GLM-4.6 vs MiniMax M3

Updated October 14, 2026. Rank says MiniMax M3 is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.

Decision reading

MiniMax M3 has the higher public point estimate, 55 versus 40.09. Their conditional score ranges overlap. These ranges do not establish rank confidence. 3 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-4.6

Z.AI

40.09/100

Estimated · Public rank #131

Conditional range 30.4–49.8

Model B

MiniMax M3

MiniMax

55/100

Supported · Public rank #68

90% interval 45.6–64.4

Shared results: 3
GLM-4.6 only: 1
MiniMax M3 only: 24
Like-for-like categories: 1 / 8

Estimated: GLM-4.6 · Supported: MiniMax M3. Conditional ranges do not establish rank confidence.How 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.

Long documents
Prompts that approach the documented context limit
MiniMax M3
MiniMax M3 has the larger documented context window.
Confidence: documented

Show secondary and unsupported calls

Coding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
GLM-4.6 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-4.6 is not ranked on the public lane for agentic, so no winner is named for agentic.
Confidence: limited
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-4.6 does not fit this workload in one request. GLM-4.6 has no comparable published API token rate.
Confidence: listed-rates
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.

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.

23.7GLM-4.636.5MiniMax M3

Directional only · BenchAlign v5.8

MiniMax M3 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.

MMLU-Pro (Vals)Knowledge
Normalized gap 2.0
GLM-4.6:82.2%
MiniMax M3:84.2%
GLM-4.6 source MiniMax M3 source
LiveCodeBench (Vals)Coding
Normalized gap 1.2
GLM-4.6:81.0%
MiniMax M3:82.2%
GLM-4.6 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.

Knowledge

Like-for-like

GLM-4.6: 37.1; Supported · #125/181
MiniMax M3: 49.6; Supported · #80/181
Basis: BenchAlign v5.8 lane · 2 vs 2 public rows
Reading: MiniMax M3 leads · intervals overlap

Coding

Directional only

GLM-4.6: 23.7; Estimated · #109/149
MiniMax M3: 36.5; Supported · #73/149
Basis: BenchAlign v5.8 lane · 2 vs 10 public rows
Reading: Directional only

Instruction following

Directional only

GLM-4.6: 42.1; #109/144
MiniMax M3: 93.8; #5/144
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Directional only

Agentic

Not comparable

GLM-4.6: Not ranked
MiniMax M3: 39.6; Supported · #66/125
Basis: BenchAlign v5.8 lane · 0 vs 9 public rows
Reading: Not comparable

Reasoning

Not comparable

GLM-4.6: 43.9; Unranked · 2 rankable rows
MiniMax M3: 82.9; #5/29
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Multimodal

Not comparable

GLM-4.6: Not ranked
MiniMax M3: 61.5; #43/54
Basis: Provisional lane · 0 vs 2 weighted rows
Reading: Not comparable

Multilingual

Not comparable

GLM-4.6: Not ranked
MiniMax M3: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Math

Not comparable

GLM-4.6: Not ranked
MiniMax M3: Not ranked
Basis: Provisional lane · 0 vs 1 weighted rows
Reading: Not comparable

Category scores with the server-provided evidence basis for GLM-4.6 and MiniMax M3
Category	GLM-4.6	MiniMax M3	Basis	Reading
Knowledge	37.1Supported · #125/181	49.6Supported · #80/181	Like-for-likeBenchAlign v5.8 lane · 2 vs 2 public rows	MiniMax M3 leads · intervals overlap
Coding	23.7Estimated · #109/149	36.5Supported · #73/149	Directional onlyBenchAlign v5.8 lane · 2 vs 10 public rows	Directional only
Instruction following	42.1#109/144	93.8#5/144	Directional onlyProvisional lane · 0 vs 0 weighted rows	Directional only
Agentic	Not ranked	39.6Supported · #66/125	Not comparableBenchAlign v5.8 lane · 0 vs 9 public rows	Not comparable
Reasoning	43.9Unranked · 2 rankable rows	82.9#5/29	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Multimodal	Not ranked	61.5#43/54	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	Not ranked	Not ranked	Not comparableProvisional lane · 0 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-4.6: API rate not published; Fits in one request
MiniMax M3: $0.0009; Fits in one request

GLM-4.6 has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

GLM-4.6: API rate not published; Fits in one request
MiniMax M3: $0.0186; Fits in one request

GLM-4.6 has no comparable published API token rate.

Cache-heavy agent loop

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

GLM-4.6: API rate not published; Does not fit in one request; Cached-input rate unavailable
MiniMax M3: $0.03; Fits in one request

GLM-4.6 does not fit this workload in one request. GLM-4.6 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.

SpecificationGLM-4.6MiniMax M3

Context window

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

GLM-4.6

200K

MiniMax M3

API model ID

GLM-4.6

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-4.6

No comparable hosted API rate

MiniMax M3

$0.06 per 1M cached input tokens

Documented inputs

GLM-4.6

Not sourced

MiniMax M3

Not sourced

Documented outputs

GLM-4.6

Not sourced

MiniMax M3

Not sourced

Provider availability

GLM-4.6

Not sourced

MiniMax M3

Not sourced

Reasoning profile

GLM-4.6

Reasoning

MiniMax M3

Non-Reasoning

Weight access

GLM-4.6

Open Weight

MiniMax M3

Open Weight

License

GLM-4.6

Open Weight

MiniMax M3

Open Weight

Release date

GLM-4.6

2025-09-30

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 point estimate, 55 versus 40.09. Their conditional score ranges overlap. These ranges do not establish rank confidence.
Workload cost: A complete comparable API-rate estimate is not available for both models.
Context tradeoff: MiniMax M3 has the larger documented window (1M).

Run the same representative tasks against both endpoints before changing production traffic.

Questions

Which is better, GLM-4.6 or MiniMax M3?

MiniMax M3 has the higher public point estimate, 55 versus 40.09. Their conditional score ranges overlap. These ranges do not establish rank confidence. The page therefore keeps the decision tied to the specific documented workload.

Which is better for coding, GLM-4.6 or MiniMax M3?

MiniMax M3 scores higher for coding on the public lane, 36.5 to 23.7. GLM-4.6 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-4.6 or MiniMax M3?

GLM-4.6 is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.

Which costs less, GLM-4.6 or MiniMax M3?

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-4.6 or MiniMax M3?

MiniMax M3 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 evidence28 rows

Agentic

Terminal-Bench 2.1
GLM-4.6—
MiniMax M366.0%
Source
Not directly comparable
BrowseComp
GLM-4.6—
MiniMax M383.5%
Source
Not directly comparable
OSWorld-Verified
GLM-4.6—
MiniMax M370.1%
Source
Not directly comparable
MCP Atlas
GLM-4.6—
MiniMax M374.2%
Source
Not directly comparable
Claw-Eval
GLM-4.6—
MiniMax M374.5%
Source
Not directly comparable
BankerToolBench
GLM-4.6—
MiniMax M376.1%
Source
Not directly comparable
ResearchClawBench
GLM-4.6—
MiniMax M319.8%
Source
Not directly comparable
OSWorld 2.0
GLM-4.6—
MiniMax M34.6%
Source
Not directly comparable
Terminal-Bench 2.1 (Vals)
GLM-4.6—
MiniMax M353.6%
Source
Not directly comparable

Coding

Vibe Code Bench
GLM-4.63.09%
Source
MiniMax M3—
Not directly comparable
LiveCodeBench (Vals)
GLM-4.681.0%
Source
MiniMax M382.2%
Source
MiniMax M3 leads this result
SWE-bench Verified
GLM-4.6—
MiniMax M380.5%
Source
Not directly comparable
SWE-bench Pro
GLM-4.6—
MiniMax M359%
Source
Not directly comparable
Terminal-Bench 2.1
GLM-4.6—
MiniMax M366.0%
Source
Not directly comparable
NL2Repo
GLM-4.6—
MiniMax M342.1%
Source
Not directly comparable
VIBE V2
GLM-4.6—
MiniMax M350.1%
Source
Not directly comparable
SVG-Bench
GLM-4.6—
MiniMax M363.7%
Source
Not directly comparable
KernelBench Hard
GLM-4.6—
MiniMax M328.8%
Source
Not directly comparable
OpenHarmony Bench
GLM-4.6—
MiniMax M348.4%
Source
Not directly comparable
SWE-bench (Vals)
GLM-4.6—
MiniMax M375.0%
Source
Not directly comparable

Multimodal

OfficeQA Pro
GLM-4.6—
MiniMax M345.1%
Source
Not directly comparable
OmniDocBench 1.5
GLM-4.6—
MiniMax M391.6%
Source
Not directly comparable
MMMU-Pro
GLM-4.6—
MiniMax M378.1%
Source
Not directly comparable
VideoMMMU
GLM-4.6—
MiniMax M384.6%
Source
Not directly comparable
Video-MME (with subtitle)
GLM-4.6—
MiniMax M385.4%
Source
Not directly comparable

Knowledge

GPQA Diamond (Vals)
GLM-4.674.5%
Source
MiniMax M392.7%
Source
MiniMax M3 leads this result
MMLU-Pro (Vals)
GLM-4.682.2%
Source
MiniMax M384.2%
Source
MiniMax M3 leads this result

Math

USAMO 2026
GLM-4.6—
MiniMax M385.7%
Source
Not directly comparable

28 public results · 3 shared

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Last updated October 14, 2026

GLM-4.6 vs MiniMax M3

Decision reading

Which one for your work

Long documents

Coding work

Agentic work

Chat turn cost

Cache-heavy agent loop cost

Repository review cost

Which one for a specific job

What is actually comparable

Shape of the matched evidence

Category results, on a stated basis

Knowledge

Coding

Instruction following

Agentic

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-4.6 or MiniMax M3?

Which is better for coding, GLM-4.6 or MiniMax M3?

Which is better for agentic tasks, GLM-4.6 or MiniMax M3?

Which costs less, GLM-4.6 or MiniMax M3?

Which has the larger context window, GLM-4.6 or MiniMax M3?

Benchmark evidence

Agentic

Coding

Multimodal

Knowledge

Math

Related comparisons

Watch GLM-4.6 vs MiniMax M3