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GLM-5.2 vs GPT-6 Sol

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

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Decision reading

GPT-6 Sol has the higher public score, 78.09 versus 60.3, and the 90% score intervals do not overlap. 3 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

Model A
Z.AI logo
GLM-5.2

Z.AI

60.3/100

Supported · Public rank #52

90% interval 52.2–68.5

Model B
OpenAI logo
GPT-6 Sol

OpenAI

78.09/100

Supported · Public rank #11

90% interval 74.8–81.4

Shared results
3
GLM-5.2 only
27
GPT-6 Sol only
21
Like-for-like categories
2 / 8
Supported: GLM-5.2 and GPT-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-6 Sol

    GPT-6 Sol has the higher public coding point estimate, 65.2 to 53.9, 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 Sol

    GPT-6 Sol has the higher public agentic point estimate, 57.6 to 55.6, 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 Sol

    GPT-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

    GPT-6 Sol

    GPT-6 Sol 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.

The same task catalog as the LLM Selector. Each job names the evidence surface it rests on; nothing here adds a new score.

53.9GLM-5.265.2GPT-6 Sol

Like-for-like · BenchAlign v5.8

GPT-6 Sol 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 leaderboardShortlist models for this job
Same basis rules as the category table below

What is actually comparable

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

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

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 60.7
    GLM-5.2:29.268%
    GPT-6 Sol:90.000%
    Shared source

  • FrontierMath v2 (Tiers 1-3)Math

    Normalized gap 30.6
    GLM-5.2:59.206%
    GPT-6 Sol:89.825%
    Shared 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-5.2
55.6
Supported · #42/131
GPT-6 Sol
57.6
Supported · #35/131
Basis
BenchAlign v5.8 lane · 8 vs 8 public rows
Reading
GPT-6 Sol leads · intervals overlap

Coding

Like-for-like
GLM-5.2
53.9
Supported · #36/157
GPT-6 Sol
65.2
Supported · #13/157
Basis
BenchAlign v5.8 lane · 9 vs 2 public rows
Reading
GPT-6 Sol leads · intervals overlap

Knowledge

Directional only
GLM-5.2
57.0
Supported · #56/178
GPT-6 Sol
80.0
Estimated · #9/178
Basis
BenchAlign v5.8 lane · 6 vs 5 public rows
Reading
Directional only

Reasoning

Not comparable
GLM-5.2
77.1
Unranked · 2 rankable rows
GPT-6 Sol
71.7
#12/25
Basis
Provisional lane · 0 vs 2 weighted rows
Reading
Not comparable

Multimodal

Not comparable
GLM-5.2
Not ranked
GPT-6 Sol
86.6
Unranked · 1 rankable row
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Multilingual

Not comparable
GLM-5.2
Not ranked
GPT-6 Sol
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Instruction following

Not comparable
GLM-5.2
89.9
#29/157
GPT-6 Sol
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Math

Not comparable
GLM-5.2
75.2
#3/12
GPT-6 Sol
93.8
Unranked · 2 rankable rows
Basis
Provisional lane · 4 vs 2 weighted rows
Reading
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-5.2
$0.0036
Fits in one request
GPT-6 Sol
$0.007
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-6 Sol
$0.13
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-6 Sol
$0.18
Fits in one request

GPT-6 Sol 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.

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-6 Sol

$0.2 per 1M cached input tokens

OpenAI GPT-6 Sol model documentation

Provider availability

GLM-5.2

Not sourced

GPT-6 Sol

Generally Available · OpenAI Responses API, OpenAI Chat Completions API, ChatGPT Work, Codex

OpenAI GPT-6 Sol and Luna launch

Reasoning profile

GLM-5.2

Reasoning

GPT-6 Sol

Reasoning

Weight access

GLM-5.2

Open Weight

GPT-6 Sol

Proprietary

License

GLM-5.2

Open Weight

GPT-6 Sol

Proprietary

Release date

GLM-5.2

2026-06-16

GPT-6 Sol

2026-09-22

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 Sol has the higher public score, 78.09 versus 60.3, and the 90% score intervals do not overlap.
Workload cost
Repository review: $0.0832 vs $0.13. Cache-heavy agent loop: $0.352 vs $0.18.
Context tradeoff
GPT-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-6 Sol?

GPT-6 Sol has the higher public score, 78.09 versus 60.3, 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-6 Sol?

GPT-6 Sol has the higher public coding point estimate, 65.2 to 53.9, with Supported evidence for both models. The conditional ranges do not establish rank confidence.

Which is better for agentic tasks, GLM-5.2 or GPT-6 Sol?

GPT-6 Sol has the higher public agentic tasks point estimate, 57.6 to 55.6, with Supported evidence for both models. The conditional ranges do not establish rank confidence.

Which costs less, GLM-5.2 or GPT-6 Sol?

For the stated presets, chat costs $0.0036 on GLM-5.2 and $0.007 on GPT-6 Sol; repository review costs $0.0832 and $0.13; the cache-heavy agent loop costs $0.352 and $0.18. 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-6 Sol?

GPT-6 Sol 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-5.2
API / mo$4,350
Self-host / mo$27,962
Break-even405M/day
GPT-6 Sol
API / mo$9,000
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 evidence51 rows

Agentic

  • Terminal-Bench 3.0

    GLM-5.24.6%
    Source
    GPT-6 Sol—

    Not directly comparable

  • Terminal-Bench 2.1

    GLM-5.281.0%
    Source
    GPT-6 Sol—

    Not directly comparable

  • MCP Atlas

    GLM-5.276.8%
    Source
    GPT-6 Sol—

    Not directly comparable

  • Toolathlon

    GLM-5.248.2%
    Source
    GPT-6 Sol—

    Not directly comparable

  • ResearchClawBench

    GLM-5.220.7%
    Source
    GPT-6 Sol—

    Not directly comparable

  • Terminal-Bench 2.1 (Vals)

    GLM-5.267.8%
    Source
    GPT-6 Sol—

    Not directly comparable

  • HLE w/ tools

    GLM-5.254.7%
    Source
    GPT-6 Sol—

    Not directly comparable

  • MazeBench

    GLM-5.20%
    Source
    GPT-6 Sol7%
    Source

    GPT-6 Sol leads this result

  • Agents' Last Exam

    GLM-5.2—
    GPT-6 Sol56.4%
    Source

    Not directly comparable

  • AutomationBench

    GLM-5.2—
    GPT-6 Sol33.2%
    Source

    Not directly comparable

  • OSWorld 2.0

    GLM-5.2—
    GPT-6 Sol60.5%
    Source

    Not directly comparable

  • ExploitGym

    GLM-5.2—
    GPT-6 Sol22.1%
    Source

    Not directly comparable

  • CWE-bench v1

    GLM-5.2—
    GPT-6 Sol52.0%
    Source

    Not directly comparable

  • Hermes Index

    GLM-5.2—
    GPT-6 Sol44.1%
    Source

    Not directly comparable

  • Hermes Bench

    GLM-5.2—
    GPT-6 Sol73.5%
    Source

    Not directly comparable

Coding

  • SWE-bench Pro

    GLM-5.262.1%
    Source
    GPT-6 Sol—

    Not directly comparable

  • NL2Repo

    GLM-5.248.9%
    Source
    GPT-6 Sol—

    Not directly comparable

  • Terminal-Bench 2.1

    GLM-5.281.0%
    Source
    GPT-6 Sol—

    Not directly comparable

  • ProgramBench

    GLM-5.263.7%
    Source
    GPT-6 Sol—

    Not directly comparable

  • CursorBench 3.2

    GLM-5.255.0%
    Source
    GPT-6 Sol—

    Not directly comparable

  • OpenHarmony Bench

    GLM-5.258.4%
    Source
    GPT-6 Sol—

    Not directly comparable

  • LiveCodeBench (Vals)

    GLM-5.269.5%
    Source
    GPT-6 Sol—

    Not directly comparable

  • SWE-bench (Vals)

    GLM-5.282.8%
    Source
    GPT-6 Sol—

    Not directly comparable

  • PostTrainBench v1.1

    GLM-5.231.7%
    Source
    GPT-6 Sol—

    Not directly comparable

  • DeepSWE

    GLM-5.2—
    GPT-6 Sol68.8%
    Source

    Not directly comparable

  • FrontierCode 2.0

    GLM-5.2—
    GPT-6 Sol34.3%
    Source

    Not directly comparable

Reasoning

  • CritPt

    GLM-5.220.9%
    Source
    GPT-6 Sol—

    Not directly comparable

  • ARC-AGI-1

    GLM-5.2—
    GPT-6 Sol95.50%
    Source

    Not directly comparable

  • ARC-AGI-2

    GLM-5.2—
    GPT-6 Sol89.6%
    Source

    Not directly comparable

  • ARC-AGI-3

    GLM-5.2—
    GPT-6 Sol4.6%
    Source

    Not directly comparable

  • ARC-AGI-3 Provider Adapter

    GLM-5.2—
    GPT-6 Sol23.03%
    Source

    Not directly comparable

Multimodal

  • ExtractBench (LlamaIndex)

    GLM-5.2—
    GPT-6 Sol94.57%
    Source

    Not directly comparable

  • ParseBench

    GLM-5.2—
    GPT-6 Sol68.85%
    Source

    Not directly comparable

Knowledge

  • GPQA

    GLM-5.291.2%
    Source
    GPT-6 Sol—

    Not directly comparable

  • GPQA-D

    GLM-5.291.2%
    Source
    GPT-6 Sol—

    Not directly comparable

  • HLE

    GLM-5.240.5%
    Source
    GPT-6 Sol—

    Not directly comparable

  • HLE w/o tools

    GLM-5.240.5%
    Source
    GPT-6 Sol—

    Not directly comparable

  • GPQA Diamond (Vals)

    GLM-5.285.6%
    Source
    GPT-6 Sol—

    Not directly comparable

  • MMLU-Pro (Vals)

    GLM-5.286.7%
    Source
    GPT-6 Sol—

    Not directly comparable

  • HealthBench (raw)

    GLM-5.2—
    GPT-6 Sol47.1%
    Source

    Not directly comparable

  • HealthBench (length-adjusted)

    GLM-5.2—
    GPT-6 Sol53.2%
    Source

    Not directly comparable

  • HealthBench Professional

    GLM-5.2—
    GPT-6 Sol60.8%
    Source

    Not directly comparable

  • HealthBench Professional (raw)

    GLM-5.2—
    GPT-6 Sol59.5%
    Source

    Not directly comparable

  • HealthBench Hard

    GLM-5.2—
    GPT-6 Sol30.1%
    Source

    Not directly comparable

Instruction following

  • Gray Swan IPI (15 attempts)

    GLM-5.2—
    GPT-6 Sol10.1%
    Source

    Not directly comparable

Math

  • AIME26

    GLM-5.299.2%
    Source
    GPT-6 Sol—

    Not directly comparable

  • HMMT Nov 2025

    GLM-5.294.4%
    Source
    GPT-6 Sol—

    Not directly comparable

  • HMMT Feb 2026

    GLM-5.292.5%
    Source
    GPT-6 Sol—

    Not directly comparable

  • MMAnswerBench

    GLM-5.291.0%
    Source
    GPT-6 Sol—

    Not directly comparable

  • FrontierMath v2 (Tiers 1-3)

    Shared source
    GLM-5.259.206%
    GPT-6 Sol89.825%

    GPT-6 Sol leads this result

  • FrontierMath v2 (Tier 4)

    Shared source
    GLM-5.229.268%
    GPT-6 Sol90.000%

    GPT-6 Sol leads this result

51 public results · 3 shared

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Last updated November 6, 2026