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Model A
GLM-5.2

Z.AI

68.19/100

Supported · Public rank #28

90% interval 61.774.7

GLM-5.2 vs Hy3 Preview

Updated September 14, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

Tencent logo
Model B
Hy3 Preview

Tencent

55.49/100

Estimated · Public rank #93

90% interval 44.067.0

Decision reading

GLM-5.2 has the higher public score estimate, 68.19 versus 55.49, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.

4 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

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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

Show secondary and unsupported calls
  • Coding work

    Code generation, repair, and software-engineering tasks

    Not enough matched evidence

    Hy3 Preview 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

    Hy3 Preview is scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.

    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

    A complete comparable API-rate estimate is not available for both models.

    Confidence: rate-fallback

  • 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

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
4
GLM-5.2 only
21
Hy3 Preview only
2
Like-for-like categories
0 / 8

4 categories rest on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.

Category results, on a stated basis

Each row shows the public-lane category score for both models: the BenchAlign 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

Directional only
GLM-5.2
58.5
Supported · #29/153
Hy3 Preview
47.9
Estimated · #69/153
Basis
BenchAlign lane · 6 vs 2 public rows
Reading
Directional only

Coding

Directional only
GLM-5.2
61.0
Supported · #19/152
Hy3 Preview
44.5
Estimated · #93/152
Basis
BenchAlign lane · 8 vs 2 public rows
Reading
Directional only

Knowledge

Directional only
GLM-5.2
60.7
Supported · #35/183
Hy3 Preview
49.0
Estimated · #88/183
Basis
BenchAlign lane · 6 vs 2 public rows
Reading
Directional only

Instruction following

Directional only
GLM-5.2
89.8
#22/123
Hy3 Preview
54.8
#76/123
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Directional only

Reasoning

Not comparable
GLM-5.2
74.8
Unranked · 2 rankable rows
Hy3 Preview
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
Hy3 Preview
Not ranked
Basis
Provisional lane · 2 vs 0 weighted rows
Reading
Not comparable

Multilingual

Not comparable
GLM-5.2
Not ranked
Hy3 Preview
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Multimodal

Not comparable
GLM-5.2
Not ranked
Hy3 Preview
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign) differ from the provisional-lane categories. Unranked scores sit on the lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.

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
Hy3 Preview
Self-hosted; infrastructure cost varies
Fits in one request

Hy3 Preview has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

GLM-5.2
$0.0832
Fits in one request
Hy3 Preview
Self-hosted; infrastructure cost varies
Fits in one request

Hy3 Preview has no comparable published API token rate.

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
Hy3 Preview
Self-hosted; infrastructure cost varies
Fits in one request
Cached-input rate unavailable

GLM-5.2 has no published cached-input rate, so cached tokens use its listed input rate. Hy3 Preview has no comparable published API token 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

Hy3 Preview

256K

API model ID

GLM-5.2

Not sourced

Hy3 Preview

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

Hy3 Preview

No comparable hosted API rate

Documented inputs

GLM-5.2

Not sourced

Hy3 Preview

Not sourced

Documented outputs

GLM-5.2

Not sourced

Hy3 Preview

Not sourced

Provider availability

GLM-5.2

Not sourced

Hy3 Preview

Not sourced

Reasoning profile

GLM-5.2

Reasoning

Hy3 Preview

Reasoning

Weight access

GLM-5.2

Open Weight

Hy3 Preview

Open Weight

License

GLM-5.2

Open Weight

Hy3 Preview

Open Weight

Release date

GLM-5.2

2026-06-16

Hy3 Preview

2026-04-23

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, 68.19 versus 55.49, but the 90% score intervals overlap.
Workload cost
A complete comparable API-rate estimate is not available for both models.
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 evidence27 rows

Agentic

  • Terminal-Bench 3.0

    GLM-5.24.6%
    Source
    Hy3 Preview

    Not directly comparable

  • Terminal-Bench 2.0

    GLM-5.281%
    Source
    Hy3 Preview54.4%
    Source

    GLM-5.2 leads this result

  • MCP Atlas

    GLM-5.276.8%
    Source
    Hy3 Preview

    Not directly comparable

  • Toolathlon

    GLM-5.248.2%
    Source
    Hy3 Preview

    Not directly comparable

  • ResearchClawBench

    GLM-5.220.7%
    Source
    Hy3 Preview

    Not directly comparable

  • Terminal-Bench 2.1 (Vals)

    GLM-5.267.8%
    Source
    Hy3 Preview

    Not directly comparable

  • Gert Labs

    GLM-5.2
    Hy3 Preview36.91%
    Source

    Not directly comparable

Coding

  • SWE-bench Pro

    GLM-5.262.1%
    Source
    Hy3 Preview

    Not directly comparable

  • NL2Repo

    GLM-5.248.9%
    Source
    Hy3 Preview

    Not directly comparable

  • Terminal-Bench 2.0

    GLM-5.281.0%
    Source
    Hy3 Preview54.4%
    Source

    GLM-5.2 leads this result

  • ProgramBench

    GLM-5.263.7%
    Source
    Hy3 Preview

    Not directly comparable

  • cursorBench32

    GLM-5.255.0%
    Source
    Hy3 Preview

    Not directly comparable

  • OpenHarmony Bench

    GLM-5.258.4%
    Source
    Hy3 Preview

    Not directly comparable

  • LiveCodeBench (Vals)

    GLM-5.269.5%
    Source
    Hy3 Preview

    Not directly comparable

  • SWE-bench (Vals)

    GLM-5.282.8%
    Source
    Hy3 Preview

    Not directly comparable

  • SWE-bench Verified

    GLM-5.2
    Hy3 Preview74.4%
    Source

    Not directly comparable

Reasoning

  • CritPt

    GLM-5.220.9%
    Source
    Hy3 Preview

    Not directly comparable

Knowledge

  • GPQA

    GLM-5.291.2%
    Source
    Hy3 Preview87.2%
    Source

    GLM-5.2 leads this result

  • GPQA-D

    GLM-5.291.2%
    Source
    Hy3 Preview87.2%
    Source

    GLM-5.2 leads this result

  • HLE

    GLM-5.254.7%
    Source
    Hy3 Preview

    Not directly comparable

  • HLE w/o tools

    GLM-5.240.5%
    Source
    Hy3 Preview

    Not directly comparable

  • GPQA Diamond (Vals)

    GLM-5.285.6%
    Source
    Hy3 Preview

    Not directly comparable

  • MMLU-Pro (Vals)

    GLM-5.286.7%
    Source
    Hy3 Preview

    Not directly comparable

Math

  • AIME26

    GLM-5.299.2%
    Source
    Hy3 Preview

    Not directly comparable

  • HMMT Nov 2025

    GLM-5.294.4%
    Source
    Hy3 Preview

    Not directly comparable

  • HMMT Feb 2026

    GLM-5.292.5%
    Source
    Hy3 Preview

    Not directly comparable

  • MMAnswerBench

    GLM-5.291.0%
    Source
    Hy3 Preview

    Not directly comparable

Frequently asked questions

Which is better, GLM-5.2 or Hy3 Preview?

GLM-5.2 has the higher public score estimate, 68.19 versus 55.49, 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 Hy3 Preview?

GLM-5.2 scores higher for coding on the public lane, 61 to 44.5. Hy3 Preview 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-5.2 or Hy3 Preview?

GLM-5.2 scores higher for agentic tasks on the public lane, 58.5 to 47.9. Hy3 Preview is scored on Estimated evidence for agentic tasks, so the reading is directional rather than like-for-like. BenchLM does not name a winner for agentic tasks from a directional reading; read the shared benchmark rows directly and test the models on the same task set.

Which costs less, GLM-5.2 or Hy3 Preview?

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-5.2 or Hy3 Preview?

GLM-5.2 has the larger documented context window: 1M, compared with 256K.

Related comparisons

Last updated September 14, 2026

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