Skip to main content
Radar

Keep up with the models you depend on. Follow price changes, retirements, and API updates.Follow the models you depend on.

Follow model changes
BenchLM
Z.AI logo
Model A
GLM-4.7

Z.AI

57.71/100

Supported · Public rank #76

90% interval 44.6–70.8

GLM-4.7 vs Laguna M.1

Updated September 10, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

Poolside logo
Model B
Laguna M.1

Poolside

3.93/100

Estimated · Public rank #250

90% interval 0.0–13.8

Decision reading

GLM-4.7 has the higher public score, 57.71 versus 3.93, and the 90% score intervals do not overlap.

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

Share or export

Share on XLinkedInSocial cardCSVJSON

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

    GLM-4.7

    GLM-4.7 leads on the public coding lane, 47.7 to 29.7, with Supported evidence for both models and non-overlapping 90% intervals.

    Confidence: stronger

  • Long documents

    Prompts that approach the documented context limit

    Laguna M.1

    Laguna M.1 has the larger documented context window.

    Confidence: documented

Show secondary and unsupported calls

  • Agentic work

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

    Not enough matched evidence

    GLM-4.7 and Laguna M.1 are 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

    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. Laguna M.1 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

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-4.7 only
11
Laguna M.1 only
8
Like-for-like categories
1 / 8

2 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.

Coding

Like-for-like
GLM-4.7
47.7
Supported · #71/151
Laguna M.1
29.7
Supported · #140/151
Basis
BenchAlign lane · 3 vs 6 public rows
Reading
GLM-4.7 leads

Agentic

Directional only
GLM-4.7
45.3
Estimated · #82/152
Laguna M.1
23.9
Estimated · #147/152
Basis
BenchAlign lane · 4 vs 2 public rows
Reading
Directional only

Knowledge

Directional only
GLM-4.7
47.6
Supported · #95/183
Laguna M.1
18.6
Estimated · #182/183
Basis
BenchAlign lane · 3 vs 2 public rows
Reading
Directional only

Reasoning

Not comparable
GLM-4.7
69.8
Unranked · 2 rankable rows
Laguna M.1
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Math

Not comparable
GLM-4.7
26.0
Unranked · 2 rankable rows
Laguna M.1
Not ranked
Basis
Provisional lane · 2 vs 0 weighted rows
Reading
Not comparable

Multilingual

Not comparable
GLM-4.7
Not ranked
Laguna M.1
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Multimodal

Not comparable
GLM-4.7
Not ranked
Laguna M.1
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Instruction following

Not comparable
GLM-4.7
82.8
#50/123
Laguna M.1
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-4.7
Self-hosted; infrastructure cost varies
Fits in one request
Laguna M.1
API rate not published
Fits in one request

GLM-4.7 has no comparable published API token rate. Laguna M.1 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
Laguna M.1
API rate not published
Fits in one request

GLM-4.7 has no comparable published API token rate. Laguna M.1 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
Laguna M.1
API rate not published
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. Laguna M.1 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-4.7

200K

Laguna M.1

256K

API model ID

GLM-4.7

Not sourced

Laguna M.1

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

Laguna M.1

No comparable hosted API rate

Documented inputs

GLM-4.7

Not sourced

Laguna M.1

Not sourced

Documented outputs

GLM-4.7

Not sourced

Laguna M.1

Not sourced

Provider availability

GLM-4.7

Not sourced

Laguna M.1

Not sourced

Reasoning profile

GLM-4.7

Reasoning

Laguna M.1

Reasoning

Weight access

GLM-4.7

Open Weight

Laguna M.1

Proprietary

License

GLM-4.7

Open Weight

Laguna M.1

Proprietary

Release date

GLM-4.7

2025-10-01

Laguna M.1

2026-04-28

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, 57.71 versus 3.93, and the 90% score intervals do not overlap.
Workload cost
A complete comparable API-rate estimate is not available for both models.
Context tradeoff
Laguna M.1 has the larger documented window (256K).

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 evidence21 rows

Agentic

  • Terminal-Bench 2.0

    GLM-4.741%
    Source
    Laguna M.145.8%
    Source

    Laguna M.1 leads this result

  • BrowseComp

    GLM-4.752%
    Source
    Laguna M.1—

    Not directly comparable

  • VITA-Bench

    GLM-4.715.5%
    Source
    Laguna M.1—

    Not directly comparable

  • Gert Labs

    GLM-4.739.95%
    Source
    Laguna M.1—

    Not directly comparable

  • Terminal-Bench 2.1 (Vals)

    GLM-4.7—
    Laguna M.134.1%
    Source

    Not directly comparable

Coding

  • SWE-bench Verified

    GLM-4.773.8%
    Source
    Laguna M.174.6%
    Source

    Laguna M.1 leads this result

  • LiveCodeBench

    GLM-4.784.9%
    Source
    Laguna M.1—

    Not directly comparable

  • SWE-Rebench

    GLM-4.758.7%
    Source
    Laguna M.1—

    Not directly comparable

  • SWE Multilingual

    GLM-4.7—
    Laguna M.163.1%
    Source

    Not directly comparable

  • SWE-bench Pro

    GLM-4.7—
    Laguna M.149.2%
    Source

    Not directly comparable

  • Terminal-Bench 2.0

    GLM-4.7—
    Laguna M.145.8%
    Source

    Not directly comparable

  • LiveCodeBench (Vals)

    GLM-4.7—
    Laguna M.168.1%
    Source

    Not directly comparable

  • SWE-bench (Vals)

    GLM-4.7—
    Laguna M.157.6%
    Source

    Not directly comparable

Knowledge

  • GPQA

    GLM-4.785.7%
    Source
    Laguna M.1—

    Not directly comparable

  • MMLU-Pro

    GLM-4.784.3%
    Source
    Laguna M.1—

    Not directly comparable

  • HLE

    GLM-4.724.8%
    Source
    Laguna M.1—

    Not directly comparable

  • GPQA Diamond (Vals)

    GLM-4.7—
    Laguna M.127.0%
    Source

    Not directly comparable

  • MMLU-Pro (Vals)

    GLM-4.7—
    Laguna M.168.8%
    Source

    Not directly comparable

Math

  • AIME 2025

    GLM-4.795.7%
    Source
    Laguna M.1—

    Not directly comparable

  • FrontierMath v2 (Tiers 1-3)

    GLM-4.72.439%
    Source
    Laguna M.1—

    Not directly comparable

  • FrontierMath v2 (Tier 4)

    GLM-4.70.000%
    Source
    Laguna M.1—

    Not directly comparable

Frequently asked questions

Which is better, GLM-4.7 or Laguna M.1?

GLM-4.7 has the higher public score, 57.71 versus 3.93, 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-4.7 or Laguna M.1?

GLM-4.7 leads the public coding lane, 47.7 to 29.7, with Supported evidence for both models and non-overlapping 90% intervals.

Which is better for agentic tasks, GLM-4.7 or Laguna M.1?

GLM-4.7 scores higher for agentic tasks on the public lane, 45.3 to 23.9. GLM-4.7 and Laguna M.1 are 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-4.7 or Laguna M.1?

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 Laguna M.1?

Laguna M.1 has the larger documented context window: 256K, compared with 200K.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated September 10, 2026

Watch GLM-4.7 vs Laguna M.1

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

Read a sample issue

Join 2,000+ readers.