Granite-4.0-H-1B 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

The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. Use the documented cost, context, and runtime rows instead. 0 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

Model A

Granite-4.0-H-1B

IBM

28.88/100

Estimated · Public rank #179

Conditional range 14.5–43.2

Model B

MiniMax M3

MiniMax

55/100

Supported · Public rank #68

90% interval 45.6–64.4

Shared results: 0
Granite-4.0-H-1B only: 7
MiniMax M3 only: 27
Like-for-like categories: 1 / 8

Estimated: Granite-4.0-H-1B · 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
Granite-4.0-H-1B is not ranked on the public lane for coding, so no winner is named for coding.
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
Granite-4.0-H-1B 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. Granite-4.0-H-1B does not fit this workload in one request. Granite-4.0-H-1B 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.

—Granite-4.0-H-1B36.5MiniMax M3

Not comparable · BenchAlign v5.8

The coding row is not comparable on the public lane: at least one model is not measured or not ranked there.

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.

A shared-evidence shape is not available.

BenchLM does not draw a radar or infer missing axes when the matched evidence is too sparse.

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

Granite-4.0-H-1B: 19.6; Supported · #177/181
MiniMax M3: 49.6; Supported · #80/181
Basis: BenchAlign v5.8 lane · 3 vs 2 public rows
Reading: MiniMax M3 leads

Instruction following

Directional only

Granite-4.0-H-1B: 28.8; #133/144
MiniMax M3: 93.8; #5/144
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Directional only

Agentic

Not comparable

Granite-4.0-H-1B: Not ranked
MiniMax M3: 39.6; Supported · #66/125
Basis: BenchAlign v5.8 lane · 0 vs 9 public rows
Reading: Not comparable

Coding

Not comparable

Granite-4.0-H-1B: Not ranked
MiniMax M3: 36.5; Supported · #73/149
Basis: BenchAlign v5.8 lane · 1 vs 10 public rows
Reading: Not comparable

Reasoning

Not comparable

Granite-4.0-H-1B: 30.7; Unranked · 3 rankable rows
MiniMax M3: 82.9; #5/29
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Multimodal

Not comparable

Granite-4.0-H-1B: Not ranked
MiniMax M3: 61.5; #43/54
Basis: Provisional lane · 0 vs 2 weighted rows
Reading: Not comparable

Multilingual

Not comparable

Granite-4.0-H-1B: Not ranked
MiniMax M3: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Math

Not comparable

Granite-4.0-H-1B: 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 Granite-4.0-H-1B and MiniMax M3
Category	Granite-4.0-H-1B	MiniMax M3	Basis	Reading
Knowledge	19.6Supported · #177/181	49.6Supported · #80/181	Like-for-likeBenchAlign v5.8 lane · 3 vs 2 public rows	MiniMax M3 leads
Instruction following	28.8#133/144	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
Coding	Not ranked	36.5Supported · #73/149	Not comparableBenchAlign v5.8 lane · 1 vs 10 public rows	Not comparable
Reasoning	30.7Unranked · 3 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

Granite-4.0-H-1B: Self-hosted; infrastructure cost varies; Fits in one request
MiniMax M3: $0.0009; Fits in one request

Granite-4.0-H-1B has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

Granite-4.0-H-1B: Self-hosted; infrastructure cost varies; Fits in one request
MiniMax M3: $0.0186; Fits in one request

Granite-4.0-H-1B has no comparable published API token rate.

Cache-heavy agent loop

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

Granite-4.0-H-1B: Self-hosted; infrastructure cost varies; Does not fit in one request; Cached-input rate unavailable
MiniMax M3: $0.03; Fits in one request

Granite-4.0-H-1B does not fit this workload in one request. Granite-4.0-H-1B 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.

SpecificationGranite-4.0-H-1BMiniMax M3

Context window

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

Granite-4.0-H-1B

128K

MiniMax M3

API model ID

Granite-4.0-H-1B

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.

Granite-4.0-H-1B

No comparable hosted API rate

MiniMax M3

$0.06 per 1M cached input tokens

Documented inputs

Granite-4.0-H-1B

Not sourced

MiniMax M3

Not sourced

Documented outputs

Granite-4.0-H-1B

Not sourced

MiniMax M3

Not sourced

Provider availability

Granite-4.0-H-1B

Not sourced

MiniMax M3

Not sourced

Reasoning profile

Granite-4.0-H-1B

Non-Reasoning

MiniMax M3

Non-Reasoning

Weight access

Granite-4.0-H-1B

Open Weight

MiniMax M3

Open Weight

License

Granite-4.0-H-1B

Open Weight

MiniMax M3

Open Weight

Release date

Granite-4.0-H-1B

2025-10-28

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: The public evidence has no benchmark result shared by both models, so it does not support a quality verdict.
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, Granite-4.0-H-1B or MiniMax M3?

The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. The page therefore keeps the decision tied to the specific documented workload.

Which is better for coding, Granite-4.0-H-1B or MiniMax M3?

Granite-4.0-H-1B is not ranked on the public lane for coding, so no winner is named for coding.

Which is better for agentic tasks, Granite-4.0-H-1B or MiniMax M3?

Granite-4.0-H-1B is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.

Which costs less, Granite-4.0-H-1B 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, Granite-4.0-H-1B or MiniMax M3?

MiniMax M3 has the larger documented context window: 1M, compared with 128K.

Benchmark evidence

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

Browse raw public benchmark evidence34 rows

Agentic

Terminal-Bench 2.1
Granite-4.0-H-1B—
MiniMax M366.0%
Source
Not directly comparable
BrowseComp
Granite-4.0-H-1B—
MiniMax M383.5%
Source
Not directly comparable
OSWorld-Verified
Granite-4.0-H-1B—
MiniMax M370.1%
Source
Not directly comparable
MCP Atlas
Granite-4.0-H-1B—
MiniMax M374.2%
Source
Not directly comparable
Claw-Eval
Granite-4.0-H-1B—
MiniMax M374.5%
Source
Not directly comparable
BankerToolBench
Granite-4.0-H-1B—
MiniMax M376.1%
Source
Not directly comparable
ResearchClawBench
Granite-4.0-H-1B—
MiniMax M319.8%
Source
Not directly comparable
OSWorld 2.0
Granite-4.0-H-1B—
MiniMax M34.6%
Source
Not directly comparable
Terminal-Bench 2.1 (Vals)
Granite-4.0-H-1B—
MiniMax M353.6%
Source
Not directly comparable

Coding

HumanEval
Granite-4.0-H-1B73%
Source
MiniMax M3—
Not directly comparable
SWE-bench Verified
Granite-4.0-H-1B—
MiniMax M380.5%
Source
Not directly comparable
SWE-bench Pro
Granite-4.0-H-1B—
MiniMax M359%
Source
Not directly comparable
Terminal-Bench 2.1
Granite-4.0-H-1B—
MiniMax M366.0%
Source
Not directly comparable
NL2Repo
Granite-4.0-H-1B—
MiniMax M342.1%
Source
Not directly comparable
VIBE V2
Granite-4.0-H-1B—
MiniMax M350.1%
Source
Not directly comparable
SVG-Bench
Granite-4.0-H-1B—
MiniMax M363.7%
Source
Not directly comparable
KernelBench Hard
Granite-4.0-H-1B—
MiniMax M328.8%
Source
Not directly comparable
OpenHarmony Bench
Granite-4.0-H-1B—
MiniMax M348.4%
Source
Not directly comparable
LiveCodeBench (Vals)
Granite-4.0-H-1B—
MiniMax M382.2%
Source
Not directly comparable
SWE-bench (Vals)
Granite-4.0-H-1B—
MiniMax M375.0%
Source
Not directly comparable

Reasoning

BBH
Granite-4.0-H-1B59.7%
Source
MiniMax M3—
Not directly comparable

Multimodal

OfficeQA Pro
Granite-4.0-H-1B—
MiniMax M345.1%
Source
Not directly comparable
OmniDocBench 1.5
Granite-4.0-H-1B—
MiniMax M391.6%
Source
Not directly comparable
MMMU-Pro
Granite-4.0-H-1B—
MiniMax M378.1%
Source
Not directly comparable
VideoMMMU
Granite-4.0-H-1B—
MiniMax M384.6%
Source
Not directly comparable
Video-MME (with subtitle)
Granite-4.0-H-1B—
MiniMax M385.4%
Source
Not directly comparable

Knowledge

MMLU
Granite-4.0-H-1B59.7%
Source
MiniMax M3—
Not directly comparable
GPQA
Granite-4.0-H-1B29.7%
Source
MiniMax M3—
Not directly comparable
MMLU-Pro
Granite-4.0-H-1B32.9%
Source
MiniMax M3—
Not directly comparable
GPQA Diamond (Vals)
Granite-4.0-H-1B—
MiniMax M392.7%
Source
Not directly comparable
MMLU-Pro (Vals)
Granite-4.0-H-1B—
MiniMax M384.2%
Source
Not directly comparable

Multilingual

MGSM
Granite-4.0-H-1B27.5%
Source
MiniMax M3—
Not directly comparable

Instruction following

IFEval
Granite-4.0-H-1B78.5%
Source
MiniMax M3—
Not directly comparable

Math

USAMO 2026
Granite-4.0-H-1B—
MiniMax M385.7%
Source
Not directly comparable

34 public results · 0 shared

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Last updated October 14, 2026

Granite-4.0-H-1B 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

Instruction following

Agentic

Coding

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, Granite-4.0-H-1B or MiniMax M3?

Which is better for coding, Granite-4.0-H-1B or MiniMax M3?

Which is better for agentic tasks, Granite-4.0-H-1B or MiniMax M3?

Which costs less, Granite-4.0-H-1B or MiniMax M3?

Which has the larger context window, Granite-4.0-H-1B or MiniMax M3?

Benchmark evidence

Agentic

Coding

Reasoning

Multimodal

Knowledge

Multilingual

Instruction following

Math

Related comparisons

Watch Granite-4.0-H-1B vs MiniMax M3