GLM-4.6 vs GPT-5.6 Luna

Updated September 24, 2026. Rank says GPT-5.6 Luna is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.

Decision reading

GPT-5.6 Luna has the higher public score, 65.6 versus 39.8, and the 90% score intervals do not overlap. 4 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-4.6

Z.AI

39.8/100

Supported · Public rank #115

90% interval 23.8–55.8

Model B

GPT-5.6 Luna

OpenAI

65.6/100

Supported · Public rank #25

90% interval 60.5–70.7

Shared results: 4
GLM-4.6 only: 2
GPT-5.6 Luna only: 25
Like-for-like categories: 1 / 8

Supported: GLM-4.6 and GPT-5.6 LunaHow 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.

Long documents
Prompts that approach the documented context limit
GPT-5.6 Luna
GPT-5.6 Luna has the larger documented context window.
Confidence: documented

Show secondary and unsupported calls

Coding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
GLM-4.6 is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like.
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
GLM-4.6 is not ranked on the public lane for agentic, so no winner is named for agentic.
Confidence: limited
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
The page does not recommend a cost winner because at least one model cannot fit the stated workload in one request. GLM-4.6 does not fit this workload in one request. GLM-4.6 has no comparable published API token rate.
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.

27.7GLM-4.664.5GPT-5.6 Luna

Directional only · BenchAlign v5.7

GPT-5.6 Luna scores higher, but at least one score rests on Estimated evidence or a different benchmark set. Directional only, no winner.

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 (Tiers 1-3)Math
Normalized gap 74.8
GLM-4.6:3.819%
GPT-5.6 Luna:78.600%
GLM-4.6 source GPT-5.6 Luna source
FrontierMath v2 (Tier 4)Math
Normalized gap 56.4
GLM-4.6:2.128%
GPT-5.6 Luna:58.500%
GLM-4.6 source GPT-5.6 Luna source
MMLU-Pro (Vals)Knowledge
Normalized gap 3.8
GLM-4.6:82.2%
GPT-5.6 Luna:86.0%
GLM-4.6 source GPT-5.6 Luna 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 v5.7 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.

Knowledge

Like-for-like

GLM-4.6: 35.9; Supported · #103/158
GPT-5.6 Luna: 64.6; Supported · #22/158
Basis: BenchAlign v5.7 lane · 2 vs 6 public rows
Reading: GPT-5.6 Luna leads

Coding

Directional only

GLM-4.6: 27.7; Estimated · #91/135
GPT-5.6 Luna: 64.5; Supported · #9/135
Basis: BenchAlign v5.7 lane · 2 vs 7 public rows
Reading: Directional only

Agentic

Not comparable

GLM-4.6: Not ranked
GPT-5.6 Luna: 55.3; Supported · #28/105
Basis: BenchAlign v5.7 lane · 0 vs 9 public rows
Reading: Not comparable

Reasoning

Not comparable

GLM-4.6: 40.2; Unranked · 2 rankable rows
GPT-5.6 Luna: 54.7; #18/19
Basis: Provisional lane · 0 vs 2 weighted rows
Reading: Not comparable

Multimodal

Not comparable

GLM-4.6: Not ranked
GPT-5.6 Luna: 67.1; #22/50
Basis: Provisional lane · 0 vs 1 weighted rows
Reading: Not comparable

Multilingual

Not comparable

GLM-4.6: Not ranked
GPT-5.6 Luna: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Instruction following

Not comparable

GLM-4.6: 40.7; #99/124
GPT-5.6 Luna: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Math

Not comparable

GLM-4.6: 27.3; Unranked · 2 rankable rows
GPT-5.6 Luna: 94.2; Unranked · 3 rankable rows
Basis: Provisional lane · 2 vs 2 weighted rows
Reading: Not comparable

Category scores with the server-provided evidence basis for GLM-4.6 and GPT-5.6 Luna
Category	GLM-4.6	GPT-5.6 Luna	Basis	Reading
Knowledge	35.9Supported · #103/158	64.6Supported · #22/158	Like-for-likeBenchAlign v5.7 lane · 2 vs 6 public rows	GPT-5.6 Luna leads
Coding	27.7Estimated · #91/135	64.5Supported · #9/135	Directional onlyBenchAlign v5.7 lane · 2 vs 7 public rows	Directional only
Agentic	Not ranked	55.3Supported · #28/105	Not comparableBenchAlign v5.7 lane · 0 vs 9 public rows	Not comparable
Reasoning	40.2Unranked · 2 rankable rows	54.7#18/19	Not comparableProvisional lane · 0 vs 2 weighted rows	Not comparable
Multimodal	Not ranked	67.1#22/50	Not comparableProvisional lane · 0 vs 1 weighted rows	Not comparable
Multilingual	Not ranked	Not ranked	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Instruction following	40.7#99/124	Not ranked	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Math	27.3Unranked · 2 rankable rows	94.2Unranked · 3 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 v5.7) 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

GLM-4.6: API rate not published; Fits in one request
GPT-5.6 Luna: $0.0008; Fits in one request

GLM-4.6 has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

GLM-4.6: API rate not published; Fits in one request
GPT-5.6 Luna: $0.0136; Fits in one request

GLM-4.6 has no comparable published API token rate.

Cache-heavy agent loop

200K cached + 20K fresh input + 10K output tokens

GLM-4.6: API rate not published; Does not fit in one request; Cached-input rate unavailable
GPT-5.6 Luna: $0.02; Fits in one request

GLM-4.6 does not fit this workload in one request. GLM-4.6 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-4.6GPT-5.6 Luna

Context window

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

GLM-4.6

200K

GPT-5.6 Luna

1.05M

OpenAI model catalog

API model ID

GLM-4.6

Not sourced

GPT-5.6 Luna

gpt-5.6-luna

OpenAI model catalog

Cached-input rate

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

GLM-4.6

No comparable hosted API rate

GPT-5.6 Luna

$0.02 per 1M cached input tokens

OpenAI pricing

Documented inputs

GLM-4.6

Not sourced

GPT-5.6 Luna

text, image

OpenAI model catalog

Documented outputs

GLM-4.6

Not sourced

GPT-5.6 Luna

text

OpenAI model catalog

Provider availability

GLM-4.6

Not sourced

GPT-5.6 Luna

Generally Available · OpenAI Responses API

OpenAI model catalog

Reasoning profile

GLM-4.6

Reasoning

GPT-5.6 Luna

Reasoning

Weight access

GLM-4.6

Open Weight

GPT-5.6 Luna

Proprietary

License

GLM-4.6

Open Weight

GPT-5.6 Luna

Proprietary

Release date

GLM-4.6

2025-09-01

GPT-5.6 Luna

2026-07-09

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-5.6 Luna has the higher public score, 65.6 versus 39.8, 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-5.6 Luna has the larger documented window (1.05M).

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

Questions

Which is better, GLM-4.6 or GPT-5.6 Luna?

GPT-5.6 Luna has the higher public score, 65.6 versus 39.8, 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-4.6 or GPT-5.6 Luna?

GPT-5.6 Luna scores higher for coding on the public lane, 64.5 to 27.7. GLM-4.6 is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like. BenchLM does not name a winner for coding from a directional reading; read the shared benchmark rows directly and test the models on the same task set.

Which is better for agentic tasks, GLM-4.6 or GPT-5.6 Luna?

GLM-4.6 is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.

Which costs less, GLM-4.6 or GPT-5.6 Luna?

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-4.6 or GPT-5.6 Luna?

GPT-5.6 Luna has the larger documented context window: 1.05M, compared with 200K.

Benchmark evidence

The full public result ledger is available for audit without forcing a wide desktop table onto a phone.

Browse raw public benchmark evidence31 rows

Agentic

Terminal-Bench 3.0
GLM-4.6—
GPT-5.6 Luna14.3%
Source
Not directly comparable
Terminal-Bench 2.1
GLM-4.6—
GPT-5.6 Luna84.7%
Source
Not directly comparable
BrowseComp
GLM-4.6—
GPT-5.6 Luna83.3%
Source
Not directly comparable
OSWorld 2.0
GLM-4.6—
GPT-5.6 Luna45.6%
Source
Not directly comparable
CyberGym
GLM-4.6—
GPT-5.6 Luna77.9%
Source
Not directly comparable
ExploitGym
GLM-4.6—
GPT-5.6 Luna12.4%
Source
Not directly comparable
Toolathlon
GLM-4.6—
GPT-5.6 Luna53.4%
Source
Not directly comparable
Terminal-Bench 2.1 (Vals)
GLM-4.6—
GPT-5.6 Luna79.0%
Source
Not directly comparable
ApprenticeBench
GLM-4.6—
GPT-5.6 Luna7%
Source
Not directly comparable

Coding

Vibe Code Bench
GLM-4.63.09%
Source
GPT-5.6 Luna—
Not directly comparable
LiveCodeBench (Vals)
GLM-4.681.0%
Source
GPT-5.6 Luna—
Not directly comparable
SWE-bench Pro
GLM-4.6—
GPT-5.6 Luna62.7%
Source
Not directly comparable
Terminal-Bench 2.1
GLM-4.6—
GPT-5.6 Luna84.7%
Source
Not directly comparable
DeepSWE
GLM-4.6—
GPT-5.6 Luna67.2%
Source
Not directly comparable
FrontierCode 1.1 Extended
GLM-4.6—
GPT-5.6 Luna55.1%
Source
Not directly comparable
cursorBench32
GLM-4.6—
GPT-5.6 Luna61.1%
Source
Not directly comparable
VulcanBench v3
GLM-4.6—
GPT-5.6 Luna85.5%
Source
Not directly comparable
SWE-bench (Vals)
GLM-4.6—
GPT-5.6 Luna93.0%
Source
Not directly comparable

Reasoning

ARC-AGI-2
GLM-4.6—
GPT-5.6 Luna59.5%
Source
Not directly comparable
ARC-AGI-3
GLM-4.6—
GPT-5.6 Luna0.2%
Source
Not directly comparable

Multimodal

MMMU-Pro
GLM-4.6—
GPT-5.6 Luna78.4%
Source
Not directly comparable
MMMU-Pro w/ Python
GLM-4.6—
GPT-5.6 Luna79.5%
Source
Not directly comparable

Knowledge

GPQA Diamond (Vals)
GLM-4.674.5%
Source
GPT-5.6 Luna91.7%
Source
GPT-5.6 Luna leads this result
MMLU-Pro (Vals)
GLM-4.682.2%
Source
GPT-5.6 Luna86.0%
Source
GPT-5.6 Luna leads this result
GPQA
GLM-4.6—
GPT-5.6 Luna92.3%
Source
Not directly comparable
GPQA-D
GLM-4.6—
GPT-5.6 Luna92.3%
Source
Not directly comparable
HealthBench Professional
GLM-4.6—
GPT-5.6 Luna55.7%
Source
Not directly comparable
HealthBench Hard
GLM-4.6—
GPT-5.6 Luna32.0%
Source
Not directly comparable

Math

FrontierMath v2 (Tiers 1-3)
GLM-4.63.819%
Source
GPT-5.6 Luna78.600%
Source
GPT-5.6 Luna leads this result
FrontierMath v2 (Tier 4)
GLM-4.62.128%
Source
GPT-5.6 Luna58.500%
Source
GPT-5.6 Luna leads this result
FrontierMath (legacy)
GLM-4.6—
GPT-5.6 Luna78.6%
Source
Not directly comparable

31 public results · 4 shared

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Last updated September 24, 2026

GLM-4.6 vs GPT-5.6 Luna

Decision reading

Which one for your work

Long documents

Coding work

Agentic work

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

Knowledge

Coding

Agentic

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-4.6 or GPT-5.6 Luna?

Which is better for coding, GLM-4.6 or GPT-5.6 Luna?

Which is better for agentic tasks, GLM-4.6 or GPT-5.6 Luna?

Which costs less, GLM-4.6 or GPT-5.6 Luna?

Which has the larger context window, GLM-4.6 or GPT-5.6 Luna?

Benchmark evidence

Agentic

Coding

Reasoning

Multimodal

Knowledge

Math

Related comparisons

Watch GLM-4.6 vs GPT-5.6 Luna