Model A

Z.AI

66.03/100

Supported · Public rank #35

90% interval 57.1–75.0

GLM-5.3-Flash vs GPT-5.2

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

Model B

GPT-5.2

OpenAI

64.87/100

Supported · Public rank #39

90% interval 59.3–70.4

Decision reading

GLM-5.3-Flash has the higher public score estimate, 66.03 versus 64.87, 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.

Share or export

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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
GLM-5.3-Flash
GLM-5.3-Flash leads on the public coding lane, 58.2 to 46.5, 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-Flash
GLM-5.3-Flash leads on the public agentic lane, 60.1 to 42.9, with Supported evidence for both models, although the 90% intervals overlap.
Confidence: limited
Long documents
Prompts that approach the documented context limit
GLM-5.3-Flash
GLM-5.3-Flash has the larger documented context window.
Confidence: documented

Show secondary and unsupported calls

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

What is actually comparable

Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.

18 GLM-5.3-Flash only

1 Shared

14 GPT-5.2 only

Shared results: 1
GLM-5.3-Flash only: 18
GPT-5.2 only: 14
Like-for-like categories: 3 / 8

1 category rests on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.

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

GLM-5.3-Flash: 60.1; Supported · #21/152
GPT-5.2: 42.9; Supported · #106/152
Basis: BenchAlign lane · 6 vs 4 public rows
Reading: GLM-5.3-Flash leads · intervals overlap

Coding

Like-for-like

GLM-5.3-Flash: 58.2; Supported · #29/151
GPT-5.2: 46.5; Supported · #83/151
Basis: BenchAlign lane · 6 vs 3 public rows
Reading: GLM-5.3-Flash leads · intervals overlap

Knowledge

Like-for-like

GLM-5.3-Flash: 66.2; Supported · #22/182
GPT-5.2: 61.8; Supported · #30/182
Basis: BenchAlign lane · 2 vs 1 public rows
Reading: GLM-5.3-Flash leads · intervals overlap

Multimodal

Directional only

GLM-5.3-Flash: 80.5; #10/48
GPT-5.2: 66.3; #23/48
Basis: Provisional lane · 2 vs 2 weighted rows
Reading: Directional only

Reasoning

Not comparable

GLM-5.3-Flash: Not ranked
GPT-5.2: 53.7; Unranked · 3 rankable rows
Basis: Provisional lane · 0 vs 1 weighted rows
Reading: Not comparable

Math

Not comparable

GLM-5.3-Flash: Not ranked
GPT-5.2: 57.5; Unranked · 2 rankable rows
Basis: Provisional lane · 0 vs 2 weighted rows
Reading: Not comparable

Multilingual

Not comparable

GLM-5.3-Flash: Not ranked
GPT-5.2: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Instruction following

Not comparable

GLM-5.3-Flash: Not ranked
GPT-5.2: 92.6; #14/121
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Category scores with the server-provided evidence basis for GLM-5.3-Flash and GPT-5.2
Category	GLM-5.3-Flash	GPT-5.2	Basis	Reading
Agentic	60.1Supported · #21/152	42.9Supported · #106/152	Like-for-likeBenchAlign lane · 6 vs 4 public rows	GLM-5.3-Flash leads · intervals overlap
Coding	58.2Supported · #29/151	46.5Supported · #83/151	Like-for-likeBenchAlign lane · 6 vs 3 public rows	GLM-5.3-Flash leads · intervals overlap
Knowledge	66.2Supported · #22/182	61.8Supported · #30/182	Like-for-likeBenchAlign lane · 2 vs 1 public rows	GLM-5.3-Flash leads · intervals overlap
Multimodal	80.5#10/48	66.3#23/48	Directional onlyProvisional lane · 2 vs 2 weighted rows	Directional only
Reasoning	Not ranked	53.7Unranked · 3 rankable rows	Not comparableProvisional lane · 0 vs 1 weighted rows	Not comparable
Math	Not ranked	57.5Unranked · 2 rankable rows	Not comparableProvisional lane · 0 vs 2 weighted rows	Not comparable
Multilingual	Not ranked	Not ranked	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Instruction following	Not ranked	92.6#14/121	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.

CharXiv
Multimodal
GLM-5.3-Flash: 89.4%GPT-5.2: 82.1%Normalized gap 7.3GLM-5.3-Flash source GPT-5.2 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

GLM-5.3-Flash: Self-hosted; infrastructure cost varies; Fits in one request
GPT-5.2: $0.00875; Fits in one request

GLM-5.3-Flash has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

GLM-5.3-Flash: Self-hosted; infrastructure cost varies; Fits in one request
GPT-5.2: $0.1295; 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

GLM-5.3-Flash: Self-hosted; infrastructure cost varies; Fits in one request; Cached-input rate unavailable
GPT-5.2: $0.525; Fits in one request; Cached input priced at the published list-input rate

GPT-5.2 has no published cached-input rate, so cached tokens use its listed input rate. GLM-5.3-Flash 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.

SpecificationGLM-5.3-FlashGPT-5.2

Context window

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

GLM-5.3-Flash

GLM-5.3-Flash model card

GPT-5.2

400K

API model ID

GLM-5.3-Flash

glm-5.3-flash

GLM-5.3-Flash model card

GPT-5.2

Not sourced

Cached-input rate

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

GLM-5.3-Flash

No comparable hosted API rate

GLM-5.3-Flash model card

GPT-5.2

Not published

Documented inputs

GLM-5.3-Flash

Not sourced

GPT-5.2

Not sourced

Documented outputs

GLM-5.3-Flash

Not sourced

GPT-5.2

Not sourced

Provider availability

GLM-5.3-Flash

Not sourced

GPT-5.2

Not sourced

Reasoning profile

GLM-5.3-Flash

Reasoning

GPT-5.2

Reasoning

Weight access

GLM-5.3-Flash

Open Weight

GPT-5.2

Proprietary

License

GLM-5.3-Flash

Open Weight

GPT-5.2

Proprietary

Release date

GLM-5.3-Flash

2026-08-26

GPT-5.2

2025-12-11

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-Flash has the higher public score estimate, 66.03 versus 64.87, but the 90% score intervals overlap.
Workload cost: A complete comparable API-rate estimate is not available for both models.
Context tradeoff: GLM-5.3-Flash has the larger documented window (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 evidence33 rows

Agentic

Terminal-Bench 2.1
GLM-5.3-Flash84.3%
Source
GPT-5.2—
Not directly comparable
Toolathlon-Verified
GLM-5.3-Flash78.4%
Source
GPT-5.2—
Not directly comparable
AutomationBench
GLM-5.3-Flash48.8%
Source
GPT-5.2—
Not directly comparable
Agents' Last Exam
GLM-5.3-Flash26.3%
Source
GPT-5.2—
Not directly comparable
HLE w/ tools
GLM-5.3-Flash55.3%
Source
GPT-5.2—
Not directly comparable
Terminal-Bench 2.1 (Vals)
GLM-5.3-Flash62.9%
Source
GPT-5.2—
Not directly comparable
BrowseComp
GLM-5.3-Flash—
GPT-5.265.8%
Source
Not directly comparable
OSWorld-Verified
GLM-5.3-Flash—
GPT-5.247.3%
Source
Not directly comparable
Gert Labs
GLM-5.3-Flash—
GPT-5.246.54%
Source
Not directly comparable
JobBench
GLM-5.3-Flash—
GPT-5.234.3%
Source
Not directly comparable

Coding

Terminal-Bench 2.1
GLM-5.3-Flash84.3%
Source
GPT-5.2—
Not directly comparable
deepSwe
GLM-5.3-Flash63.4%
Source
GPT-5.2—
Not directly comparable
NL2Repo
GLM-5.3-Flash56.3%
Source
GPT-5.2—
Not directly comparable
LiveCodeBench (Vals)
GLM-5.3-Flash80.5%
Source
GPT-5.2—
Not directly comparable
SWE-bench (Vals)
GLM-5.3-Flash92.0%
Source
GPT-5.2—
Not directly comparable
OpenHarmony Bench
GLM-5.3-Flash57.3%
Source
GPT-5.2—
Not directly comparable
SWE-bench Verified
GLM-5.3-Flash—
GPT-5.280%
Source
Not directly comparable
SWE-bench Pro
GLM-5.3-Flash—
GPT-5.255.6%
Source
Not directly comparable
Vibe Code Bench
GLM-5.3-Flash—
GPT-5.253.50%
Source
Not directly comparable

Reasoning

ARC-AGI-2
GLM-5.3-Flash—
GPT-5.252.9%
Source
Not directly comparable

Knowledge

GPQA Diamond (Vals)
GLM-5.3-Flash86.4%
Source
GPT-5.2—
Not directly comparable
MMLU-Pro (Vals)
GLM-5.3-Flash86.1%
Source
GPT-5.2—
Not directly comparable
GPQA
GLM-5.3-Flash—
GPT-5.292.4%
Source
Not directly comparable

Math

FrontierMath v2 (Tiers 1-3)
GLM-5.3-Flash—
GPT-5.240.700%
Source
Not directly comparable
FrontierMath v2 (Tier 4)
GLM-5.3-Flash—
GPT-5.218.800%
Source
Not directly comparable

Multimodal

OfficeQA Pro
GLM-5.3-Flash62.4%
Source
GPT-5.2—
Not directly comparable
CharXiv
GLM-5.3-Flash89.4%
Source
GPT-5.282.1%
Source
GLM-5.3-Flash leads this result
Chartography (tools)
GLM-5.3-Flash78.0%
Source
GPT-5.2—
Not directly comparable
BabyVision
GLM-5.3-Flash53.4%
Source
GPT-5.2—
Not directly comparable
MMVU
GLM-5.3-Flash80.5%
Source
GPT-5.2—
Not directly comparable
MMMU-Pro
GLM-5.3-Flash—
GPT-5.279.5%
Source
Not directly comparable
MathVision
GLM-5.3-Flash—
GPT-5.283.0%
Source
Not directly comparable
V*
GLM-5.3-Flash—
GPT-5.275.9%
Source
Not directly comparable

Frequently asked questions

Which is better, GLM-5.3-Flash or GPT-5.2?

GLM-5.3-Flash has the higher public score estimate, 66.03 versus 64.87, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.

Which is better for coding, GLM-5.3-Flash or GPT-5.2?

GLM-5.3-Flash leads the public coding lane, 58.2 to 46.5, with Supported evidence for both models, although the 90% intervals overlap.

Which is better for agentic tasks, GLM-5.3-Flash or GPT-5.2?

GLM-5.3-Flash leads the public agentic tasks lane, 60.1 to 42.9, with Supported evidence for both models, although the 90% intervals overlap.

Which costs less, GLM-5.3-Flash or GPT-5.2?

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-Flash or GPT-5.2?

GLM-5.3-Flash has the larger documented context window: 1M, compared with 400K.

Related comparisons

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Last updated September 8, 2026

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GLM-5.3-Flash vs GPT-5.2

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

Multimodal

Reasoning

Math

Multilingual

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

Multimodal

Frequently asked questions

Which is better, GLM-5.3-Flash or GPT-5.2?

Which is better for coding, GLM-5.3-Flash or GPT-5.2?

Which is better for agentic tasks, GLM-5.3-Flash or GPT-5.2?

Which costs less, GLM-5.3-Flash or GPT-5.2?

Which has the larger context window, GLM-5.3-Flash or GPT-5.2?

Related comparisons

Watch GLM-5.3-Flash vs GPT-5.2