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BenchLM

GLM-5.3 vs MiniMax M2.7

Updated September 28, 2026. Rank says GLM-5.3 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-5.3 has the higher public score, 65.44 versus 48.03, and the 90% score intervals do not overlap. 6 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
GLM-5.3

Z.AI

65.44/100

Estimated · Public rank #27

90% interval 59.7–71.2

Model B
MiniMax logo
MiniMax M2.7

MiniMax

48.03/100

Supported · Public rank #84

90% interval 37.2–58.9

Shared results
6
GLM-5.3 only
18
MiniMax M2.7 only
17
Like-for-like categories
3 / 8
Estimated: GLM-5.3 · Supported: MiniMax M2.7How 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.

  • Coding work

    Code generation, repair, and software-engineering tasks

    GLM-5.3

    GLM-5.3 leads on the public coding lane, 56.7 to 35.9, with Supported evidence for both models and non-overlapping 90% intervals.

    Confidence: stronger

  • Agentic work

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

    GLM-5.3

    GLM-5.3 leads on the public agentic lane, 67.3 to 28.9, with Supported evidence for both models and non-overlapping 90% intervals.

    Confidence: stronger

  • Long documents

    Prompts that approach the documented context limit

    GLM-5.3

    GLM-5.3 has the larger documented context window.

    Confidence: documented
Show secondary and unsupported calls

  • 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. 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-5.3 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

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.

56.7GLM-5.335.9MiniMax M2.7

Like-for-like · BenchAlign v5.7

GLM-5.3 leads the like-for-like coding row.

Coding scores combine specific tasks and setups. Match the editor, harness, and effort to your workflow.

Open the coding leaderboardShortlist models for this job
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.

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 v5.7 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

Like-for-like
GLM-5.3
67.3
Supported · #9/111
MiniMax M2.7
28.9
Supported · #77/111
Basis
BenchAlign v5.7 lane · 9 vs 7 public rows
Reading
GLM-5.3 leads

Coding

Like-for-like
GLM-5.3
56.7
Supported · #22/136
MiniMax M2.7
35.9
Supported · #70/136
Basis
BenchAlign v5.7 lane · 13 vs 11 public rows
Reading
GLM-5.3 leads

Knowledge

Like-for-like
GLM-5.3
61.9
Supported · #33/160
MiniMax M2.7
43.1
Supported · #81/160
Basis
BenchAlign v5.7 lane · 2 vs 4 public rows
Reading
GLM-5.3 leads · intervals overlap

Reasoning

Not comparable
GLM-5.3
77.1
#11/27
MiniMax M2.7
76.1
Unranked · 2 rankable rows
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Multimodal

Not comparable
GLM-5.3
Not ranked
MiniMax M2.7
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Multilingual

Not comparable
GLM-5.3
Not ranked
MiniMax M2.7
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Instruction following

Not comparable
GLM-5.3
Not ranked
MiniMax M2.7
91.6
#10/124
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Math

Not comparable
GLM-5.3
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 v5.7) 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-5.3
Self-hosted; infrastructure cost varies
Fits in one request
MiniMax M2.7
$0.0009
Fits in one request

GLM-5.3 has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

GLM-5.3
Self-hosted; infrastructure cost varies
Fits in one request
MiniMax M2.7
$0.0186
Fits in one request

GLM-5.3 has no comparable published API token rate.

Cache-heavy agent loop

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

GLM-5.3
Self-hosted; infrastructure cost varies
Fits 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

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-5.3 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-5.3

1M

Z.AI GLM-5.3 model card

MiniMax M2.7

200K

Cached-input rate

A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.

GLM-5.3

No comparable hosted API rate

Z.AI GLM-5.3 model card

MiniMax M2.7

Not published

Documented inputs

GLM-5.3

Not sourced

MiniMax M2.7

Not sourced

Documented outputs

GLM-5.3

Not sourced

MiniMax M2.7

Not sourced

Provider availability

GLM-5.3

Not sourced

MiniMax M2.7

Not sourced

Reasoning profile

GLM-5.3

Reasoning

MiniMax M2.7

Non-Reasoning

Weight access

GLM-5.3

Open Weight

MiniMax M2.7

Open Weight

License

GLM-5.3

Open Weight

MiniMax M2.7

Open Weight

Release date

GLM-5.3

2026-08-14

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-5.3 has the higher public score, 65.44 versus 48.03, and the 90% score intervals do not overlap.
Workload cost
A complete comparable API-rate estimate is not available for both models.
Context tradeoff
GLM-5.3 has the larger documented window (1M).
Run the same representative tasks against both endpoints before changing production traffic.

Questions

Which is better, GLM-5.3 or MiniMax M2.7?

GLM-5.3 has the higher public score, 65.44 versus 48.03, 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-5.3 or MiniMax M2.7?

GLM-5.3 leads the public coding lane, 56.7 to 35.9, with Supported evidence for both models and non-overlapping 90% intervals.

Which is better for agentic tasks, GLM-5.3 or MiniMax M2.7?

GLM-5.3 leads the public agentic tasks lane, 67.3 to 28.9, with Supported evidence for both models and non-overlapping 90% intervals.

Which costs less, GLM-5.3 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-5.3 or MiniMax M2.7?

GLM-5.3 has the larger documented context window: 1M, 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 evidence41 rows

Agentic

  • Terminal-Bench 2.1

    GLM-5.388.2%
    Source
    MiniMax M2.7—

    Not directly comparable

  • terminalBench3

    GLM-5.328.3%
    Source
    MiniMax M2.7—

    Not directly comparable

  • CyberGym

    GLM-5.384.5%
    Source
    MiniMax M2.7—

    Not directly comparable

  • ExploitGym

    GLM-5.315.0%
    Source
    MiniMax M2.7—

    Not directly comparable

  • Toolathlon-Verified

    GLM-5.373.0%
    Source
    MiniMax M2.7—

    Not directly comparable

  • AutomationBench

    GLM-5.348.2%
    Source
    MiniMax M2.7—

    Not directly comparable

  • Agents' Last Exam

    GLM-5.328.5%
    Source
    MiniMax M2.7—

    Not directly comparable

  • HLE w/ tools

    GLM-5.362.5%
    Source
    MiniMax M2.7—

    Not directly comparable

  • Terminal-Bench 2.1 (Vals)

    GLM-5.371.5%
    Source
    MiniMax M2.748.7%
    Source

    GLM-5.3 leads this result

  • Terminal-Bench 2.0

    GLM-5.3—
    MiniMax M2.757%
    Source

    Not directly comparable

  • Toolathlon

    GLM-5.3—
    MiniMax M2.746.3%
    Source

    Not directly comparable

  • MLE-Bench Lite

    GLM-5.3—
    MiniMax M2.766.6%
    Source

    Not directly comparable

  • MM-ClawBench

    GLM-5.3—
    MiniMax M2.762.7%
    Source

    Not directly comparable

  • Claw-Eval

    GLM-5.3—
    MiniMax M2.748.7%
    Source

    Not directly comparable

  • Gert Labs

    GLM-5.3—
    MiniMax M2.740.40%
    Source

    Not directly comparable

Coding

  • Terminal-Bench 2.1

    GLM-5.388.2%
    Source
    MiniMax M2.7—

    Not directly comparable

  • terminalBench3

    GLM-5.328.3%
    Source
    MiniMax M2.7—

    Not directly comparable

  • DeepSWE

    GLM-5.366.9%
    Source
    MiniMax M2.7—

    Not directly comparable

  • NL2Repo

    GLM-5.358%
    Source
    MiniMax M2.739.8%
    Source

    GLM-5.3 leads this result

  • ProgramBench

    GLM-5.319.0%
    Source
    MiniMax M2.7—

    Not directly comparable

  • FrontierSWE

    GLM-5.378.1%
    Source
    MiniMax M2.7—

    Not directly comparable

  • sweMarathon

    GLM-5.342.5%
    Source
    MiniMax M2.7—

    Not directly comparable

  • PostTrain Bench

    GLM-5.339.8%
    Source
    MiniMax M2.7—

    Not directly comparable

  • VulcanBench v3

    GLM-5.378.3%
    Source
    MiniMax M2.7—

    Not directly comparable

  • OpenHarmony Bench

    GLM-5.360.8%
    Source
    MiniMax M2.7—

    Not directly comparable

  • FrontierSWE v2

    GLM-5.330.2%
    Source
    MiniMax M2.7—

    Not directly comparable

  • LiveCodeBench (Vals)

    GLM-5.380.5%
    Source
    MiniMax M2.779.9%
    Source

    GLM-5.3 leads this result

  • SWE-bench (Vals)

    GLM-5.395.4%
    Source
    MiniMax M2.773.8%
    Source

    GLM-5.3 leads this result

  • SWE-bench Verified*

    GLM-5.3—
    MiniMax M2.775.4%
    Source

    Not directly comparable

  • SWE-bench Pro

    GLM-5.3—
    MiniMax M2.756.2%
    Source

    Not directly comparable

  • SWE-Rebench

    GLM-5.3—
    MiniMax M2.751.9%
    Source

    Not directly comparable

  • SWE Multilingual

    GLM-5.3—
    MiniMax M2.776.5%
    Source

    Not directly comparable

  • Multi-SWE Bench

    GLM-5.3—
    MiniMax M2.752.7%
    Source

    Not directly comparable

  • VIBE-Pro

    GLM-5.3—
    MiniMax M2.755.6%
    Source

    Not directly comparable

  • Vibe Code Bench

    GLM-5.3—
    MiniMax M2.727.04%
    Source

    Not directly comparable

  • React Native Evals

    GLM-5.3—
    MiniMax M2.771.4%
    Source

    Not directly comparable

Knowledge

  • GPQA Diamond (Vals)

    GLM-5.388.1%
    Source
    MiniMax M2.786.6%
    Source

    GLM-5.3 leads this result

  • MMLU-Pro (Vals)

    GLM-5.386.8%
    Source
    MiniMax M2.780.4%
    Source

    GLM-5.3 leads this result

  • GPQA-D

    GLM-5.3—
    MiniMax M2.787.0%
    Source

    Not directly comparable

  • MMLU-Pro (Arcee)

    GLM-5.3—
    MiniMax M2.780.8%
    Source

    Not directly comparable

Math

  • AIME25 (Arcee)

    GLM-5.3—
    MiniMax M2.780.0%
    Source

    Not directly comparable

41 public results · 6 shared

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Last updated September 28, 2026