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Model comparison

GLM-5.2 vs MiniMax M3

Updated July 31, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

21 confirmed releases in the last 30 daystrack changes
GLM-5.2

Z.AI

62.9/100

Estimated · Public rank #41

90% interval 47.7–78.2

MiniMax M3

MiniMax

68.8/100

Supported · Public rank #18

90% interval 64.2–73.4

MiniMax M3 has the higher public score estimate, 68.76 versus 62.94, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.

6 results are shared. Category rows based on different benchmark sets are marked directional and do not name a winner.

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.

  • Chat turn cost

    1K fresh input + 500 output tokens

    MiniMax M3

    MiniMax M3 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.

    Confidence: listed-rates

  • Cache-heavy agent loop cost

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

    MiniMax M3

    MiniMax M3 has the lower estimated token cost for this stated workload. GLM-5.2 has no published cached-input rate, so cached tokens use its listed input rate.

    Confidence: rate-fallback

Show secondary and unsupported calls

  • Repository review cost

    50K fresh input + 3K output tokens

    MiniMax M3

    MiniMax M3 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.

    Confidence: listed-rates

  • Coding work

    Code generation, repair, and software-engineering tasks

    Not enough matched evidence

    The category averages use different weighted benchmark sets, so they are directional rather than like-for-like.

    Confidence: limited

  • Agentic work

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

    Not enough matched evidence

    The category averages use different weighted benchmark sets, so they are 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

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
6
GLM-5.2 only
12
MiniMax M3 only
15
Like-for-like categories
0 / 8

2 categories use different evidence sets. Those rows remain visible for coverage context but do not name a winner.

Category results, on a stated basis

Each row states whether both averages use the same weighted benchmark set. Directional and not-comparable rows remain visible, but they never receive a winner in this template.

Agentic

Directional only
GLM-5.2
81.0
MiniMax M3
72.3
Weighted basis
1 vs 3 rows
Reading
Directional only

Coding

Directional only
GLM-5.2
62.1
MiniMax M3
72.2
Weighted basis
1 vs 2 rows
Reading
Directional only

Reasoning

Not comparable
GLM-5.2
Not measured
MiniMax M3
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Knowledge

Not comparable
GLM-5.2
59.6
MiniMax M3
Not measured
Weighted basis
2 vs 0 rows
Reading
Not comparable

Math

Not comparable
GLM-5.2
95.9
MiniMax M3
85.7
Weighted basis
2 vs 1 rows
Reading
Not comparable

Multilingual

Not comparable
GLM-5.2
Not measured
MiniMax M3
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multimodal

Not comparable
GLM-5.2
Not measured
MiniMax M3
64.9
Weighted basis
0 vs 2 rows
Reading
Not comparable

Instruction following

Not comparable
GLM-5.2
Not measured
MiniMax M3
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

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
MiniMax M3
$0.0009
Fits in one request

MiniMax M3 has the lower modeled cost

Costs use the listed standard API rates.

Repository review

50K fresh input + 3K output tokens

GLM-5.2
$0.0832
Fits in one request
MiniMax M3
$0.0186
Fits in one request

MiniMax M3 has the lower modeled cost

Costs use the listed standard API rates.

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
MiniMax M3
$0.03
Fits in one request

MiniMax M3 has the lower modeled cost

GLM-5.2 has no published cached-input rate, so cached tokens use its listed input 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

MiniMax M3

1M

API model ID

GLM-5.2

Not sourced

MiniMax M3

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

MiniMax M3

$0.06 per 1M cached input tokens

Documented inputs

GLM-5.2

Not sourced

MiniMax M3

Not sourced

Documented outputs

GLM-5.2

Not sourced

MiniMax M3

Not sourced

Provider availability

GLM-5.2

Not sourced

MiniMax M3

Not sourced

Reasoning profile

GLM-5.2

Reasoning

MiniMax M3

Non-Reasoning

Weight access

GLM-5.2

Open Weight

MiniMax M3

Open Weight

License

GLM-5.2

Open Weight

MiniMax M3

Open Weight

Release date

GLM-5.2

2026-06-16

MiniMax M3

2026-06-01

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
MiniMax M3 has the higher public score estimate, 68.76 versus 62.94, but the 90% score intervals overlap.
Workload cost
Repository review: $0.0832 vs $0.0186. Cache-heavy agent loop: $0.352 vs $0.03.
Context tradeoff
Both models list 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 evidence33 rows

Agentic

  • Terminal-Bench 2.0

    GLM-5.281%
    Source
    MiniMax M366%
    Source

    GLM-5.2 leads this result

  • MCP Atlas

    GLM-5.276.8%
    Source
    MiniMax M374.2%
    Source

    GLM-5.2 leads this result

  • Toolathlon

    GLM-5.248.2%
    Source
    MiniMax M3—

    Not directly comparable

  • ResearchClawBench

    Shared source
    GLM-5.220.7%
    MiniMax M319.8%

    GLM-5.2 leads this result

  • BrowseComp

    GLM-5.2—
    MiniMax M383.5%
    Source

    Not directly comparable

  • OSWorld-Verified

    GLM-5.2—
    MiniMax M370.1%
    Source

    Not directly comparable

  • Claw-Eval

    GLM-5.2—
    MiniMax M374.5%
    Source

    Not directly comparable

  • BankerToolBench

    GLM-5.2—
    MiniMax M376.1%
    Source

    Not directly comparable

  • OSWorld 2.0

    GLM-5.2—
    MiniMax M34.6%
    Source

    Not directly comparable

Coding

  • SWE-bench Pro

    GLM-5.262.1%
    Source
    MiniMax M359%
    Source

    GLM-5.2 leads this result

  • NL2Repo

    GLM-5.248.9%
    Source
    MiniMax M342.1%
    Source

    GLM-5.2 leads this result

  • Terminal-Bench 2.0

    GLM-5.281.0%
    Source
    MiniMax M366.0%
    Source

    GLM-5.2 leads this result

  • ProgramBench

    GLM-5.263.7%
    Source
    MiniMax M3—

    Not directly comparable

  • cursorBench32

    GLM-5.255.0%
    Source
    MiniMax M3—

    Not directly comparable

  • SWE-bench Verified

    GLM-5.2—
    MiniMax M380.5%
    Source

    Not directly comparable

  • VIBE V2

    GLM-5.2—
    MiniMax M350.1%
    Source

    Not directly comparable

  • SVG-Bench

    GLM-5.2—
    MiniMax M363.7%
    Source

    Not directly comparable

  • KernelBench Hard

    GLM-5.2—
    MiniMax M328.8%
    Source

    Not directly comparable

Reasoning

  • CritPt

    GLM-5.220.9%
    Source
    MiniMax M3—

    Not directly comparable

Knowledge

  • GPQA

    GLM-5.291.2%
    Source
    MiniMax M3—

    Not directly comparable

  • GPQA-D

    GLM-5.291.2%
    Source
    MiniMax M3—

    Not directly comparable

  • HLE

    GLM-5.254.7%
    Source
    MiniMax M3—

    Not directly comparable

  • HLE w/o tools

    GLM-5.240.5%
    Source
    MiniMax M3—

    Not directly comparable

Math

  • AIME26

    GLM-5.299.2%
    Source
    MiniMax M3—

    Not directly comparable

  • HMMT Nov 2025

    GLM-5.294.4%
    Source
    MiniMax M3—

    Not directly comparable

  • HMMT Feb 2026

    GLM-5.292.5%
    Source
    MiniMax M3—

    Not directly comparable

  • MMAnswerBench

    GLM-5.291.0%
    Source
    MiniMax M3—

    Not directly comparable

  • USAMO 2026

    GLM-5.2—
    MiniMax M385.7%
    Source

    Not directly comparable

Multimodal

  • OfficeQA Pro

    GLM-5.2—
    MiniMax M345.1%
    Source

    Not directly comparable

  • OmniDocBench 1.5

    GLM-5.2—
    MiniMax M391.6%
    Source

    Not directly comparable

  • MMMU-Pro

    GLM-5.2—
    MiniMax M378.1%
    Source

    Not directly comparable

  • VideoMMMU

    GLM-5.2—
    MiniMax M384.6%
    Source

    Not directly comparable

  • Video-MME (with subtitle)

    GLM-5.2—
    MiniMax M385.4%
    Source

    Not directly comparable

Frequently asked questions

Which is better, GLM-5.2 or MiniMax M3?

MiniMax M3 has the higher public score estimate, 68.76 versus 62.94, 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 MiniMax M3?

The current coding averages use different weighted benchmark sets, so BenchLM does not name a winner from them. 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 MiniMax M3?

The current agentic tasks averages use different weighted benchmark sets, so BenchLM does not name a winner from them. Read the shared benchmark rows directly and test the models on the same task set.

Which costs less, GLM-5.2 or MiniMax M3?

For the stated presets, chat costs $0.0036 on GLM-5.2 and $0.0009 on MiniMax M3; repository review costs $0.0832 and $0.0186; the cache-heavy agent loop costs $0.352 and $0.03. GLM-5.2 has no published cached-input rate, so cached tokens use its listed input rate.

Which has the larger context window, GLM-5.2 or MiniMax M3?

Both models list the same context window, 1M.

Related comparisons

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Last updated July 31, 2026

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