Claude Opus 5 vs GLM-5.3-Flash

Updated October 10, 2026. Rank says Claude Opus 5 is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.

Decision reading

Claude Opus 5 has the higher public point estimate, 79.44 versus 57.36. Their conditional score ranges do not 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

Claude Opus 5

Anthropic

79.44/100

Supported · Public rank #7

90% interval 76.0–82.9

Model B

GLM-5.3-Flash

Z.AI

57.36/100

Estimated · Public rank #55

Conditional range 47.6–67.1

Shared results: 13
Claude Opus 5 only: 64
GLM-5.3-Flash only: 8
Like-for-like categories: 3 / 8

Supported: Claude Opus 5 · Estimated: GLM-5.3-Flash. 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.

Coding work
Code generation, repair, and software-engineering tasks
Claude Opus 5
Claude Opus 5 has the higher public coding point estimate, 71.1 to 49.3, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
Claude Opus 5
Claude Opus 5 has the higher public agentic point estimate, 77.7 to 55.7, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
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

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.

71.1Claude Opus 549.3GLM-5.3-Flash

Like-for-like · BenchAlign v5.8

Claude Opus 5 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 22.8
Claude Opus 5:26.0%
GLM-5.3-Flash:48.8%
Claude Opus 5 source GLM-5.3-Flash source
LiveCodeBench (Vals)Coding
Normalized gap 8.5
Claude Opus 5:89.0%
GLM-5.3-Flash:80.5%
Claude Opus 5 source GLM-5.3-Flash source
MMLU-Pro (Vals)Knowledge
Normalized gap 5.5
Claude Opus 5:91.6%
GLM-5.3-Flash:86.1%
Claude Opus 5 source GLM-5.3-Flash source
OfficeQA ProMultimodal
Normalized gap 4.5
Claude Opus 5:66.9%
GLM-5.3-Flash:62.4%
Claude Opus 5 source GLM-5.3-Flash 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.

Agentic

Like-for-like

Claude Opus 5: 77.7; Supported · #3/123
GLM-5.3-Flash: 55.7; Supported · #34/123
Basis: BenchAlign v5.8 lane · 20 vs 6 public rows
Reading: Claude Opus 5 leads

Coding

Like-for-like

Claude Opus 5: 71.1; Supported · #7/146
GLM-5.3-Flash: 49.3; Supported · #42/146
Basis: BenchAlign v5.8 lane · 18 vs 8 public rows
Reading: Claude Opus 5 leads

Knowledge

Like-for-like

Claude Opus 5: 81.7; Supported · #7/177
GLM-5.3-Flash: 60.5; Supported · #43/177
Basis: BenchAlign v5.8 lane · 19 vs 2 public rows
Reading: Claude Opus 5 leads

Reasoning

Directional only

Claude Opus 5: 81.1; #10/28
GLM-5.3-Flash: 81.1; #11/28
Basis: Provisional lane · 2 vs 0 weighted rows
Reading: Directional only

Multimodal

Directional only

Claude Opus 5: 92.2; #2/54
GLM-5.3-Flash: 84.0; #14/54
Basis: Provisional lane · 1 vs 2 weighted rows
Reading: Directional only

Multilingual

Not comparable

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

Instruction following

Not comparable

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

Math

Not comparable

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

Category scores with the server-provided evidence basis for Claude Opus 5 and GLM-5.3-Flash
Category	Claude Opus 5	GLM-5.3-Flash	Basis	Reading
Agentic	77.7Supported · #3/123	55.7Supported · #34/123	Like-for-likeBenchAlign v5.8 lane · 20 vs 6 public rows	Claude Opus 5 leads
Coding	71.1Supported · #7/146	49.3Supported · #42/146	Like-for-likeBenchAlign v5.8 lane · 18 vs 8 public rows	Claude Opus 5 leads
Knowledge	81.7Supported · #7/177	60.5Supported · #43/177	Like-for-likeBenchAlign v5.8 lane · 19 vs 2 public rows	Claude Opus 5 leads
Reasoning	81.1#10/28	81.1#11/28	Directional onlyProvisional lane · 2 vs 0 weighted rows	Directional only
Multimodal	92.2#2/54	84.0#14/54	Directional onlyProvisional lane · 1 vs 2 weighted rows	Directional only
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	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 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

Claude Opus 5: $0.0175; Fits in one request
GLM-5.3-Flash: Self-hosted; infrastructure cost varies; Fits in one request

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

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

GLM-5.3-Flash 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 5GLM-5.3-Flash

Context window

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

Claude Opus 5

Anthropic model overview

GLM-5.3-Flash

GLM-5.3-Flash model card

API model ID

Claude Opus 5

claude-opus-5

Anthropic model overview

GLM-5.3-Flash

glm-5.3-flash

GLM-5.3-Flash 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 5

$0.5 per 1M cached input tokens

Claude API pricing

GLM-5.3-Flash

No comparable hosted API rate

GLM-5.3-Flash model card

Documented inputs

Claude Opus 5

text, image

Anthropic model overview

GLM-5.3-Flash

Not sourced

Documented outputs

Claude Opus 5

text

Anthropic model overview

GLM-5.3-Flash

Not sourced

Provider availability

Claude Opus 5

Generally Available · Claude API

Anthropic model overview

GLM-5.3-Flash

Not sourced

Reasoning profile

Claude Opus 5

Reasoning

GLM-5.3-Flash

Reasoning

Weight access

Claude Opus 5

Proprietary

GLM-5.3-Flash

Open Weight

License

Claude Opus 5

Proprietary

GLM-5.3-Flash

Open Weight

Release date

Claude Opus 5

2026-07-24

GLM-5.3-Flash

2026-08-26

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 Opus 5 has the higher public point estimate, 79.44 versus 57.36. Their conditional score ranges do not 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, Claude Opus 5 or GLM-5.3-Flash?

Claude Opus 5 has the higher public point estimate, 79.44 versus 57.36. Their conditional score ranges do not 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, Claude Opus 5 or GLM-5.3-Flash?

Claude Opus 5 has the higher public coding point estimate, 71.1 to 49.3, with Supported evidence for both models. The conditional ranges do not establish rank confidence.

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

Claude Opus 5 has the higher public agentic tasks point estimate, 77.7 to 55.7, with Supported evidence for both models. The conditional ranges do not establish rank confidence.

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

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-Flash?

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 evidence85 rows

Agentic

Terminal-Bench 3.0
Claude Opus 542.7%
Source
GLM-5.3-Flash—
Not directly comparable
BrowseComp
Claude Opus 590.8%
Source
GLM-5.3-Flash—
Not directly comparable
HLE w/ tools
Claude Opus 564.7%
Source
GLM-5.3-Flash55.3%
Source
Claude Opus 5 leads this result
DeepSearchQA
Claude Opus 595.0%
Source
GLM-5.3-Flash—
Not directly comparable
DRACO
Claude Opus 588.6%
Source
GLM-5.3-Flash—
Not directly comparable
BrowseComp (10-agent, prerelease)
Claude Opus 593.6%
Source
GLM-5.3-Flash—
Not directly comparable
OSWorld 2.0
Claude Opus 570.6%
Source
GLM-5.3-Flash—
Not directly comparable
MCP Atlas
Claude Opus 585.8%
Source
GLM-5.3-Flash—
Not directly comparable
MCP-Atlas claim coverage
Claude Opus 589.1%
Source
GLM-5.3-Flash—
Not directly comparable
LAB all-pass (Anthropic harness)
Claude Opus 523.58%
Source
GLM-5.3-Flash—
Not directly comparable
LAB criterion-pass (Anthropic harness)
Claude Opus 593.74%
Source
GLM-5.3-Flash—
Not directly comparable
LAB all-pass (Harvey held-out)
Claude Opus 511.7%
Source
GLM-5.3-Flash—
Not directly comparable
LAB criterion-pass (Harvey held-out)
Claude Opus 594.1%
Source
GLM-5.3-Flash—
Not directly comparable
Toolathlon-Verified
Claude Opus 580.6%
Source
GLM-5.3-Flash78.4%
Source
Claude Opus 5 leads this result
Toolathlon Verified Pass@3
Claude Opus 587.0%
Source
GLM-5.3-Flash—
Not directly comparable
Toolathlon Verified Pass³
Claude Opus 573.1%
Source
GLM-5.3-Flash—
Not directly comparable
Toolathlon Verified avg. turns
Claude Opus 523.5 turns
Source
GLM-5.3-Flash—
Not directly comparable
AutomationBench
Claude Opus 526.0%
Source
GLM-5.3-Flash48.8%
Source
GLM-5.3-Flash leads this result
Terminal-Bench 2.1 (Vals)
Claude Opus 584.6%
Source
GLM-5.3-Flash62.9%
Source
Claude Opus 5 leads this result
ApprenticeBench
Claude Opus 536%
Source
GLM-5.3-Flash—
Not directly comparable
Terminal-Bench 2.1
Claude Opus 5—
GLM-5.3-Flash84.3%
Source
Not directly comparable
Agents' Last Exam
Claude Opus 5—
GLM-5.3-Flash26.3%
Source
Not directly comparable

Coding

Bug Hunt Bench
Shared source
Claude Opus 527.0 fixes
GLM-5.3-Flash17.7 fixes
Claude Opus 5 leads this result
SWE-bench Verified
Claude Opus 596%
Source
GLM-5.3-Flash—
Not directly comparable
SWE-bench Pro
Claude Opus 579.2%
Source
GLM-5.3-Flash—
Not directly comparable
SWE Multilingual
Claude Opus 589.5%
Source
GLM-5.3-Flash—
Not directly comparable
SWE Multimodal
Claude Opus 559.4%
Source
GLM-5.3-Flash—
Not directly comparable
DeepSWE
Claude Opus 568.8%
Source
GLM-5.3-Flash63.4%
Source
Claude Opus 5 leads this result
FrontierCode 1.1 Main
Claude Opus 553.4%
Source
GLM-5.3-Flash—
Not directly comparable
FrontierCode 1.1 Extended
Claude Opus 563.6%
Source
GLM-5.3-Flash—
Not directly comparable
FrontierSWE v2
Shared source
Claude Opus 552.0%
GLM-5.3-Flash18.1%
Claude Opus 5 leads this result
ProgramBench (episode 1)
Claude Opus 583.0%
Source
GLM-5.3-Flash—
Not directly comparable
ProgramBench
Claude Opus 593.0%
Source
GLM-5.3-Flash—
Not directly comparable
CursorBench 3.2
Claude Opus 570.0%
Source
GLM-5.3-Flash—
Not directly comparable
VulcanBench v3
Claude Opus 587.0%
Source
GLM-5.3-Flash—
Not directly comparable
VulcanBench CII v1
Claude Opus 596.4%
Source
GLM-5.3-Flash—
Not directly comparable
LiveCodeBench (Vals)
Claude Opus 589.0%
Source
GLM-5.3-Flash80.5%
Source
Claude Opus 5 leads this result
SWE-bench (Vals)
Claude Opus 597.0%
Source
GLM-5.3-Flash92.0%
Source
Claude Opus 5 leads this result
CursorBench 4.0
Claude Opus 546.6%
Source
GLM-5.3-Flash—
Not directly comparable
PostTrainBench v1.1
Claude Opus 535.0%
Source
GLM-5.3-Flash—
Not directly comparable
Terminal-Bench 2.1
Claude Opus 5—
GLM-5.3-Flash84.3%
Source
Not directly comparable
NL2Repo
Claude Opus 5—
GLM-5.3-Flash56.3%
Source
Not directly comparable
OpenHarmony Bench
Claude Opus 5—
GLM-5.3-Flash57.3%
Source
Not directly comparable

Reasoning

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

Multimodal

Chartography (no tools)
Claude Opus 529.6%
Source
GLM-5.3-Flash—
Not directly comparable
Chartography (tools)
Claude Opus 583.0%
Source
GLM-5.3-Flash78.0%
Source
Claude Opus 5 leads this result
BenchCAD Vision2Code (no tools)
Claude Opus 50.366
Source
GLM-5.3-Flash—
Not directly comparable
BenchCAD Vision2Code (tools)
Claude Opus 50.821
Source
GLM-5.3-Flash—
Not directly comparable
GDP.pdf (no tools)
Claude Opus 583.4%
Source
GLM-5.3-Flash—
Not directly comparable
GDP.pdf (tools)
Claude Opus 585.5%
Source
GLM-5.3-Flash—
Not directly comparable
OfficeQA
Claude Opus 578.1%
Source
GLM-5.3-Flash—
Not directly comparable
OfficeQA Pro
Claude Opus 566.9%
Source
GLM-5.3-Flash62.4%
Source
Claude Opus 5 leads this result
CharXiv
Claude Opus 5—
GLM-5.3-Flash89.4%
Source
Not directly comparable
BabyVision
Claude Opus 5—
GLM-5.3-Flash53.4%
Source
Not directly comparable
MMVU
Claude Opus 5—
GLM-5.3-Flash80.5%
Source
Not directly comparable

Knowledge

HLE
Claude Opus 564.7%
Source
GLM-5.3-Flash—
Not directly comparable
HLE w/o tools
Claude Opus 556.3%
Source
GLM-5.3-Flash—
Not directly comparable
HLE-Verified
Claude Opus 554.4%
Source
GLM-5.3-Flash—
Not directly comparable
LABBench2
Claude Opus 584.2%
Source
GLM-5.3-Flash—
Not directly comparable
HealthBench (raw)
Claude Opus 567.1%
Source
GLM-5.3-Flash—
Not directly comparable
HealthBench (length-adjusted)
Claude Opus 557.8%
Source
GLM-5.3-Flash—
Not directly comparable
HealthBench Professional
Claude Opus 559.8%
Source
GLM-5.3-Flash—
Not directly comparable
HealthBench Professional (raw)
Claude Opus 573.4%
Source
GLM-5.3-Flash—
Not directly comparable
BioMysteryBench (human-solvable)
Claude Opus 590.1%
Source
GLM-5.3-Flash—
Not directly comparable
BioMysteryBench (human-difficult)
Claude Opus 549.4%
Source
GLM-5.3-Flash—
Not directly comparable
SpatialBench Verified
Claude Opus 572.5%
Source
GLM-5.3-Flash—
Not directly comparable
SingleCellBench
Claude Opus 560.6%
Source
GLM-5.3-Flash—
Not directly comparable
ProteinGym Hard
Claude Opus 547.7%
Source
GLM-5.3-Flash—
Not directly comparable
Protein Design
Claude Opus 542.5%
Source
GLM-5.3-Flash—
Not directly comparable
Organic chemistry V2
Claude Opus 561.6%
Source
GLM-5.3-Flash—
Not directly comparable
Protocols (troubleshooting)
Claude Opus 561.1%
Source
GLM-5.3-Flash—
Not directly comparable
Protocols (understanding)
Claude Opus 578.4%
Source
GLM-5.3-Flash—
Not directly comparable
GPQA Diamond (Vals)
Claude Opus 593.4%
Source
GLM-5.3-Flash86.4%
Source
Claude Opus 5 leads this result
MMLU-Pro (Vals)
Claude Opus 591.6%
Source
GLM-5.3-Flash86.1%
Source
Claude Opus 5 leads this result

Multilingual

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

Instruction following

Gray Swan IPI (15 attempts)
Claude Opus 54.6%
Source
GLM-5.3-Flash—
Not directly comparable

Math

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

85 public results · 13 shared

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Last updated October 10, 2026

Claude Opus 5 vs GLM-5.3-Flash

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 5 or GLM-5.3-Flash?

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

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

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

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

Benchmark evidence

Agentic

Coding

Reasoning

Multimodal

Knowledge

Multilingual

Instruction following

Math

Related comparisons

Watch Claude Opus 5 vs GLM-5.3-Flash