GLM-5.3 vs GPT-6 Astra

Updated October 17, 2026. Rank says GPT-6 Astra is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.

Decision reading

GPT-6 Astra has the higher public score, 83.88 versus 67.88, and the 90% score intervals do not overlap. 14 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

Model A

GLM-5.3

Z.AI

67.88/100

Supported · Public rank #26

90% interval 63.4–72.3

Model B

GPT-6 Astra

OpenAI

83.88/100

Supported · Public rank #3

90% interval 79.4–88.3

Shared results: 14
GLM-5.3 only: 17
GPT-6 Astra only: 26
Like-for-like categories: 3 / 8

Supported: GLM-5.3 and GPT-6 AstraHow 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
GPT-6 Astra
GPT-6 Astra has the higher public coding point estimate, 75.5 to 56.4, 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
GPT-6 Astra
GPT-6 Astra has the higher public agentic point estimate, 74.8 to 70.2, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
Confidence: limited
Long documents
Prompts that approach the documented context limit
GPT-6 Astra
GPT-6 Astra 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: 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.

56.4GLM-5.375.5GPT-6 Astra

Like-for-like · BenchAlign v5.8

GPT-6 Astra 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.

FrontierMath v2 (Tier 4)Math
Normalized gap 68.3
GLM-5.3:29.268%
GPT-6 Astra:97.600%
GLM-5.3 source GPT-6 Astra source
FrontierMath v2 (Tiers 1-3)Math
Normalized gap 24.9
GLM-5.3:68.772%
GPT-6 Astra:93.684%
Shared source
AutomationBenchAgentic
Normalized gap 6.8
GLM-5.3:48.2%
GPT-6 Astra:41.4%
GLM-5.3 source GPT-6 Astra 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

GLM-5.3: 70.2; Supported · #8/128
GPT-6 Astra: 74.8; Supported · #7/128
Basis: BenchAlign v5.8 lane · 12 vs 14 public rows
Reading: GPT-6 Astra leads · intervals overlap

Coding

Like-for-like

GLM-5.3: 56.4; Supported · #28/150
GPT-6 Astra: 75.5; Supported · #4/150
Basis: BenchAlign v5.8 lane · 14 vs 6 public rows
Reading: GPT-6 Astra leads

Knowledge

Like-for-like

GLM-5.3: 62.7; Supported · #43/184
GPT-6 Astra: 86.2; Supported · #3/184
Basis: BenchAlign v5.8 lane · 2 vs 7 public rows
Reading: GPT-6 Astra leads

Reasoning

Directional only

GLM-5.3: 80.6; #13/28
GPT-6 Astra: 93.3; #1/28
Basis: Provisional lane · 0 vs 2 weighted rows
Reading: Directional only

Multimodal

Not comparable

GLM-5.3: Not ranked
GPT-6 Astra: 87.3; Unranked · 3 rankable rows
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Multilingual

Not comparable

GLM-5.3: Not ranked
GPT-6 Astra: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Instruction following

Not comparable

GLM-5.3: Not ranked
GPT-6 Astra: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Math

Not comparable

GLM-5.3: 71.5; Unranked · 2 rankable rows
GPT-6 Astra: 97.3; Unranked · 2 rankable rows
Basis: Provisional lane · 2 vs 2 weighted rows
Reading: Not comparable

Category scores with the server-provided evidence basis for GLM-5.3 and GPT-6 Astra
Category	GLM-5.3	GPT-6 Astra	Basis	Reading
Agentic	70.2Supported · #8/128	74.8Supported · #7/128	Like-for-likeBenchAlign v5.8 lane · 12 vs 14 public rows	GPT-6 Astra leads · intervals overlap
Coding	56.4Supported · #28/150	75.5Supported · #4/150	Like-for-likeBenchAlign v5.8 lane · 14 vs 6 public rows	GPT-6 Astra leads
Knowledge	62.7Supported · #43/184	86.2Supported · #3/184	Like-for-likeBenchAlign v5.8 lane · 2 vs 7 public rows	GPT-6 Astra leads
Reasoning	80.6#13/28	93.3#1/28	Directional onlyProvisional lane · 0 vs 2 weighted rows	Directional only
Multimodal	Not ranked	87.3Unranked · 3 rankable rows	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
Instruction following	Not ranked	Not ranked	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Math	71.5Unranked · 2 rankable rows	97.3Unranked · 2 rankable rows	Not comparableProvisional lane · 2 vs 2 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

GLM-5.3: Self-hosted; infrastructure cost varies; Fits in one request
GPT-6 Astra: $0.035; Fits in one request

GLM-5.3 has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

GLM-5.3: Self-hosted; infrastructure cost varies; Fits in one request
GPT-6 Astra: $0.65; 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

GLM-5.3: Self-hosted; infrastructure cost varies; Fits in one request; Cached-input rate unavailable
GPT-6 Astra: $0.9; Fits in one request

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.

SpecificationGLM-5.3GPT-6 Astra

Context window

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

GLM-5.3

Z.AI GLM-5.3 model card

GPT-6 Astra

1.05M

OpenAI GPT-6 Astra model documentation

API model ID

GLM-5.3

zai-org/GLM-5.3

Z.AI GLM-5.3 model card

GPT-6 Astra

gpt-6-astra

OpenAI GPT-6 Astra model documentation

Cached-input rate

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

GLM-5.3

No comparable hosted API rate

Z.AI GLM-5.3 model card

GPT-6 Astra

$1 per 1M cached input tokens

OpenAI GPT-6 Astra model documentation

Documented inputs

GLM-5.3

Not sourced

GPT-6 Astra

text, image

OpenAI GPT-6 Astra model documentation

Documented outputs

GLM-5.3

Not sourced

GPT-6 Astra

text

OpenAI GPT-6 Astra model documentation

Provider availability

GLM-5.3

Not sourced

GPT-6 Astra

Limited Availability · OpenAI Responses API, OpenAI Chat Completions API, ChatGPT Plus, Pro, Business, and Enterprise, Microsoft Azure, AWS Bedrock

OpenAI GPT-6 Astra model documentation

Reasoning profile

GLM-5.3

Reasoning

GPT-6 Astra

Reasoning

Weight access

GLM-5.3

Open Weight

GPT-6 Astra

Proprietary

License

GLM-5.3

Open Weight

GPT-6 Astra

Proprietary

Release date

GLM-5.3

2026-08-14

GPT-6 Astra

2026-09-03

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: GPT-6 Astra has the higher public score, 83.88 versus 67.88, and the 90% score intervals do not overlap.
Workload cost: A complete comparable API-rate estimate is not available for both models.
Context tradeoff: GPT-6 Astra has the larger documented window (1.05M).

Run the same representative tasks against both endpoints before changing production traffic.

Questions

Which is better, GLM-5.3 or GPT-6 Astra?

GPT-6 Astra has the higher public score, 83.88 versus 67.88, and the 90% score intervals do not overlap. That is the clearest overall quality signal in the current public data.

Which is better for coding, GLM-5.3 or GPT-6 Astra?

GPT-6 Astra has the higher public coding point estimate, 75.5 to 56.4, with Supported evidence for both models. The conditional ranges do not establish rank confidence.

Which is better for agentic tasks, GLM-5.3 or GPT-6 Astra?

GPT-6 Astra has the higher public agentic tasks point estimate, 74.8 to 70.2, with Supported evidence for both models. The conditional ranges do not establish rank confidence.

Which costs less, GLM-5.3 or GPT-6 Astra?

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 or GPT-6 Astra?

GPT-6 Astra has the larger documented context window: 1.05M, compared with 1M.

Self-host vs API cost

Estimates at 50,000 req/day · 1000 tokens/req average.

GLM-5.3

API / mo$0

Self-host / mo$27,962

Break-even—

GPT-6 Astra

API / mo$45,000

Self-host / moNot listed

Break-even—

Proprietary model — self-hosting not applicable.

Model the full break-even

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 2.1
GLM-5.388.2%
Source
GPT-6 Astra—
Not directly comparable
terminalBench3
GLM-5.328.3%
Source
GPT-6 Astra—
Not directly comparable
CyberGym
GLM-5.384.5%
Source
GPT-6 Astra—
Not directly comparable
ExploitGym
GLM-5.315.0%
Source
GPT-6 Astra42.4%
Source
GPT-6 Astra leads this result
Toolathlon-Verified
GLM-5.373.0%
Source
GPT-6 Astra—
Not directly comparable
AutomationBench
GLM-5.348.2%
Source
GPT-6 Astra41.4%
Source
GLM-5.3 leads this result
Agents' Last Exam
GLM-5.328.5%
Source
GPT-6 Astra59.3%
Source
GPT-6 Astra leads this result
HLE w/ tools
GLM-5.362.5%
Source
GPT-6 Astra57.2%
Source
GLM-5.3 leads this result
Terminal-Bench 2.1 (Vals)
GLM-5.371.5%
Source
GPT-6 Astra87.3%
Source
GPT-6 Astra leads this result
Hermes Index
Shared source
GLM-5.339.2%
GPT-6 Astra56.3%
GPT-6 Astra leads this result
Hermes Bench
Shared source
GLM-5.371.8%
GPT-6 Astra73.7%
GPT-6 Astra leads this result
MazeBench
GLM-5.30%
Source
GPT-6 Astra23%
Source
GPT-6 Astra leads this result
BrowseComp
GLM-5.3—
GPT-6 Astra91.5%
Source
Not directly comparable
OSWorld 2.0
GLM-5.3—
GPT-6 Astra72.6%
Source
Not directly comparable
Terminal-Bench 4.0
GLM-5.3—
GPT-6 Astra57.90%
Source
Not directly comparable
Terminal-Bench-Science 0.1
GLM-5.3—
GPT-6 Astra64.6%
Source
Not directly comparable
ApprenticeBench
GLM-5.3—
GPT-6 Astra68%
Source
Not directly comparable
CWE-bench v1
GLM-5.3—
GPT-6 Astra68.0%
Source
Not directly comparable

Coding

Terminal-Bench 2.1
GLM-5.388.2%
Source
GPT-6 Astra—
Not directly comparable
terminalBench3
GLM-5.328.3%
Source
GPT-6 Astra—
Not directly comparable
DeepSWE
GLM-5.366.9%
Source
GPT-6 Astra74.1%
Source
GPT-6 Astra leads this result
NL2Repo
GLM-5.358%
Source
GPT-6 Astra—
Not directly comparable
ProgramBench
GLM-5.319.0%
Source
GPT-6 Astra—
Not directly comparable
FrontierSWE
GLM-5.378.1%
Source
GPT-6 Astra—
Not directly comparable
sweMarathon
GLM-5.342.5%
Source
GPT-6 Astra—
Not directly comparable
PostTrain Bench
GLM-5.339.8%
Source
GPT-6 Astra—
Not directly comparable
VulcanBench v3
GLM-5.378.3%
Source
GPT-6 Astra—
Not directly comparable
OpenHarmony Bench
GLM-5.360.8%
Source
GPT-6 Astra—
Not directly comparable
FrontierSWE v2
Shared source
GLM-5.330.2%
GPT-6 Astra65.5%
GPT-6 Astra leads this result
LiveCodeBench (Vals)
GLM-5.380.5%
Source
GPT-6 Astra—
Not directly comparable
SWE-bench (Vals)
GLM-5.395.4%
Source
GPT-6 Astra—
Not directly comparable
Bug Hunt Bench
Shared source
GLM-5.319.0 fixes
GPT-6 Astra45.0 fixes
GPT-6 Astra leads this result
FrontierCode 1.1 Main
GLM-5.3—
GPT-6 Astra53.3%
Source
Not directly comparable
FrontierCode 1.1 Extended
GLM-5.3—
GPT-6 Astra64.5%
Source
Not directly comparable
PostTrainBench v1.1
GLM-5.3—
GPT-6 Astra44.3%
Source
Not directly comparable

Reasoning

STEMBench
GLM-5.3—
GPT-6 Astra2.81
Source
Not directly comparable
ARC-AGI-1
GLM-5.3—
GPT-6 Astra98.50%
Source
Not directly comparable
ARC-AGI-2
GLM-5.3—
GPT-6 Astra95%
Source
Not directly comparable
ARC-AGI-3
GLM-5.3—
GPT-6 Astra62.7%
Source
Not directly comparable
GeneBench-Pro
GLM-5.3—
GPT-6 Astra37.8%
Source
Not directly comparable
MRCR v2 256K-512K
GLM-5.3—
GPT-6 Astra100.0%
Source
Not directly comparable
MRCR v2 512K-1M
GLM-5.3—
GPT-6 Astra96.3%
Source
Not directly comparable
GraphWalks BFS 256K–1M
GLM-5.3—
GPT-6 Astra71.8%
Source
Not directly comparable

Multimodal

ScreenSpot Pro
GLM-5.3—
GPT-6 Astra92.7%
Source
Not directly comparable
BenchCAD Vision2Code (tools)
GLM-5.3—
GPT-6 Astra0.959
Source
Not directly comparable

Knowledge

GPQA Diamond (Vals)
GLM-5.388.1%
Source
GPT-6 Astra—
Not directly comparable
MMLU-Pro (Vals)
GLM-5.386.8%
Source
GPT-6 Astra—
Not directly comparable
GPQA
GLM-5.3—
GPT-6 Astra96%
Source
Not directly comparable
GPQA-D
GLM-5.3—
GPT-6 Astra96.0%
Source
Not directly comparable
HealthBench (raw)
GLM-5.3—
GPT-6 Astra56.9%
Source
Not directly comparable
HealthBench (length-adjusted)
GLM-5.3—
GPT-6 Astra58.3%
Source
Not directly comparable
HealthBench Professional
GLM-5.3—
GPT-6 Astra64.7%
Source
Not directly comparable
HealthBench Professional (raw)
GLM-5.3—
GPT-6 Astra68.2%
Source
Not directly comparable
HealthBench Hard
GLM-5.3—
GPT-6 Astra36.6%
Source
Not directly comparable

Instruction following

Gray Swan IPI (15 attempts)
Shared source
GLM-5.331.5%
GPT-6 Astra8.5%
GPT-6 Astra leads this result

Math

FrontierMath v2 (Tiers 1-3)
Shared source
GLM-5.368.772%
GPT-6 Astra93.684%
GPT-6 Astra leads this result
FrontierMath v2 (Tier 4)
GLM-5.329.268%
Source
GPT-6 Astra97.600%
Source
GPT-6 Astra leads this result

57 public results · 14 shared

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Last updated October 17, 2026

GLM-5.3 vs GPT-6 Astra

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, GLM-5.3 or GPT-6 Astra?

Which is better for coding, GLM-5.3 or GPT-6 Astra?

Which is better for agentic tasks, GLM-5.3 or GPT-6 Astra?

Which costs less, GLM-5.3 or GPT-6 Astra?

Which has the larger context window, GLM-5.3 or GPT-6 Astra?

Self-host vs API cost

Benchmark evidence

Agentic

Coding

Reasoning

Multimodal

Knowledge

Instruction following

Math

Related comparisons

Watch GLM-5.3 vs GPT-6 Astra