GLM-5.2 vs MiniMax M3

Updated October 29, 2026. Rank says GLM-5.2 is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.

Decision reading

GLM-5.2 has the higher public score estimate, 60.03 versus 48.57, but the 90% score intervals overlap. Treat that as a lead, not a settled winner. 13 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

Model A

GLM-5.2

Z.AI

60.03/100

Supported · Public rank #52

90% interval 51.7–68.4

Model B

MiniMax M3

MiniMax

48.57/100

Supported · Public rank #104

90% interval 32.6–64.5

Shared results: 13
GLM-5.2 only: 17
MiniMax M3 only: 15
Like-for-like categories: 3 / 8

Supported: GLM-5.2 and MiniMax M3How 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-5.2
GLM-5.2 has the higher public coding point estimate, 53.7 to 37.4, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
GLM-5.2
GLM-5.2 has the higher public agentic point estimate, 57.1 to 39.3, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
Confidence: limited
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

Show secondary and unsupported calls

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
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
Long documents
Prompts that approach the documented context limit
No clear pick
The documented context windows are equal.
Confidence: documented

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.

Pick the job you would use them for

The same task catalog as the LLM Selector. Each job names the evidence surface it rests on; nothing here adds a new score.

53.7GLM-5.237.4MiniMax M3

Like-for-like · BenchAlign v5.8

GLM-5.2 has the higher coding point estimate. Conditional score ranges do not establish rank confidence.

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

Open the coding leaderboard Shortlist models for this job

Same basis rules as the category table below

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.

LiveCodeBench (Vals)Coding
Normalized gap 12.7
GLM-5.2:69.5%
MiniMax M3:82.2%
GLM-5.2 source MiniMax M3 source
SWE-bench ProCoding
Normalized gap 3.1
GLM-5.2:62.1%
MiniMax M3:59%
GLM-5.2 source MiniMax M3 source
MMLU-Pro (Vals)Knowledge
Normalized gap 2.5
GLM-5.2:86.7%
MiniMax M3:84.2%
GLM-5.2 source MiniMax M3 source

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 v5.8 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: 57.1; Supported · #38/135
MiniMax M3: 39.3; Supported · #76/135
Basis: BenchAlign v5.8 lane · 8 vs 10 public rows
Reading: GLM-5.2 leads

Coding

Like-for-like

GLM-5.2: 53.7; Supported · #34/156
MiniMax M3: 37.4; Supported · #75/156
Basis: BenchAlign v5.8 lane · 9 vs 10 public rows
Reading: GLM-5.2 leads · intervals overlap

Knowledge

Like-for-like

GLM-5.2: 56.4; Supported · #56/179
MiniMax M3: 54.3; Supported · #62/179
Basis: BenchAlign v5.8 lane · 6 vs 2 public rows
Reading: GLM-5.2 leads · intervals overlap

Instruction following

Directional only

GLM-5.2: 89.9; #29/157
MiniMax M3: 93.8; #6/157
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Directional only

Reasoning

Not comparable

GLM-5.2: 78.8; Unranked · 2 rankable rows
MiniMax M3: 82.0; Unranked · 2 rankable rows
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Multimodal

Not comparable

GLM-5.2: Not ranked
MiniMax M3: 59.0; #48/58
Basis: Provisional lane · 0 vs 2 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

Math

Not comparable

GLM-5.2: 78.4; #3/12
MiniMax M3: Not ranked
Basis: Provisional lane · 4 vs 1 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	57.1Supported · #38/135	39.3Supported · #76/135	Like-for-likeBenchAlign v5.8 lane · 8 vs 10 public rows	GLM-5.2 leads
Coding	53.7Supported · #34/156	37.4Supported · #75/156	Like-for-likeBenchAlign v5.8 lane · 9 vs 10 public rows	GLM-5.2 leads · intervals overlap
Knowledge	56.4Supported · #56/179	54.3Supported · #62/179	Like-for-likeBenchAlign v5.8 lane · 6 vs 2 public rows	GLM-5.2 leads · intervals overlap
Instruction following	89.9#29/157	93.8#6/157	Directional onlyProvisional lane · 0 vs 0 weighted rows	Directional only
Reasoning	78.8Unranked · 2 rankable rows	82.0Unranked · 2 rankable rows	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Multimodal	Not ranked	59.0#48/58	Not comparableProvisional lane · 0 vs 2 weighted rows	Not comparable
Multilingual	Not ranked	Not ranked	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Math	78.4#3/12	Not ranked	Not comparableProvisional lane · 4 vs 1 weighted rows	Not comparable

Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign v5.8) 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-5.2: $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.

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.

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, 60.03 versus 48.57, 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.

Questions

Which is better, GLM-5.2 or MiniMax M3?

GLM-5.2 has the higher public score estimate, 60.03 versus 48.57, 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 has the higher public coding point estimate, 53.7 to 37.4, with Supported evidence for both models. The conditional ranges do not establish rank confidence.

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

GLM-5.2 has the higher public agentic tasks point estimate, 57.1 to 39.3, with Supported evidence for both models. The conditional ranges do not establish rank confidence.

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.

Self-host vs API cost

Estimates at 50,000 req/day · 1000 tokens/req average.

GLM-5.2

API / mo$4,350

Self-host / mo$27,962

Break-even405M/day

MiniMax M3

API / mo$1,125

Self-host / mo$13,981

Break-even818M/day

Model the full break-even

Benchmark evidence

The full public result ledger is available for audit without forcing a wide desktop table onto a phone.

Browse raw public benchmark evidence45 rows

Agentic

Terminal-Bench 3.0
GLM-5.24.6%
Source
MiniMax M3—
Not directly comparable
Terminal-Bench 2.1
GLM-5.281.0%
Source
MiniMax M366.0%
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
HLE w/ tools
GLM-5.254.7%
Source
MiniMax M3—
Not directly comparable
MazeBench
GLM-5.20%
Source
MiniMax M30%
Source
Tie
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 2.1
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
CursorBench 3.2
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
PostTrainBench v1.1
GLM-5.231.7%
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

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

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-5.240.5%
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
FrontierMath v2 (Tiers 1-3)
GLM-5.259.206%
Source
MiniMax M3—
Not directly comparable
FrontierMath v2 (Tier 4)
GLM-5.229.268%
Source
MiniMax M3—
Not directly comparable
USAMO 2026
GLM-5.2—
MiniMax M385.7%
Source
Not directly comparable

45 public results · 13 shared

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Last updated October 29, 2026

GLM-5.2 vs MiniMax M3

Decision reading

Which one for your work

Coding work

Agentic work

Chat turn cost

Cache-heavy agent loop cost

Repository review cost

Long documents

Which one for a specific job

What is actually comparable

Shape of the matched evidence

Category results, on a stated basis

Agentic

Coding

Knowledge

Instruction following

Reasoning

Multimodal

Multilingual

Math

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

If you already use one of these models

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?

Self-host vs API cost

Benchmark evidence

Agentic

Coding

Reasoning

Multimodal

Knowledge

Math

Related comparisons

Watch GLM-5.2 vs MiniMax M3