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BenchLM

GLM-4.7 vs Hy3 Preview

Updated September 23, 2026. Rank says GLM-4.7 is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.

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Decision reading

GLM-4.7 has the higher public score estimate, 50.17 versus 45.32, 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.

Model A
Z.AI logo

Z.AI

50.17/100

Supported · Public rank #78

90% interval 38.761.6

Model B
Tencent logo

Tencent

45.32/100

Estimated · Public rank #95

90% interval 33.856.8

Shared results
4
GLM-4.7 only
13
Hy3 Preview only
2
Like-for-like categories
0 / 8
Supported: GLM-4.7 · Estimated: Hy3 PreviewHow 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

    Hy3 Preview

    Hy3 Preview 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 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-4.7 does not fit this workload in one request. GLM-4.7 has no comparable published API token rate. Hy3 Preview 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.

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

41.0GLM-4.733.3Hy3 Preview

Directional only · BenchAlign v5.6

GLM-4.7 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.

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.

3 categories rest 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.

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 v5.6 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.

Coding

Directional only
GLM-4.7
41.0
Supported · #57/135
Hy3 Preview
33.3
Estimated · #85/135
Basis
BenchAlign v5.6 lane · 5 vs 2 public rows
Reading
Directional only

Knowledge

Directional only
GLM-4.7
39.8
Supported · #91/160
Hy3 Preview
39.1
Estimated · #93/160
Basis
BenchAlign v5.6 lane · 5 vs 2 public rows
Reading
Directional only

Instruction following

Directional only
GLM-4.7
81.4
#51/124
Hy3 Preview
46.9
#86/124
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Directional only

Agentic

Not comparable
GLM-4.7
27.7
Estimated · #70/105
Hy3 Preview
Not ranked
Basis
BenchAlign v5.6 lane · 4 vs 2 public rows
Reading
Not comparable

Reasoning

Not comparable
GLM-4.7
69.8
Unranked · 2 rankable rows
Hy3 Preview
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Multimodal

Not comparable
GLM-4.7
Not ranked
Hy3 Preview
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Multilingual

Not comparable
GLM-4.7
Not ranked
Hy3 Preview
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Math

Not comparable
GLM-4.7
25.8
Unranked · 2 rankable rows
Hy3 Preview
Not ranked
Basis
Provisional lane · 2 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 v5.6) 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-4.7
Self-hosted; infrastructure cost varies
Fits in one request
Hy3 Preview
Self-hosted; infrastructure cost varies
Fits in one request

GLM-4.7 has no comparable published API token rate. Hy3 Preview has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

GLM-4.7
Self-hosted; infrastructure cost varies
Fits in one request
Hy3 Preview
Self-hosted; infrastructure cost varies
Fits in one request

GLM-4.7 has no comparable published API token rate. Hy3 Preview has no comparable published API token rate.

Cache-heavy agent loop

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

GLM-4.7
Self-hosted; infrastructure cost varies
Does not fit in one request
Cached-input rate unavailable
Hy3 Preview
Self-hosted; infrastructure cost varies
Fits in one request
Cached-input rate unavailable

GLM-4.7 does not fit this workload in one request. GLM-4.7 has no comparable published API token rate. Hy3 Preview 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.

Context window

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

GLM-4.7

200K

Hy3 Preview

256K

API model ID

GLM-4.7

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-4.7

No comparable hosted API rate

Hy3 Preview

No comparable hosted API rate

Documented inputs

GLM-4.7

Not sourced

Hy3 Preview

Not sourced

Documented outputs

GLM-4.7

Not sourced

Hy3 Preview

Not sourced

Provider availability

GLM-4.7

Not sourced

Hy3 Preview

Not sourced

Reasoning profile

GLM-4.7

Reasoning

Hy3 Preview

Reasoning

Weight access

GLM-4.7

Open Weight

Hy3 Preview

Open Weight

License

GLM-4.7

Open Weight

Hy3 Preview

Open Weight

Release date

GLM-4.7

2025-10-01

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-4.7 has the higher public score estimate, 50.17 versus 45.32, but the 90% score intervals overlap.
Workload cost
A complete comparable API-rate estimate is not available for both models.
Context tradeoff
Hy3 Preview has the larger documented window (256K).
Run the same representative tasks against both endpoints before changing production traffic.

Questions

Which is better, GLM-4.7 or Hy3 Preview?

GLM-4.7 has the higher public score estimate, 50.17 versus 45.32, 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-4.7 or Hy3 Preview?

GLM-4.7 scores higher for coding on the public lane, 41 to 33.3. 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-4.7 or Hy3 Preview?

Hy3 Preview is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.

Which costs less, GLM-4.7 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-4.7 or Hy3 Preview?

Hy3 Preview has the larger documented context window: 256K, 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 evidence19 rows

Agentic

  • Terminal-Bench 2.0

    GLM-4.741%
    Source
    Hy3 Preview54.4%
    Source

    Hy3 Preview leads this result

  • BrowseComp

    GLM-4.752%
    Source
    Hy3 Preview

    Not directly comparable

  • VITA-Bench

    GLM-4.715.5%
    Source
    Hy3 Preview

    Not directly comparable

  • GLM-4.739.95%
    Hy3 Preview36.91%

    GLM-4.7 leads this result

Coding

  • SWE-bench Verified

    GLM-4.773.8%
    Source
    Hy3 Preview74.4%
    Source

    Hy3 Preview leads this result

  • LiveCodeBench

    GLM-4.784.9%
    Source
    Hy3 Preview

    Not directly comparable

  • SWE-Rebench

    GLM-4.758.7%
    Source
    Hy3 Preview

    Not directly comparable

  • LiveCodeBench (Vals)

    GLM-4.782.2%
    Source
    Hy3 Preview

    Not directly comparable

  • SWE-bench (Vals)

    GLM-4.769.4%
    Source
    Hy3 Preview

    Not directly comparable

  • Terminal-Bench 2.0

    GLM-4.7
    Hy3 Preview54.4%
    Source

    Not directly comparable

Knowledge

  • GPQA

    GLM-4.785.7%
    Source
    Hy3 Preview87.2%
    Source

    Hy3 Preview leads this result

  • MMLU-Pro

    GLM-4.784.3%
    Source
    Hy3 Preview

    Not directly comparable

  • HLE

    GLM-4.724.8%
    Source
    Hy3 Preview

    Not directly comparable

  • GPQA Diamond (Vals)

    GLM-4.780.1%
    Source
    Hy3 Preview

    Not directly comparable

  • MMLU-Pro (Vals)

    GLM-4.782.7%
    Source
    Hy3 Preview

    Not directly comparable

  • GPQA-D

    GLM-4.7
    Hy3 Preview87.2%
    Source

    Not directly comparable

Math

  • AIME 2025

    GLM-4.795.7%
    Source
    Hy3 Preview

    Not directly comparable

  • FrontierMath v2 (Tiers 1-3)

    GLM-4.72.439%
    Source
    Hy3 Preview

    Not directly comparable

  • FrontierMath v2 (Tier 4)

    GLM-4.70.000%
    Source
    Hy3 Preview

    Not directly comparable

19 public results · 4 shared

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Last updated September 23, 2026