GLM-5.2 vs GPT-5.6 Sol

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

Decision reading

GPT-5.6 Sol has the higher public score, 78.49 versus 62.52, and the 90% score intervals do not overlap. 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

GLM-5.2

Z.AI

62.52/100

Supported · Public rank #36

90% interval 55.8–69.2

Model B

GPT-5.6 Sol

OpenAI

78.49/100

Supported · Public rank #7

90% interval 75.6–81.4

Shared results: 13
GLM-5.2 only: 12
GPT-5.6 Sol only: 24
Like-for-like categories: 3 / 8

Supported: GLM-5.2 and GPT-5.6 SolHow 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-5.6 Sol
GPT-5.6 Sol leads on the public coding lane, 71.6 to 57.2, with Supported evidence for both models and non-overlapping 90% intervals.
Confidence: stronger
Agentic work
Tool use, computer use, and multi-step task completion
GPT-5.6 Sol
GPT-5.6 Sol leads on the public agentic lane, 69.6 to 55.6, with Supported evidence for both models and non-overlapping 90% intervals.
Confidence: stronger
Long documents
Prompts that approach the documented context limit
GPT-5.6 Sol
GPT-5.6 Sol has the larger documented context window.
Confidence: documented

Show secondary and unsupported calls

Chat turn cost
1K fresh input + 500 output tokens
GLM-5.2
GLM-5.2 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
Cache-heavy agent loop cost
200K cached + 20K fresh input + 10K output tokens
GLM-5.2
GLM-5.2 has the lower estimated token cost for this stated workload. GLM-5.2 has no published cached-input rate, so cached tokens use its listed input rate.
Confidence: rate-fallback
Repository review cost
50K fresh input + 3K output tokens
GLM-5.2
GLM-5.2 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
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.

57.2GLM-5.271.6GPT-5.6 Sol

Like-for-like · BenchAlign v5.7

GPT-5.6 Sol leads the like-for-like coding row.

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.

LiveCodeBench (Vals)Coding
Normalized gap 13.1
GLM-5.2:69.5%
GPT-5.6 Sol:82.6%
GLM-5.2 source GPT-5.6 Sol source
cursorBench32Coding
Normalized gap 12.2
GLM-5.2:55.0%
GPT-5.6 Sol:67.2%
Shared source
GPQAKnowledge
Normalized gap 3.4
GLM-5.2:91.2%
GPT-5.6 Sol:94.6%
GLM-5.2 source GPT-5.6 Sol source
SWE-bench ProCoding
Normalized gap 2.5
GLM-5.2:62.1%
GPT-5.6 Sol:64.6%
GLM-5.2 source GPT-5.6 Sol source
MMLU-Pro (Vals)Knowledge
Normalized gap 2.4
GLM-5.2:86.7%
GPT-5.6 Sol:89.1%
GLM-5.2 source GPT-5.6 Sol 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.

Agentic

Like-for-like

GLM-5.2: 55.6; Supported · #26/105
GPT-5.6 Sol: 69.6; Supported · #7/105
Basis: BenchAlign v5.7 lane · 6 vs 9 public rows
Reading: GPT-5.6 Sol leads

Coding

Like-for-like

GLM-5.2: 57.2; Supported · #22/135
GPT-5.6 Sol: 71.6; Supported · #6/135
Basis: BenchAlign v5.7 lane · 8 vs 12 public rows
Reading: GPT-5.6 Sol leads

Knowledge

Like-for-like

GLM-5.2: 57.2; Supported · #40/158
GPT-5.6 Sol: 78.8; Supported · #7/158
Basis: BenchAlign v5.7 lane · 6 vs 8 public rows
Reading: GPT-5.6 Sol leads

Instruction following

Directional only

GLM-5.2: 88.5; #23/124
GPT-5.6 Sol: 87.7; #28/124
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Directional only

Reasoning

Not comparable

GLM-5.2: 75.9; Unranked · 2 rankable rows
GPT-5.6 Sol: 72.1; #8/19
Basis: Provisional lane · 0 vs 2 weighted rows
Reading: Not comparable

Multimodal

Not comparable

GLM-5.2: Not ranked
GPT-5.6 Sol: 87.6; #5/50
Basis: Provisional lane · 0 vs 1 weighted rows
Reading: Not comparable

Multilingual

Not comparable

GLM-5.2: Not ranked
GPT-5.6 Sol: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Math

Not comparable

GLM-5.2: 80.7; Unranked · 4 rankable rows
GPT-5.6 Sol: 96.8; 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-5.2 and GPT-5.6 Sol
Category	GLM-5.2	GPT-5.6 Sol	Basis	Reading
Agentic	55.6Supported · #26/105	69.6Supported · #7/105	Like-for-likeBenchAlign v5.7 lane · 6 vs 9 public rows	GPT-5.6 Sol leads
Coding	57.2Supported · #22/135	71.6Supported · #6/135	Like-for-likeBenchAlign v5.7 lane · 8 vs 12 public rows	GPT-5.6 Sol leads
Knowledge	57.2Supported · #40/158	78.8Supported · #7/158	Like-for-likeBenchAlign v5.7 lane · 6 vs 8 public rows	GPT-5.6 Sol leads
Instruction following	88.5#23/124	87.7#28/124	Directional onlyProvisional lane · 0 vs 0 weighted rows	Directional only
Reasoning	75.9Unranked · 2 rankable rows	72.1#8/19	Not comparableProvisional lane · 0 vs 2 weighted rows	Not comparable
Multimodal	Not ranked	87.6#5/50	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
Math	80.7Unranked · 4 rankable rows	96.8Unranked · 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-5.2: $0.0036; Fits in one request
GPT-5.6 Sol: $0.014; Fits in one request

GLM-5.2 has the lower modeled cost

Costs use the listed standard API rates.

Repository review

50K fresh input + 3K output tokens

GLM-5.2: $0.0832; Fits in one request
GPT-5.6 Sol: $0.26; Fits in one request

GLM-5.2 has the lower modeled cost

Costs use the listed standard API rates.

Cache-heavy agent loop

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

GLM-5.2: $0.352; Fits in one request; Cached input priced at the published list-input rate
GPT-5.6 Sol: $0.36; Fits in one request

GLM-5.2 has the lower modeled cost

GLM-5.2 has no published cached-input rate, so cached tokens use its listed input 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-5.2GPT-5.6 Sol

Context window

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

GLM-5.2

GPT-5.6 Sol

1.05M

OpenAI model catalog

API model ID

GLM-5.2

Not sourced

GPT-5.6 Sol

gpt-5.6-sol

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-5.2

Not published

GPT-5.6 Sol

$0.4 per 1M cached input tokens

OpenAI pricing

Documented inputs

GLM-5.2

Not sourced

GPT-5.6 Sol

text, image

OpenAI model catalog

Documented outputs

GLM-5.2

Not sourced

GPT-5.6 Sol

text

OpenAI model catalog

Provider availability

GLM-5.2

Not sourced

GPT-5.6 Sol

Generally Available · OpenAI Responses API

OpenAI model catalog

Reasoning profile

GLM-5.2

Reasoning

GPT-5.6 Sol

Reasoning

Weight access

GLM-5.2

Open Weight

GPT-5.6 Sol

Proprietary

License

GLM-5.2

Open Weight

GPT-5.6 Sol

Proprietary

Release date

GLM-5.2

2026-06-16

GPT-5.6 Sol

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 Sol has the higher public score, 78.49 versus 62.52, and the 90% score intervals do not overlap.
Workload cost: Repository review: $0.0832 vs $0.26. Cache-heavy agent loop: $0.352 vs $0.36.
Context tradeoff: GPT-5.6 Sol has the larger documented window (1.05M).

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

Questions

Which is better, GLM-5.2 or GPT-5.6 Sol?

GPT-5.6 Sol has the higher public score, 78.49 versus 62.52, 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-5.2 or GPT-5.6 Sol?

GPT-5.6 Sol leads the public coding lane, 71.6 to 57.2, with Supported evidence for both models and non-overlapping 90% intervals.

Which is better for agentic tasks, GLM-5.2 or GPT-5.6 Sol?

GPT-5.6 Sol leads the public agentic tasks lane, 69.6 to 55.6, with Supported evidence for both models and non-overlapping 90% intervals.

Which costs less, GLM-5.2 or GPT-5.6 Sol?

For the stated presets, chat costs $0.0036 on GLM-5.2 and $0.014 on GPT-5.6 Sol; repository review costs $0.0832 and $0.26; the cache-heavy agent loop costs $0.352 and $0.36. GLM-5.2 has no published cached-input rate, so cached tokens use its listed input rate.

Which has the larger context window, GLM-5.2 or GPT-5.6 Sol?

GPT-5.6 Sol has the larger documented context window: 1.05M, compared with 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 evidence49 rows

Agentic

Terminal-Bench 3.0
Shared source
GLM-5.24.6%
GPT-5.6 Sol34.6%
GPT-5.6 Sol leads this result
Terminal-Bench 2.1
GLM-5.281.0%
Source
GPT-5.6 Sol91.9%
Source
GPT-5.6 Sol leads this result
MCP Atlas
GLM-5.276.8%
Source
GPT-5.6 Sol—
Not directly comparable
Toolathlon
GLM-5.248.2%
Source
GPT-5.6 Sol58%
Source
GPT-5.6 Sol leads this result
ResearchClawBench
GLM-5.220.7%
Source
GPT-5.6 Sol—
Not directly comparable
Terminal-Bench 2.1 (Vals)
GLM-5.267.8%
Source
GPT-5.6 Sol85.8%
Source
GPT-5.6 Sol leads this result
BrowseComp
GLM-5.2—
GPT-5.6 Sol92.2%
Source
Not directly comparable
OSWorld 2.0
GLM-5.2—
GPT-5.6 Sol62.6%
Source
Not directly comparable
CyberGym
GLM-5.2—
GPT-5.6 Sol84.5%
Source
Not directly comparable
ExploitGym
GLM-5.2—
GPT-5.6 Sol33.7%
Source
Not directly comparable
ApprenticeBench
GLM-5.2—
GPT-5.6 Sol26%
Source
Not directly comparable

Coding

SWE-bench Pro
GLM-5.262.1%
Source
GPT-5.6 Sol64.6%
Source
GPT-5.6 Sol leads this result
NL2Repo
GLM-5.248.9%
Source
GPT-5.6 Sol—
Not directly comparable
Terminal-Bench 2.1
GLM-5.281.0%
Source
GPT-5.6 Sol91.9%
Source
GPT-5.6 Sol leads this result
ProgramBench
GLM-5.263.7%
Source
GPT-5.6 Sol—
Not directly comparable
cursorBench32
Shared source
GLM-5.255.0%
GPT-5.6 Sol67.2%
GPT-5.6 Sol leads this result
OpenHarmony Bench
GLM-5.258.4%
Source
GPT-5.6 Sol—
Not directly comparable
LiveCodeBench (Vals)
GLM-5.269.5%
Source
GPT-5.6 Sol82.6%
Source
GPT-5.6 Sol leads this result
SWE-bench (Vals)
GLM-5.282.8%
Source
GPT-5.6 Sol96.2%
Source
GPT-5.6 Sol leads this result
Bug Hunt Bench
GLM-5.2—
GPT-5.6 Sol42 fixes
Source
Not directly comparable
DeepSWE
GLM-5.2—
GPT-5.6 Sol72.7%
Source
Not directly comparable
FrontierCode 1.1 Extended
GLM-5.2—
GPT-5.6 Sol60.6%
Source
Not directly comparable
FrontierSWE v2
GLM-5.2—
GPT-5.6 Sol32.2%
Source
Not directly comparable
VulcanBench v3
GLM-5.2—
GPT-5.6 Sol87.0%
Source
Not directly comparable
VulcanBench CII v1
GLM-5.2—
GPT-5.6 Sol86.5%
Source
Not directly comparable
cursorBench40
GLM-5.2—
GPT-5.6 Sol41.7%
Source
Not directly comparable

Reasoning

CritPt
GLM-5.220.9%
Source
GPT-5.6 Sol—
Not directly comparable
ARC-AGI-2
GLM-5.2—
GPT-5.6 Sol92.5%
Source
Not directly comparable
ARC-AGI-3
GLM-5.2—
GPT-5.6 Sol7.8%
Source
Not directly comparable
GeneBench-Pro
GLM-5.2—
GPT-5.6 Sol28.7%
Source
Not directly comparable

Multimodal

MMMU-Pro
GLM-5.2—
GPT-5.6 Sol83%
Source
Not directly comparable
MMMU-Pro w/ Python
GLM-5.2—
GPT-5.6 Sol84.6%
Source
Not directly comparable

Knowledge

GPQA
GLM-5.291.2%
Source
GPT-5.6 Sol94.6%
Source
GPT-5.6 Sol leads this result
GPQA-D
GLM-5.291.2%
Source
GPT-5.6 Sol94.6%
Source
GPT-5.6 Sol leads this result
HLE
GLM-5.254.7%
Source
GPT-5.6 Sol—
Not directly comparable
HLE w/o tools
GLM-5.240.5%
Source
GPT-5.6 Sol—
Not directly comparable
GPQA Diamond (Vals)
GLM-5.285.6%
Source
GPT-5.6 Sol95.2%
Source
GPT-5.6 Sol leads this result
MMLU-Pro (Vals)
GLM-5.286.7%
Source
GPT-5.6 Sol89.1%
Source
GPT-5.6 Sol leads this result
HLE-Verified
GLM-5.2—
GPT-5.6 Sol54.5%
Source
Not directly comparable
LABBench2
GLM-5.2—
GPT-5.6 Sol82.1%
Source
Not directly comparable
HealthBench Professional
GLM-5.2—
GPT-5.6 Sol60.5%
Source
Not directly comparable
HealthBench Hard
GLM-5.2—
GPT-5.6 Sol33.1%
Source
Not directly comparable

Math

AIME26
GLM-5.299.2%
Source
GPT-5.6 Sol—
Not directly comparable
HMMT Nov 2025
GLM-5.294.4%
Source
GPT-5.6 Sol—
Not directly comparable
HMMT Feb 2026
GLM-5.292.5%
Source
GPT-5.6 Sol—
Not directly comparable
MMAnswerBench
GLM-5.291.0%
Source
GPT-5.6 Sol—
Not directly comparable
FrontierMath (legacy)
GLM-5.2—
GPT-5.6 Sol89%
Source
Not directly comparable
FrontierMath v2 (Tiers 1-3)
GLM-5.2—
GPT-5.6 Sol89.000%
Source
Not directly comparable
FrontierMath v2 (Tier 4)
GLM-5.2—
GPT-5.6 Sol83.000%
Source
Not directly comparable

49 public results · 13 shared

Related comparisons

Compare API pricing Read the methodology Open the model selector

Watch GLM-5.2 vs GPT-5.6 Sol

One weekly email when material rank, price, or benchmark evidence changes make this matchup worth revisiting.

Read a sample issue

Join 2,000+ readers.

Last updated September 24, 2026

GLM-5.2 vs GPT-5.6 Sol

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

Instruction following

Reasoning

Multimodal

Multilingual

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-5.2 or GPT-5.6 Sol?

Which is better for coding, GLM-5.2 or GPT-5.6 Sol?

Which is better for agentic tasks, GLM-5.2 or GPT-5.6 Sol?

Which costs less, GLM-5.2 or GPT-5.6 Sol?

Which has the larger context window, GLM-5.2 or GPT-5.6 Sol?

Benchmark evidence

Agentic

Coding

Reasoning

Multimodal

Knowledge

Math

Related comparisons

Watch GLM-5.2 vs GPT-5.6 Sol