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Model comparison

GLM-5.2 vs GPT-5.2

Updated July 31, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

21 confirmed releases in the last 30 daystrack changes
GLM-5.2

Z.AI

62.9/100

Estimated · Public rank #41

90% interval 47.7–78.2

GPT-5.2

OpenAI

57.6/100

Estimated · Public rank #72

90% interval 49.3–65.9

GLM-5.2 has the higher public score estimate, 62.94 versus 57.61, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.

2 results are shared. Category rows based on different benchmark sets are marked directional and do not name a winner.

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

    GLM-5.2

    GLM-5.2 has the larger documented context window.

    Confidence: documented

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

    Confidence: rate-fallback

Show secondary and unsupported calls

  • 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

  • Coding work

    Code generation, repair, and software-engineering tasks

    Not enough matched evidence

    The category averages use different weighted benchmark sets, so they are directional rather than like-for-like.

    Confidence: limited

  • Agentic work

    Tool use, computer use, and multi-step task completion

    Not enough matched evidence

    No shared weighted benchmark basis supports a winner.

    Confidence: limited

What is actually comparable

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

Shared results
2
GLM-5.2 only
16
GPT-5.2 only
13
Like-for-like categories
0 / 8

2 categories use different evidence sets. Those rows remain visible for coverage context but do not name a winner.

Category results, on a stated basis

Each row states whether both averages use the same weighted benchmark set. Directional and not-comparable rows remain visible, but they never receive a winner in this template.

Coding

Directional only
GLM-5.2
62.1
GPT-5.2
70.6
Weighted basis
1 vs 2 rows
Reading
Directional only

Knowledge

Directional only
GLM-5.2
59.6
GPT-5.2
92.4
Weighted basis
2 vs 1 rows
Reading
Directional only

Agentic

Not comparable
GLM-5.2
81.0
GPT-5.2
55.7
Weighted basis
1 vs 2 rows
Reading
Not comparable

Reasoning

Not comparable
GLM-5.2
Not measured
GPT-5.2
52.9
Weighted basis
0 vs 1 rows
Reading
Not comparable

Math

Not comparable
GLM-5.2
95.9
GPT-5.2
35.2
Weighted basis
2 vs 2 rows
Reading
Not comparable

Multilingual

Not comparable
GLM-5.2
Not measured
GPT-5.2
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multimodal

Not comparable
GLM-5.2
Not measured
GPT-5.2
80.4
Weighted basis
0 vs 2 rows
Reading
Not comparable

Instruction following

Not comparable
GLM-5.2
Not measured
GPT-5.2
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

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.

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-5.2
$0.00875
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-5.2
$0.1295
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-5.2
$0.525
Fits in one request
Cached input priced at the published list-input rate

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

Specification differences

Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.

Context window

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

GLM-5.2

1M

GPT-5.2

400K

API model ID

GLM-5.2

Not sourced

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-5.2

Not published

GPT-5.2

Not published

Documented inputs

GLM-5.2

Not sourced

GPT-5.2

Not sourced

Documented outputs

GLM-5.2

Not sourced

GPT-5.2

Not sourced

Provider availability

GLM-5.2

Not sourced

GPT-5.2

Not sourced

Reasoning profile

GLM-5.2

Reasoning

GPT-5.2

Reasoning

Weight access

GLM-5.2

Open Weight

GPT-5.2

Proprietary

License

GLM-5.2

Open Weight

GPT-5.2

Proprietary

Release date

GLM-5.2

2026-06-16

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-5.2 has the higher public score estimate, 62.94 versus 57.61, but the 90% score intervals overlap.
Workload cost
Repository review: $0.0832 vs $0.1295. Cache-heavy agent loop: $0.352 vs $0.525.
Context tradeoff
GLM-5.2 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 evidence31 rows

Agentic

  • Terminal-Bench 2.0

    GLM-5.281%
    Source
    GPT-5.2—

    Not directly comparable

  • MCP Atlas

    GLM-5.276.8%
    Source
    GPT-5.2—

    Not directly comparable

  • Toolathlon

    GLM-5.248.2%
    Source
    GPT-5.2—

    Not directly comparable

  • ResearchClawBench

    GLM-5.220.7%
    Source
    GPT-5.2—

    Not directly comparable

  • BrowseComp

    GLM-5.2—
    GPT-5.265.8%
    Source

    Not directly comparable

  • OSWorld-Verified

    GLM-5.2—
    GPT-5.247.3%
    Source

    Not directly comparable

  • Gert Labs

    GLM-5.2—
    GPT-5.246.54%
    Source

    Not directly comparable

  • JobBench

    GLM-5.2—
    GPT-5.234.3%
    Source

    Not directly comparable

Coding

  • SWE-bench Pro

    GLM-5.262.1%
    Source
    GPT-5.255.6%
    Source

    GLM-5.2 leads this result

  • NL2Repo

    GLM-5.248.9%
    Source
    GPT-5.2—

    Not directly comparable

  • Terminal-Bench 2.0

    GLM-5.281.0%
    Source
    GPT-5.2—

    Not directly comparable

  • ProgramBench

    GLM-5.263.7%
    Source
    GPT-5.2—

    Not directly comparable

  • cursorBench32

    GLM-5.255.0%
    Source
    GPT-5.2—

    Not directly comparable

  • SWE-bench Verified

    GLM-5.2—
    GPT-5.280%
    Source

    Not directly comparable

  • Vibe Code Bench

    GLM-5.2—
    GPT-5.253.50%
    Source

    Not directly comparable

Reasoning

  • CritPt

    GLM-5.220.9%
    Source
    GPT-5.2—

    Not directly comparable

  • ARC-AGI-2

    GLM-5.2—
    GPT-5.252.9%
    Source

    Not directly comparable

Knowledge

  • GPQA

    GLM-5.291.2%
    Source
    GPT-5.292.4%
    Source

    GPT-5.2 leads this result

  • GPQA-D

    GLM-5.291.2%
    Source
    GPT-5.2—

    Not directly comparable

  • HLE

    GLM-5.254.7%
    Source
    GPT-5.2—

    Not directly comparable

  • HLE w/o tools

    GLM-5.240.5%
    Source
    GPT-5.2—

    Not directly comparable

Math

  • AIME26

    GLM-5.299.2%
    Source
    GPT-5.2—

    Not directly comparable

  • HMMT Nov 2025

    GLM-5.294.4%
    Source
    GPT-5.2—

    Not directly comparable

  • HMMT Feb 2026

    GLM-5.292.5%
    Source
    GPT-5.2—

    Not directly comparable

  • MMAnswerBench

    GLM-5.291.0%
    Source
    GPT-5.2—

    Not directly comparable

  • FrontierMath v2 (Tiers 1-3)

    GLM-5.2—
    GPT-5.240.700%
    Source

    Not directly comparable

  • FrontierMath v2 (Tier 4)

    GLM-5.2—
    GPT-5.218.800%
    Source

    Not directly comparable

Multimodal

  • MMMU-Pro

    GLM-5.2—
    GPT-5.279.5%
    Source

    Not directly comparable

  • MathVision

    GLM-5.2—
    GPT-5.283.0%
    Source

    Not directly comparable

  • CharXiv

    GLM-5.2—
    GPT-5.282.1%
    Source

    Not directly comparable

  • V*

    GLM-5.2—
    GPT-5.275.9%
    Source

    Not directly comparable

Frequently asked questions

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

GLM-5.2 has the higher public score estimate, 62.94 versus 57.61, 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-5.2 or GPT-5.2?

The current coding averages use different weighted benchmark sets, so BenchLM does not name a winner from them. Read the shared benchmark rows directly and test the models on the same task set.

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

The published evidence does not provide a shared weighted agentic tasks basis for both models, so BenchLM does not name a agentic tasks winner.

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

For the stated presets, chat costs $0.0036 on GLM-5.2 and $0.00875 on GPT-5.2; repository review costs $0.0832 and $0.1295; the cache-heavy agent loop costs $0.352 and $0.525. GLM-5.2 has no published cached-input rate, so cached tokens use its listed input rate. GPT-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-5.2?

GLM-5.2 has the larger documented context window: 1M, compared with 400K.

Related comparisons

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Last updated July 31, 2026

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