Model A

Z.AI

68.19/100

Supported · Public rank #28

90% interval 61.7–74.7

GLM-5.2 vs MiniMax M3

Updated September 14, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

Model B

MiniMax M3

MiniMax

61.62/100

Supported · Public rank #55

90% interval 52.6–70.7

Decision reading

GLM-5.2 has the higher public score estimate, 68.19 versus 61.62, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.

12 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.

Agentic work
Tool use, computer use, and multi-step task completion
GLM-5.2
GLM-5.2 leads on the public agentic lane, 58.5 to 42, with Supported evidence for both models and non-overlapping 90% intervals.
Confidence: stronger
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
MiniMax M3 is scored on Estimated evidence for coding, 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

What is actually comparable

Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.

13 GLM-5.2 only

12 Shared

15 MiniMax M3 only

Shared results: 12
GLM-5.2 only: 13
MiniMax M3 only: 15
Like-for-like categories: 2 / 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.

Agentic

Like-for-like

GLM-5.2: 58.5; Supported · #29/153
MiniMax M3: 42.0; Supported · #109/153
Basis: BenchAlign lane · 6 vs 9 public rows
Reading: GLM-5.2 leads

Knowledge

Like-for-like

GLM-5.2: 60.7; Supported · #35/183
MiniMax M3: 53.2; Supported · #64/183
Basis: BenchAlign lane · 6 vs 2 public rows
Reading: GLM-5.2 leads · intervals overlap

Coding

Directional only

GLM-5.2: 61.0; Supported · #19/152
MiniMax M3: 48.9; Estimated · #67/152
Basis: BenchAlign lane · 8 vs 10 public rows
Reading: Directional only

Instruction following

Directional only

GLM-5.2: 89.8; #22/123
MiniMax M3: 93.7; #4/123
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Directional only

Reasoning

Not comparable

GLM-5.2: 74.8; Unranked · 2 rankable rows
MiniMax M3: 78.0; #4/20
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Math

Not comparable

GLM-5.2: 80.7; Unranked · 4 rankable rows
MiniMax M3: Not ranked
Basis: Provisional lane · 2 vs 1 weighted rows
Reading: Not comparable

Multilingual

Not comparable

GLM-5.2: Not ranked
MiniMax M3: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Multimodal

Not comparable

GLM-5.2: Not ranked
MiniMax M3: 52.2; #34/48
Basis: Provisional lane · 0 vs 2 weighted rows
Reading: Not comparable

Category scores with the server-provided evidence basis for GLM-5.2 and MiniMax M3
Category	GLM-5.2	MiniMax M3	Basis	Reading
Agentic	58.5Supported · #29/153	42.0Supported · #109/153	Like-for-likeBenchAlign lane · 6 vs 9 public rows	GLM-5.2 leads
Knowledge	60.7Supported · #35/183	53.2Supported · #64/183	Like-for-likeBenchAlign lane · 6 vs 2 public rows	GLM-5.2 leads · intervals overlap
Coding	61.0Supported · #19/152	48.9Estimated · #67/152	Directional onlyBenchAlign lane · 8 vs 10 public rows	Directional only
Instruction following	89.8#22/123	93.7#4/123	Directional onlyProvisional lane · 0 vs 0 weighted rows	Directional only
Reasoning	74.8Unranked · 2 rankable rows	78.0#4/20	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Math	80.7Unranked · 4 rankable rows	Not ranked	Not comparableProvisional lane · 2 vs 1 weighted rows	Not comparable
Multilingual	Not ranked	Not ranked	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Multimodal	Not ranked	52.2#34/48	Not comparableProvisional lane · 0 vs 2 weighted rows	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.

Terminal-Bench 2.0
Agentic
GLM-5.2: 81%MiniMax M3: 66%Normalized gap 15.0GLM-5.2 source MiniMax M3 source
LiveCodeBench (Vals)
Coding
GLM-5.2: 69.5%MiniMax M3: 82.2%Normalized gap 12.7GLM-5.2 source MiniMax M3 source
SWE-bench Pro
Coding
GLM-5.2: 62.1%MiniMax M3: 59%Normalized gap 3.1GLM-5.2 source MiniMax M3 source
MMLU-Pro (Vals)
Knowledge
GLM-5.2: 86.7%MiniMax M3: 84.2%Normalized gap 2.5GLM-5.2 source MiniMax M3 source

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.

SpecificationGLM-5.2MiniMax M3

Context window

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

GLM-5.2

MiniMax M3

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: GLM-5.2 has the higher public score estimate, 68.19 versus 61.62, 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 evidence40 rows

Agentic

Terminal-Bench 3.0
GLM-5.24.6%
Source
MiniMax M3—
Not directly comparable
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
Terminal-Bench 2.1 (Vals)
GLM-5.267.8%
Source
MiniMax M353.6%
Source
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
OpenHarmony Bench
Shared source
GLM-5.258.4%
MiniMax M348.4%
GLM-5.2 leads this result
LiveCodeBench (Vals)
GLM-5.269.5%
Source
MiniMax M382.2%
Source
MiniMax M3 leads this result
SWE-bench (Vals)
GLM-5.282.8%
Source
MiniMax M375.0%
Source
GLM-5.2 leads this result
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
GPQA Diamond (Vals)
GLM-5.285.6%
Source
MiniMax M392.7%
Source
MiniMax M3 leads this result
MMLU-Pro (Vals)
GLM-5.286.7%
Source
MiniMax M384.2%
Source
GLM-5.2 leads this result

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?

GLM-5.2 has the higher public score estimate, 68.19 versus 61.62, 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?

GLM-5.2 scores higher for coding on the public lane, 61 to 48.9. MiniMax M3 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-5.2 or MiniMax M3?

GLM-5.2 leads the public agentic tasks lane, 58.5 to 42, with Supported evidence for both models and non-overlapping 90% intervals.

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.

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Last updated September 14, 2026

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GLM-5.2 vs MiniMax M3

Which one for your work

Agentic work

Chat turn cost

Cache-heavy agent loop cost

Repository review cost

Coding work

Long documents

What is actually comparable

Category results, on a stated basis

Agentic

Knowledge

Coding

Instruction following

Reasoning

Math

Multilingual

Multimodal

Shape of the matched evidence

What each workload costs

Chat turn

Repository review

Cache-heavy agent loop

Specification differences

Context window

API model ID

Cached-input rate

Documented inputs

Documented outputs

Provider availability

Reasoning profile

Weight access

License

Release date

Benchmark evidence

Agentic

Coding

Reasoning

Knowledge

Math

Multimodal

Frequently asked questions

Which is better, GLM-5.2 or MiniMax M3?

Which is better for coding, GLM-5.2 or MiniMax M3?

Which is better for agentic tasks, GLM-5.2 or MiniMax M3?

Which costs less, GLM-5.2 or MiniMax M3?

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

Related comparisons

Watch GLM-5.2 vs MiniMax M3