GLM-5.3

Not sourced

Provider availability

Claude Opus 4.6

Not sourced

GLM-5.3

Not sourced

Reasoning profile

Claude Opus 4.6

Non-Reasoning

GLM-5.3

Reasoning

Weight access

Claude Opus 4.6

Proprietary

GLM-5.3

Open Weight

License

Claude Opus 4.6

Proprietary

GLM-5.3

Open Weight

Release date

Claude Opus 4.6

2026-02-01

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: GLM-5.3 has the higher public score estimate, 65.44 versus 63.61, 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.

Questions

Which is better, Claude Opus 4.6 or GLM-5.3?

GLM-5.3 has the higher public score estimate, 65.44 versus 63.61, 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 Opus 4.6 or GLM-5.3?

GLM-5.3 leads the public coding lane, 56.8 to 49.5, with Supported evidence for both models, although the 90% intervals overlap.

Which is better for agentic tasks, Claude Opus 4.6 or GLM-5.3?

GLM-5.3 leads the public agentic tasks lane, 67.4 to 44.4, with Supported evidence for both models and non-overlapping 90% intervals.

Which costs less, Claude Opus 4.6 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 4.6 or GLM-5.3?

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 evidence57 rows

Agentic

Terminal-Bench 2.0
Claude Opus 4.665.4%
Source
GLM-5.3—
Not directly comparable
BrowseComp
Claude Opus 4.683.7%
Source
GLM-5.3—
Not directly comparable
OSWorld-Verified
Claude Opus 4.672.7%
Source
GLM-5.3—
Not directly comparable
Claw-Eval
Claude Opus 4.670.4%
Source
GLM-5.3—
Not directly comparable
DeepSearchQA
Claude Opus 4.673.7%
Source
GLM-5.3—
Not directly comparable
CyberGym
Claude Opus 4.666.6%
Source
GLM-5.384.5%
Source
GLM-5.3 leads this result
Gert Labs
Claude Opus 4.661.85%
Source
GLM-5.3—
Not directly comparable
ResearchClawBench
Claude Opus 4.619.9%
Source
GLM-5.3—
Not directly comparable
JobBench
Claude Opus 4.636.7%
Source
GLM-5.3—
Not directly comparable
ApprenticeBench
Claude Opus 4.65%
Source
GLM-5.3—
Not directly comparable
Terminal-Bench 2.1
Claude Opus 4.6—
GLM-5.388.2%
Source
Not directly comparable
terminalBench3
Claude Opus 4.6—
GLM-5.328.3%
Source
Not directly comparable
ExploitGym
Claude Opus 4.6—
GLM-5.315.0%
Source
Not directly comparable
Toolathlon-Verified
Claude Opus 4.6—
GLM-5.373.0%
Source
Not directly comparable
AutomationBench
Claude Opus 4.6—
GLM-5.348.2%
Source
Not directly comparable
Agents' Last Exam
Claude Opus 4.6—
GLM-5.328.5%
Source
Not directly comparable
HLE w/ tools
Claude Opus 4.6—
GLM-5.362.5%
Source
Not directly comparable
Terminal-Bench 2.1 (Vals)
Claude Opus 4.6—
GLM-5.371.5%
Source
Not directly comparable

Coding

SWE-bench Verified
Claude Opus 4.680.8%
Source
GLM-5.3—
Not directly comparable
SWE-bench Verified*
Claude Opus 4.675.6%
Source
GLM-5.3—
Not directly comparable
LiveCodeBench Pro
Claude Opus 4.670.7%
Source
GLM-5.3—
Not directly comparable
SWE-bench Pro
Claude Opus 4.653.4%
Source
GLM-5.3—
Not directly comparable
SWE-Rebench
Claude Opus 4.665.3%
Source
GLM-5.3—
Not directly comparable
React Native Evals
Claude Opus 4.684.1%
Source
GLM-5.3—
Not directly comparable
Vibe Code Bench
Claude Opus 4.657.57%
Source
GLM-5.3—
Not directly comparable
FrontierCode 1.1 Main
Claude Opus 4.626.9%
Source
GLM-5.3—
Not directly comparable
Terminal-Bench 2.1
Claude Opus 4.6—
GLM-5.388.2%
Source
Not directly comparable
terminalBench3
Claude Opus 4.6—
GLM-5.328.3%
Source
Not directly comparable
DeepSWE
Claude Opus 4.6—
GLM-5.366.9%
Source
Not directly comparable
NL2Repo
Claude Opus 4.6—
GLM-5.358%
Source
Not directly comparable
ProgramBench
Claude Opus 4.6—
GLM-5.319.0%
Source
Not directly comparable
FrontierSWE
Claude Opus 4.6—
GLM-5.378.1%
Source
Not directly comparable
sweMarathon
Claude Opus 4.6—
GLM-5.342.5%
Source
Not directly comparable
PostTrain Bench
Claude Opus 4.6—
GLM-5.339.8%
Source
Not directly comparable
VulcanBench v3
Claude Opus 4.6—
GLM-5.378.3%
Source
Not directly comparable
OpenHarmony Bench
Claude Opus 4.6—
GLM-5.360.8%
Source
Not directly comparable
FrontierSWE v2
Claude Opus 4.6—
GLM-5.330.2%
Source
Not directly comparable
LiveCodeBench (Vals)
Claude Opus 4.6—
GLM-5.380.5%
Source
Not directly comparable
SWE-bench (Vals)
Claude Opus 4.6—
GLM-5.395.4%
Source
Not directly comparable

Multimodal

MMMU-Pro
Claude Opus 4.677.3%
Source
GLM-5.3—
Not directly comparable
ERQA
Claude Opus 4.651.6%
Source
GLM-5.3—
Not directly comparable
ScreenSpot Pro
Claude Opus 4.683.1%
Source
GLM-5.3—
Not directly comparable
MedXpertQA (MM)
Claude Opus 4.664.8%
Source
GLM-5.3—
Not directly comparable

Knowledge

GPQA
Claude Opus 4.691.3%
Source
GLM-5.3—
Not directly comparable
GPQA-D
Claude Opus 4.689.2%
Source
GLM-5.3—
Not directly comparable
SuperGPQA
Claude Opus 4.695%
Source
GLM-5.3—
Not directly comparable
MMLU-Pro
Claude Opus 4.682%
Source
GLM-5.3—
Not directly comparable
MMLU-Pro (Arcee)
Claude Opus 4.689.1%
Source
GLM-5.3—
Not directly comparable
HLE
Claude Opus 4.653%
Source
GLM-5.3—
Not directly comparable
HLE w/o tools
Claude Opus 4.640%
Source
GLM-5.3—
Not directly comparable
HealthBench Hard
Claude Opus 4.614.8%
Source
GLM-5.3—
Not directly comparable
MedXpertQA (Text)
Claude Opus 4.652.1%
Source
GLM-5.3—
Not directly comparable
GPQA Diamond (Vals)
Claude Opus 4.6—
GLM-5.388.1%
Source
Not directly comparable
MMLU-Pro (Vals)
Claude Opus 4.6—
GLM-5.386.8%
Source
Not directly comparable

Math

AIME25 (Arcee)
Claude Opus 4.699.8%
Source
GLM-5.3—
Not directly comparable
FrontierMath v2 (Tiers 1-3)
Claude Opus 4.640.700%
Source
GLM-5.3—
Not directly comparable
FrontierMath v2 (Tier 4)
Claude Opus 4.622.900%
Source
GLM-5.3—
Not directly comparable

57 public results · 1 shared

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Last updated September 28, 2026

Claude Opus 4.6 vs GLM-5.3

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

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, Claude Opus 4.6 or GLM-5.3?

Which is better for coding, Claude Opus 4.6 or GLM-5.3?

Which is better for agentic tasks, Claude Opus 4.6 or GLM-5.3?

Which costs less, Claude Opus 4.6 or GLM-5.3?

Which has the larger context window, Claude Opus 4.6 or GLM-5.3?

Benchmark evidence

Agentic

Coding

Multimodal

Knowledge

Math

Related comparisons

Watch Claude Opus 4.6 vs GLM-5.3