GLM-5 vs MiniMax M3

Updated October 14, 2026. Rank cannot separate these two. Price, access, and your workload decide. Public scores include evidence status and uncertainty.

Decision reading

MiniMax M3 has the higher public score estimate, 55 versus 54.83, but the 90% score intervals overlap. Treat that as a lead, not a settled winner. 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

GLM-5

Z.AI

54.83/100

Supported · Public rank #69

90% interval 44.1–65.5

Model B

MiniMax M3

MiniMax

55/100

Supported · Public rank #68

90% interval 45.6–64.4

Shared results: 6
GLM-5 only: 32
MiniMax M3 only: 21
Like-for-like categories: 0 / 8

Supported: GLM-5 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.

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

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
GLM-5 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-5 is scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.
Confidence: limited
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-5 does not fit this workload in one request. GLM-5 has no published cached-input rate, so cached tokens use its listed input rate.
Confidence: rate-fallback

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.

38.0GLM-536.5MiniMax M3

Directional only · BenchAlign v5.8

GLM-5 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.

BrowseCompAgentic
Normalized gap 21.5
GLM-5:62%
MiniMax M3:83.5%
GLM-5 source MiniMax M3 source
SWE-bench ProCoding
Normalized gap 3.9
GLM-5:55.1%
MiniMax M3:59%
GLM-5 source MiniMax M3 source
SWE-bench VerifiedCoding
Normalized gap 2.7
GLM-5:77.8%
MiniMax M3:80.5%
GLM-5 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

Directional only

GLM-5: 42.0; Estimated · #61/125
MiniMax M3: 39.6; Supported · #66/125
Basis: BenchAlign v5.8 lane · 13 vs 9 public rows
Reading: Directional only

Coding

Directional only

GLM-5: 38.0; Estimated · #69/149
MiniMax M3: 36.5; Supported · #73/149
Basis: BenchAlign v5.8 lane · 8 vs 10 public rows
Reading: Directional only

Knowledge

Directional only

GLM-5: 50.3; Estimated · #78/181
MiniMax M3: 49.6; Supported · #80/181
Basis: BenchAlign v5.8 lane · 6 vs 2 public rows
Reading: Directional only

Instruction following

Directional only

GLM-5: 88.6; #34/144
MiniMax M3: 93.8; #5/144
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Directional only

Reasoning

Not comparable

GLM-5: 70.9; Unranked · 4 rankable rows
MiniMax M3: 82.9; #5/29
Basis: Provisional lane · 1 vs 0 weighted rows
Reading: Not comparable

Multimodal

Not comparable

GLM-5: Not ranked
MiniMax M3: 61.5; #43/54
Basis: Provisional lane · 0 vs 2 weighted rows
Reading: Not comparable

Multilingual

Not comparable

GLM-5: 89.5; #6/17
MiniMax M3: Not ranked
Basis: Provisional lane · 1 vs 0 weighted rows
Reading: Not comparable

Math

Not comparable

GLM-5: 83.0; Unranked · 6 rankable rows
MiniMax M3: Not ranked
Basis: Provisional lane · 2 vs 1 weighted rows
Reading: Not comparable

Category scores with the server-provided evidence basis for GLM-5 and MiniMax M3
Category	GLM-5	MiniMax M3	Basis	Reading
Agentic	42.0Estimated · #61/125	39.6Supported · #66/125	Directional onlyBenchAlign v5.8 lane · 13 vs 9 public rows	Directional only
Coding	38.0Estimated · #69/149	36.5Supported · #73/149	Directional onlyBenchAlign v5.8 lane · 8 vs 10 public rows	Directional only
Knowledge	50.3Estimated · #78/181	49.6Supported · #80/181	Directional onlyBenchAlign v5.8 lane · 6 vs 2 public rows	Directional only
Instruction following	88.6#34/144	93.8#5/144	Directional onlyProvisional lane · 0 vs 0 weighted rows	Directional only
Reasoning	70.9Unranked · 4 rankable rows	82.9#5/29	Not comparableProvisional lane · 1 vs 0 weighted rows	Not comparable
Multimodal	Not ranked	61.5#43/54	Not comparableProvisional lane · 0 vs 2 weighted rows	Not comparable
Multilingual	89.5#6/17	Not ranked	Not comparableProvisional lane · 1 vs 0 weighted rows	Not comparable
Math	83.0Unranked · 6 rankable rows	Not ranked	Not comparableProvisional lane · 2 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: $0.0026; 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: $0.0596; 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: $0.252; Does not fit in one request; Cached input priced at the published list-input rate
MiniMax M3: $0.03; Fits in one request

GLM-5 does not fit this workload in one request. GLM-5 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-5MiniMax M3

Context window

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

GLM-5

200K

MiniMax M3

API model ID

GLM-5

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

Not published

MiniMax M3

$0.06 per 1M cached input tokens

Documented inputs

GLM-5

Not sourced

MiniMax M3

Not sourced

Documented outputs

GLM-5

Not sourced

MiniMax M3

Not sourced

Provider availability

GLM-5

Not sourced

MiniMax M3

Not sourced

Reasoning profile

GLM-5

Non-Reasoning

MiniMax M3

Non-Reasoning

Weight access

GLM-5

Open Weight

MiniMax M3

Open Weight

License

GLM-5

Open Weight

MiniMax M3

Open Weight

Release date

GLM-5

2026-02-12

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, 55 versus 54.83, but the 90% score intervals overlap.
Workload cost: Repository review: $0.0596 vs $0.0186. Cache-heavy agent loop: $0.252 vs $0.03.
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-5 or MiniMax M3?

MiniMax M3 has the higher public score estimate, 55 versus 54.83, 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 or MiniMax M3?

GLM-5 scores higher for coding on the public lane, 38 to 36.5. GLM-5 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 or MiniMax M3?

GLM-5 scores higher for agentic tasks on the public lane, 42 to 39.6. GLM-5 is scored on Estimated evidence for agentic tasks, so the reading is directional rather than like-for-like. BenchLM does not name a winner for agentic tasks from a directional reading; read the shared benchmark rows directly and test the models on the same task set.

Which costs less, GLM-5 or MiniMax M3?

For the stated presets, chat costs $0.0026 on GLM-5 and $0.0009 on MiniMax M3; repository review costs $0.0596 and $0.0186; the cache-heavy agent loop costs $0.252 and $0.03. GLM-5 does not fit this workload in one request. GLM-5 has no published cached-input rate, so cached tokens use its listed input rate.

Which has the larger context window, GLM-5 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 evidence59 rows

Agentic

Terminal-Bench 2.0
GLM-556.2%
Source
MiniMax M3—
Not directly comparable
BrowseComp
GLM-562%
Source
MiniMax M383.5%
Source
MiniMax M3 leads this result
HLE w/ tools
GLM-550.4%
Source
MiniMax M3—
Not directly comparable
Claw-Eval
GLM-557.7%
Source
MiniMax M374.5%
Source
MiniMax M3 leads this result
QwenClawBench
GLM-554.1%
Source
MiniMax M3—
Not directly comparable
τ³-bench results
GLM-565.6%
Source
MiniMax M3—
Not directly comparable
DeepPlanning
GLM-514.6%
Source
MiniMax M3—
Not directly comparable
Toolathlon
GLM-538%
Source
MiniMax M3—
Not directly comparable
MCP Atlas
GLM-531.1%
Source
MiniMax M374.2%
Source
MiniMax M3 leads this result
MCP-Tasks
GLM-560.8%
Source
MiniMax M3—
Not directly comparable
WideResearch
GLM-569.8%
Source
MiniMax M3—
Not directly comparable
CyberGym
GLM-543.2%
Source
MiniMax M3—
Not directly comparable
Gert Labs
GLM-550.99%
Source
MiniMax M3—
Not directly comparable
Terminal-Bench 2.1
GLM-5—
MiniMax M366.0%
Source
Not directly comparable
OSWorld-Verified
GLM-5—
MiniMax M370.1%
Source
Not directly comparable
BankerToolBench
GLM-5—
MiniMax M376.1%
Source
Not directly comparable
ResearchClawBench
GLM-5—
MiniMax M319.8%
Source
Not directly comparable
OSWorld 2.0
GLM-5—
MiniMax M34.6%
Source
Not directly comparable
Terminal-Bench 2.1 (Vals)
GLM-5—
MiniMax M353.6%
Source
Not directly comparable

Coding

SWE-bench Verified
GLM-577.8%
Source
MiniMax M380.5%
Source
MiniMax M3 leads this result
SWE-bench Verified*
GLM-572.8%
Source
MiniMax M3—
Not directly comparable
SWE-bench Pro
GLM-555.1%
Source
MiniMax M359%
Source
MiniMax M3 leads this result
SWE Multilingual
GLM-573.3%
Source
MiniMax M3—
Not directly comparable
NL2Repo
GLM-535.9%
Source
MiniMax M342.1%
Source
MiniMax M3 leads this result
SWE-Rebench
GLM-562.8%
Source
MiniMax M3—
Not directly comparable
React Native Evals
GLM-574.8%
Source
MiniMax M3—
Not directly comparable
Terminal-Bench 2.0
GLM-556.2%
Source
MiniMax M3—
Not directly comparable
Terminal-Bench 2.1
GLM-5—
MiniMax M366.0%
Source
Not directly comparable
VIBE V2
GLM-5—
MiniMax M350.1%
Source
Not directly comparable
SVG-Bench
GLM-5—
MiniMax M363.7%
Source
Not directly comparable
KernelBench Hard
GLM-5—
MiniMax M328.8%
Source
Not directly comparable
OpenHarmony Bench
GLM-5—
MiniMax M348.4%
Source
Not directly comparable
LiveCodeBench (Vals)
GLM-5—
MiniMax M382.2%
Source
Not directly comparable
SWE-bench (Vals)
GLM-5—
MiniMax M375.0%
Source
Not directly comparable

Reasoning

LongBench v2
GLM-560.8%
Source
MiniMax M3—
Not directly comparable
AI-Needle
GLM-563.3%
Source
MiniMax M3—
Not directly comparable

Multimodal

OfficeQA Pro
GLM-5—
MiniMax M345.1%
Source
Not directly comparable
OmniDocBench 1.5
GLM-5—
MiniMax M391.6%
Source
Not directly comparable
MMMU-Pro
GLM-5—
MiniMax M378.1%
Source
Not directly comparable
VideoMMMU
GLM-5—
MiniMax M384.6%
Source
Not directly comparable
Video-MME (with subtitle)
GLM-5—
MiniMax M385.4%
Source
Not directly comparable

Knowledge

GPQA
GLM-586%
Source
MiniMax M3—
Not directly comparable
GPQA-D
GLM-586.0%
Source
MiniMax M3—
Not directly comparable
SuperGPQA
GLM-566.8%
Source
MiniMax M3—
Not directly comparable
MMLU-Pro
GLM-585.7%
Source
MiniMax M3—
Not directly comparable
MMLU-Pro (Arcee)
GLM-585.8%
Source
MiniMax M3—
Not directly comparable
HLE
GLM-530.5%
Source
MiniMax M3—
Not directly comparable
GPQA Diamond (Vals)
GLM-5—
MiniMax M392.7%
Source
Not directly comparable
MMLU-Pro (Vals)
GLM-5—
MiniMax M384.2%
Source
Not directly comparable

Multilingual

MMLU-ProX
GLM-583.1%
Source
MiniMax M3—
Not directly comparable
NOVA-63
GLM-555.1%
Source
MiniMax M3—
Not directly comparable

Instruction following

IFEval
GLM-592.6%
Source
MiniMax M3—
Not directly comparable

Math

AIME26
GLM-595.8%
Source
MiniMax M3—
Not directly comparable
AIME25 (Arcee)
GLM-593.3%
Source
MiniMax M3—
Not directly comparable
HMMT Feb 2025
GLM-597.5%
Source
MiniMax M3—
Not directly comparable
HMMT Nov 2025
GLM-596.9%
Source
MiniMax M3—
Not directly comparable
HMMT Feb 2026
GLM-586.4%
Source
MiniMax M3—
Not directly comparable
MMAnswerBench
GLM-582.5%
Source
MiniMax M3—
Not directly comparable
USAMO 2026
GLM-5—
MiniMax M385.7%
Source
Not directly comparable

59 public results · 6 shared

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Last updated October 14, 2026

GLM-5 vs MiniMax M3

Decision reading

Which one for your work

Long documents

Chat turn cost

Repository review cost

Coding work

Agentic work

Cache-heavy agent loop cost

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 or MiniMax M3?

Which is better for coding, GLM-5 or MiniMax M3?

Which is better for agentic tasks, GLM-5 or MiniMax M3?

Which costs less, GLM-5 or MiniMax M3?

Which has the larger context window, GLM-5 or MiniMax M3?

Benchmark evidence

Agentic

Coding

Reasoning

Multimodal

Knowledge

Multilingual

Instruction following

Math

Related comparisons

Watch GLM-5 vs MiniMax M3