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BenchLM
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Hy4 vs MiniMax M3

Updated November 4, 2026. We do not rank this pair: at least one has no public score. Public scores include evidence status and uncertainty.

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Decision reading

At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner. 1 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

Model A
Tencent logo
Hy4

Tencent

—

Evidence status unavailable

90% interval unavailable

Model B
MiniMax logo
MiniMax M3

MiniMax

48.57/100

Supported · Public rank #104

90% interval 32.6–64.5

Shared results
1
Hy4 only
14
MiniMax M3 only
27
Like-for-like categories
0 / 8
Supported: 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.

  • 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. 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

    Hy4 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

    Hy4 is not ranked on the public lane for agentic, so no winner is named for agentic.

    Confidence: limited

  • 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.

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

—Hy437.4MiniMax 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 leaderboardShortlist models for this job
Same basis rules as the category table below

What is actually comparable

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

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.

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

Not comparable
Hy4
Not ranked
MiniMax M3
39.3
Supported · #76/135
Basis
BenchAlign v5.8 lane · 7 vs 10 public rows
Reading
Not comparable

Coding

Not comparable
Hy4
Not ranked
MiniMax M3
37.4
Supported · #75/156
Basis
BenchAlign v5.8 lane · 5 vs 10 public rows
Reading
Not comparable

Reasoning

Not comparable
Hy4
Not ranked
MiniMax M3
82.0
Unranked · 2 rankable rows
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Multimodal

Not comparable
Hy4
Not ranked
MiniMax M3
59.0
#48/58
Basis
Provisional lane · 1 vs 2 weighted rows
Reading
Not comparable

Knowledge

Not comparable
Hy4
Not ranked
MiniMax M3
54.3
Supported · #62/179
Basis
BenchAlign v5.8 lane · 1 vs 2 public rows
Reading
Not comparable

Multilingual

Not comparable
Hy4
Not ranked
MiniMax M3
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Instruction following

Not comparable
Hy4
Not ranked
MiniMax M3
93.8
#6/157
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Math

Not comparable
Hy4
Not ranked
MiniMax M3
Not ranked
Basis
Provisional lane · 0 vs 1 weighted rows
Reading
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

Hy4
$0.00208
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

Hy4
$0.0492
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

Hy4
$0.05009
Fits in one request
MiniMax M3
$0.03
Fits in one request

MiniMax M3 has the lower modeled cost

Costs use the listed standard API rates.

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.

Cached-input rate

A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.

Hy4

$0.042 per 1M cached input tokens

Tencent Cloud TokenHub model pricing

MiniMax M3

$0.06 per 1M cached input tokens

Documented inputs

Hy4

Not sourced

MiniMax M3

Not sourced

Documented outputs

Hy4

Not sourced

MiniMax M3

Not sourced

Provider availability

Hy4

Not sourced

MiniMax M3

Not sourced

Reasoning profile

Hy4

Reasoning

MiniMax M3

Non-Reasoning

Weight access

Hy4

Pending

MiniMax M3

Open Weight

License

Hy4

Pending

MiniMax M3

Open Weight

Release date

Hy4

2026-11-04

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
At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner.
Workload cost
Repository review: $0.0492 vs $0.0186. Cache-heavy agent loop: $0.05009 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, Hy4 or MiniMax M3?

At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner. The page therefore keeps the decision tied to the specific documented workload.

Which is better for coding, Hy4 or MiniMax M3?

Hy4 is not ranked on the public lane for coding, so no winner is named for coding.

Which is better for agentic tasks, Hy4 or MiniMax M3?

Hy4 is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.

Which costs less, Hy4 or MiniMax M3?

For the stated presets, chat costs $0.00209 on Hy4 and $0.0009 on MiniMax M3; repository review costs $0.0492 and $0.0186; the cache-heavy agent loop costs $0.05009 and $0.03. Costs use the listed standard API rates.

Which has the larger context window, Hy4 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.

Hy4
API / mo$2,501
Self-host / moNot listed
Break-even—
Proprietary model — self-hosting not applicable.
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 evidence42 rows

Agentic

  • Terminal-Bench 4.0

    Hy444.40%
    Source
    MiniMax M3—

    Not directly comparable

  • Terminal-Bench-Science 0.1

    Hy419.8%
    Source
    MiniMax M3—

    Not directly comparable

  • HLE w/ tools

    Hy459.1%
    Source
    MiniMax M3—

    Not directly comparable

  • Agents' Last Exam

    Hy441.0%
    Source
    MiniMax M3—

    Not directly comparable

  • APEX-Agents

    Hy475.2%
    Source
    MiniMax M3—

    Not directly comparable

  • AutomationBench

    Hy456.2%
    Source
    MiniMax M3—

    Not directly comparable

  • OSWorld 2.1

    Hy478.9%
    Source
    MiniMax M3—

    Not directly comparable

  • Terminal-Bench 2.1

    Hy4—
    MiniMax M366.0%
    Source

    Not directly comparable

  • BrowseComp

    Hy4—
    MiniMax M383.5%
    Source

    Not directly comparable

  • OSWorld-Verified

    Hy4—
    MiniMax M370.1%
    Source

    Not directly comparable

  • MCP Atlas

    Hy4—
    MiniMax M374.2%
    Source

    Not directly comparable

  • Claw-Eval

    Hy4—
    MiniMax M374.5%
    Source

    Not directly comparable

  • BankerToolBench

    Hy4—
    MiniMax M376.1%
    Source

    Not directly comparable

  • ResearchClawBench

    Hy4—
    MiniMax M319.8%
    Source

    Not directly comparable

  • OSWorld 2.0

    Hy4—
    MiniMax M34.6%
    Source

    Not directly comparable

  • Terminal-Bench 2.1 (Vals)

    Hy4—
    MiniMax M353.6%
    Source

    Not directly comparable

  • MazeBench

    Hy4—
    MiniMax M30%
    Source

    Not directly comparable

Coding

  • DeepSWE

    Hy474.9%
    Source
    MiniMax M3—

    Not directly comparable

  • sweMarathon

    Hy443.8%
    Source
    MiniMax M3—

    Not directly comparable

  • ProgramBench

    Hy430.0%
    Source
    MiniMax M3—

    Not directly comparable

  • FrontierCode 2.0

    Hy431.0%
    Source
    MiniMax M3—

    Not directly comparable

  • PostTrainBench v1.2

    Hy435.2%
    Source
    MiniMax M3—

    Not directly comparable

  • SWE-bench Verified

    Hy4—
    MiniMax M380.5%
    Source

    Not directly comparable

  • SWE-bench Pro

    Hy4—
    MiniMax M359%
    Source

    Not directly comparable

  • Terminal-Bench 2.1

    Hy4—
    MiniMax M366.0%
    Source

    Not directly comparable

  • NL2Repo

    Hy4—
    MiniMax M342.1%
    Source

    Not directly comparable

  • VIBE V2

    Hy4—
    MiniMax M350.1%
    Source

    Not directly comparable

  • SVG-Bench

    Hy4—
    MiniMax M363.7%
    Source

    Not directly comparable

  • KernelBench Hard

    Hy4—
    MiniMax M328.8%
    Source

    Not directly comparable

  • OpenHarmony Bench

    Hy4—
    MiniMax M348.4%
    Source

    Not directly comparable

  • LiveCodeBench (Vals)

    Hy4—
    MiniMax M382.2%
    Source

    Not directly comparable

  • SWE-bench (Vals)

    Hy4—
    MiniMax M375.0%
    Source

    Not directly comparable

Multimodal

  • MMMU-Pro

    Hy478.6%
    Source
    MiniMax M378.1%
    Source

    Hy4 leads this result

  • ZeroBench w/ Python

    Hy455.4%
    Source
    MiniMax M3—

    Not directly comparable

  • OfficeQA Pro

    Hy4—
    MiniMax M345.1%
    Source

    Not directly comparable

  • OmniDocBench 1.5

    Hy4—
    MiniMax M391.6%
    Source

    Not directly comparable

  • VideoMMMU

    Hy4—
    MiniMax M384.6%
    Source

    Not directly comparable

  • Video-MME (with subtitle)

    Hy4—
    MiniMax M385.4%
    Source

    Not directly comparable

Knowledge

  • HLE

    Hy459.1%
    Source
    MiniMax M3—

    Not directly comparable

  • GPQA Diamond (Vals)

    Hy4—
    MiniMax M392.7%
    Source

    Not directly comparable

  • MMLU-Pro (Vals)

    Hy4—
    MiniMax M384.2%
    Source

    Not directly comparable

Math

  • USAMO 2026

    Hy4—
    MiniMax M385.7%
    Source

    Not directly comparable

42 public results · 1 shared

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Last updated November 4, 2026