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BenchLM
Z.AI logo
Model A
GLM-4.7

Z.AI

57.82/100

Supported · Public rank #75

90% interval 44.9–70.8

GLM-4.7 vs MiniMax M2.7

Updated September 15, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

MiniMax logo
Model B
MiniMax M2.7

MiniMax

55.14/100

Supported · Public rank #95

90% interval 43.8–66.5

Decision reading

GLM-4.7 has the higher public score estimate, 57.82 versus 55.14, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.

3 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.

No workload recommendation clears the current evidence threshold.

Use the matched evidence, workload costs, and sourced specifications below instead of treating a point score as a universal answer.

Show secondary and unsupported calls

  • Coding work

    Code generation, repair, and software-engineering tasks

    Not enough matched evidence

    MiniMax M2.7 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

    GLM-4.7 and MiniMax M2.7 are scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.

    Confidence: limited

  • Long documents

    Prompts that approach the documented context limit

    No clear pick

    The documented context windows are equal.

    Confidence: documented

  • 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. MiniMax M2.7 does not fit this workload in one request. MiniMax M2.7 has no published cached-input rate, so cached tokens use its listed input rate. GLM-4.7 has no comparable published API token rate.

    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
3
GLM-4.7 only
10
MiniMax M2.7 only
20
Like-for-like categories
1 / 8

3 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.

Knowledge

Like-for-like
GLM-4.7
47.6
Supported · #95/183
MiniMax M2.7
48.7
Supported · #90/183
Basis
BenchAlign lane · 3 vs 4 public rows
Reading
MiniMax M2.7 leads · intervals overlap

Agentic

Directional only
GLM-4.7
45.3
Estimated · #84/153
MiniMax M2.7
41.1
Estimated · #110/153
Basis
BenchAlign lane · 4 vs 7 public rows
Reading
Directional only

Coding

Directional only
GLM-4.7
47.7
Supported · #73/152
MiniMax M2.7
48.6
Estimated · #68/152
Basis
BenchAlign lane · 3 vs 11 public rows
Reading
Directional only

Instruction following

Directional only
GLM-4.7
82.8
#50/123
MiniMax M2.7
93.0
#10/123
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Directional only

Reasoning

Not comparable
GLM-4.7
69.8
Unranked · 2 rankable rows
MiniMax M2.7
74.8
Unranked · 2 rankable rows
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Math

Not comparable
GLM-4.7
26.0
Unranked · 2 rankable rows
MiniMax M2.7
Not ranked
Basis
Provisional lane · 2 vs 0 weighted rows
Reading
Not comparable

Multilingual

Not comparable
GLM-4.7
Not ranked
MiniMax M2.7
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Multimodal

Not comparable
GLM-4.7
Not ranked
MiniMax M2.7
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
MiniMax M2.7
$0.0009
Fits in one request

GLM-4.7 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
MiniMax M2.7
$0.0186
Fits in one request

GLM-4.7 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
MiniMax M2.7
$0.078
Does not fit in one request
Cached input priced at the published list-input rate

GLM-4.7 does not fit this workload in one request. MiniMax M2.7 does not fit this workload in one request. MiniMax M2.7 has no published cached-input rate, so cached tokens use its listed input rate. GLM-4.7 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

MiniMax M2.7

200K

API model ID

GLM-4.7

Not sourced

MiniMax M2.7

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

MiniMax M2.7

Not published

Documented inputs

GLM-4.7

Not sourced

MiniMax M2.7

Not sourced

Documented outputs

GLM-4.7

Not sourced

MiniMax M2.7

Not sourced

Provider availability

GLM-4.7

Not sourced

MiniMax M2.7

Not sourced

Reasoning profile

GLM-4.7

Reasoning

MiniMax M2.7

Non-Reasoning

Weight access

GLM-4.7

Open Weight

MiniMax M2.7

Open Weight

License

GLM-4.7

Open Weight

MiniMax M2.7

Open Weight

Release date

GLM-4.7

2025-10-01

MiniMax M2.7

2026-03-18

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, 57.82 versus 55.14, but the 90% score intervals overlap.
Workload cost
A complete comparable API-rate estimate is not available for both models.
Context tradeoff
Both models list 200K.

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 evidence33 rows

Agentic

  • Terminal-Bench 2.0

    GLM-4.741%
    Source
    MiniMax M2.757%
    Source

    MiniMax M2.7 leads this result

  • BrowseComp

    GLM-4.752%
    Source
    MiniMax M2.7—

    Not directly comparable

  • VITA-Bench

    GLM-4.715.5%
    Source
    MiniMax M2.7—

    Not directly comparable

  • Gert Labs

    Shared source
    GLM-4.739.95%
    MiniMax M2.740.40%

    MiniMax M2.7 leads this result

  • Toolathlon

    GLM-4.7—
    MiniMax M2.746.3%
    Source

    Not directly comparable

  • MLE-Bench Lite

    GLM-4.7—
    MiniMax M2.766.6%
    Source

    Not directly comparable

  • MM-ClawBench

    GLM-4.7—
    MiniMax M2.762.7%
    Source

    Not directly comparable

  • Claw-Eval

    GLM-4.7—
    MiniMax M2.748.7%
    Source

    Not directly comparable

  • Terminal-Bench 2.1 (Vals)

    GLM-4.7—
    MiniMax M2.748.7%
    Source

    Not directly comparable

Coding

  • SWE-bench Verified

    GLM-4.773.8%
    Source
    MiniMax M2.7—

    Not directly comparable

  • LiveCodeBench

    GLM-4.784.9%
    Source
    MiniMax M2.7—

    Not directly comparable

  • SWE-Rebench

    Shared source
    GLM-4.758.7%
    MiniMax M2.751.9%

    GLM-4.7 leads this result

  • SWE-bench Verified*

    GLM-4.7—
    MiniMax M2.775.4%
    Source

    Not directly comparable

  • SWE-bench Pro

    GLM-4.7—
    MiniMax M2.756.2%
    Source

    Not directly comparable

  • SWE Multilingual

    GLM-4.7—
    MiniMax M2.776.5%
    Source

    Not directly comparable

  • Multi-SWE Bench

    GLM-4.7—
    MiniMax M2.752.7%
    Source

    Not directly comparable

  • VIBE-Pro

    GLM-4.7—
    MiniMax M2.755.6%
    Source

    Not directly comparable

  • NL2Repo

    GLM-4.7—
    MiniMax M2.739.8%
    Source

    Not directly comparable

  • Vibe Code Bench

    GLM-4.7—
    MiniMax M2.727.04%
    Source

    Not directly comparable

  • React Native Evals

    GLM-4.7—
    MiniMax M2.771.4%
    Source

    Not directly comparable

  • LiveCodeBench (Vals)

    GLM-4.7—
    MiniMax M2.779.9%
    Source

    Not directly comparable

  • SWE-bench (Vals)

    GLM-4.7—
    MiniMax M2.773.8%
    Source

    Not directly comparable

Knowledge

  • GPQA

    GLM-4.785.7%
    Source
    MiniMax M2.7—

    Not directly comparable

  • MMLU-Pro

    GLM-4.784.3%
    Source
    MiniMax M2.7—

    Not directly comparable

  • HLE

    GLM-4.724.8%
    Source
    MiniMax M2.7—

    Not directly comparable

  • GPQA-D

    GLM-4.7—
    MiniMax M2.787.0%
    Source

    Not directly comparable

  • MMLU-Pro (Arcee)

    GLM-4.7—
    MiniMax M2.780.8%
    Source

    Not directly comparable

  • GPQA Diamond (Vals)

    GLM-4.7—
    MiniMax M2.786.6%
    Source

    Not directly comparable

  • MMLU-Pro (Vals)

    GLM-4.7—
    MiniMax M2.780.4%
    Source

    Not directly comparable

Math

  • AIME 2025

    GLM-4.795.7%
    Source
    MiniMax M2.7—

    Not directly comparable

  • FrontierMath v2 (Tiers 1-3)

    GLM-4.72.439%
    Source
    MiniMax M2.7—

    Not directly comparable

  • FrontierMath v2 (Tier 4)

    GLM-4.70.000%
    Source
    MiniMax M2.7—

    Not directly comparable

  • AIME25 (Arcee)

    GLM-4.7—
    MiniMax M2.780.0%
    Source

    Not directly comparable

Frequently asked questions

Which is better, GLM-4.7 or MiniMax M2.7?

GLM-4.7 has the higher public score estimate, 57.82 versus 55.14, 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 MiniMax M2.7?

MiniMax M2.7 scores higher for coding on the public lane, 48.6 to 47.7. MiniMax M2.7 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 MiniMax M2.7?

GLM-4.7 scores higher for agentic tasks on the public lane, 45.3 to 41.1. GLM-4.7 and MiniMax M2.7 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 MiniMax M2.7?

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 MiniMax M2.7?

Both models list the same context window, 200K.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated September 15, 2026

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