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Model comparison

Mair-hub 0.5B Omni vs MiniMax M2.7

Updated August 4, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

Mair-hub 0.5B Omni

Mair Hub

—

Evidence status unavailable

90% interval unavailable

MiniMax M2.7

MiniMax

63.1/100

Supported · Public rank #41

90% interval 56.6–69.7

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 based on different benchmark sets are marked directional and do not name a winner.

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.

No workload recommendation clears the current evidence threshold.

Use the matched evidence, workload costs, and sourced specifications below instead of treating a point score as a universal answer.

Show secondary and unsupported calls

  • Coding work

    Code generation, repair, and software-engineering tasks

    Not enough matched evidence

    No shared weighted benchmark basis supports a winner.

    Confidence: limited

  • Agentic work

    Tool use, computer use, and multi-step task completion

    Not enough matched evidence

    No shared weighted benchmark basis supports a winner.

    Confidence: limited

  • Long documents

    Prompts that approach the documented context limit

    Not enough matched evidence

    A complete context comparison is not sourced.

    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. MiniMax M2.7 does not fit this workload in one request. MiniMax M2.7 has no published cached-input rate, so cached tokens use its listed input rate. Mair-hub 0.5B Omni has no comparable published API token rate.

    Confidence: rate-fallback

  • 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

What is actually comparable

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

Shared results
0
Mair-hub 0.5B Omni only
0
MiniMax M2.7 only
18
Like-for-like categories
0 / 8

Category results, on a stated basis

Each row states whether both averages use the same weighted benchmark set. Directional and not-comparable rows remain visible, but they never receive a winner in this template.

Agentic

Not comparable
Mair-hub 0.5B Omni
Not measured
MiniMax M2.7
57.0
Weighted basis
0 vs 1 rows
Reading
Not comparable

Coding

Not comparable
Mair-hub 0.5B Omni
Not measured
MiniMax M2.7
53.3
Weighted basis
0 vs 2 rows
Reading
Not comparable

Reasoning

Not comparable
Mair-hub 0.5B Omni
Not measured
MiniMax M2.7
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Knowledge

Not comparable
Mair-hub 0.5B Omni
Not measured
MiniMax M2.7
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Math

Not comparable
Mair-hub 0.5B Omni
Not measured
MiniMax M2.7
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multilingual

Not comparable
Mair-hub 0.5B Omni
Not measured
MiniMax M2.7
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multimodal

Not comparable
Mair-hub 0.5B Omni
Not measured
MiniMax M2.7
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Instruction following

Not comparable
Mair-hub 0.5B Omni
Not measured
MiniMax M2.7
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

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.

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

Mair-hub 0.5B Omni
API rate not published
Fit state unavailable
MiniMax M2.7
$0.0009
Fits in one request

Mair-hub 0.5B Omni has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

Mair-hub 0.5B Omni
API rate not published
Fit state unavailable
MiniMax M2.7
$0.0186
Fits in one request

Mair-hub 0.5B Omni has no comparable published API token rate.

Cache-heavy agent loop

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

Mair-hub 0.5B Omni
API rate not published
Fit state unavailable
Cached-input rate unavailable
MiniMax M2.7
$0.078
Does not fit in one request
Cached input priced at the published list-input rate

MiniMax M2.7 does not fit this workload in one request. MiniMax M2.7 has no published cached-input rate, so cached tokens use its listed input rate. Mair-hub 0.5B Omni has no comparable published API token rate.

Specification differences

Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.

Context window

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

Mair-hub 0.5B Omni

N/A

MiniMax M2.7

200K

API model ID

Mair-hub 0.5B Omni

Not sourced

MiniMax M2.7

Not sourced

Cached-input rate

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

Mair-hub 0.5B Omni

No comparable hosted API rate

Mair Hub model documentation

MiniMax M2.7

Not published

Documented inputs

Mair-hub 0.5B Omni

Not sourced

MiniMax M2.7

Not sourced

Documented outputs

Mair-hub 0.5B Omni

Not sourced

MiniMax M2.7

Not sourced

Provider availability

Mair-hub 0.5B Omni

Not sourced

MiniMax M2.7

Not sourced

Reasoning profile

Mair-hub 0.5B Omni

Non-Reasoning

MiniMax M2.7

Non-Reasoning

Weight access

Mair-hub 0.5B Omni

Open Weight

MiniMax M2.7

Open Weight

License

Mair-hub 0.5B Omni

Open Weight

MiniMax M2.7

Open Weight

Release date

Mair-hub 0.5B Omni

Not sourced

MiniMax M2.7

2026-03-18

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
A complete documented context comparison is not available.

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

Benchmark evidence

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

Browse raw public benchmark evidence18 rows

Agentic

  • Terminal-Bench 2.0

    Mair-hub 0.5B Omni—
    MiniMax M2.757%
    Source

    Not directly comparable

  • Toolathlon

    Mair-hub 0.5B Omni—
    MiniMax M2.746.3%
    Source

    Not directly comparable

  • MLE-Bench Lite

    Mair-hub 0.5B Omni—
    MiniMax M2.766.6%
    Source

    Not directly comparable

  • MM-ClawBench

    Mair-hub 0.5B Omni—
    MiniMax M2.762.7%
    Source

    Not directly comparable

  • Claw-Eval

    Mair-hub 0.5B Omni—
    MiniMax M2.748.7%
    Source

    Not directly comparable

  • Gert Labs

    Mair-hub 0.5B Omni—
    MiniMax M2.740.40%
    Source

    Not directly comparable

Coding

  • SWE-bench Verified*

    Mair-hub 0.5B Omni—
    MiniMax M2.775.4%
    Source

    Not directly comparable

  • SWE-bench Pro

    Mair-hub 0.5B Omni—
    MiniMax M2.756.2%
    Source

    Not directly comparable

  • SWE-Rebench

    Mair-hub 0.5B Omni—
    MiniMax M2.751.9%
    Source

    Not directly comparable

  • SWE Multilingual

    Mair-hub 0.5B Omni—
    MiniMax M2.776.5%
    Source

    Not directly comparable

  • Multi-SWE Bench

    Mair-hub 0.5B Omni—
    MiniMax M2.752.7%
    Source

    Not directly comparable

  • VIBE-Pro

    Mair-hub 0.5B Omni—
    MiniMax M2.755.6%
    Source

    Not directly comparable

  • NL2Repo

    Mair-hub 0.5B Omni—
    MiniMax M2.739.8%
    Source

    Not directly comparable

  • Vibe Code Bench

    Mair-hub 0.5B Omni—
    MiniMax M2.727.04%
    Source

    Not directly comparable

  • React Native Evals

    Mair-hub 0.5B Omni—
    MiniMax M2.771.4%
    Source

    Not directly comparable

Knowledge

  • GPQA-D

    Mair-hub 0.5B Omni—
    MiniMax M2.787.0%
    Source

    Not directly comparable

  • MMLU-Pro (Arcee)

    Mair-hub 0.5B Omni—
    MiniMax M2.780.8%
    Source

    Not directly comparable

Math

  • AIME25 (Arcee)

    Mair-hub 0.5B Omni—
    MiniMax M2.780.0%
    Source

    Not directly comparable

Frequently asked questions

Which is better, Mair-hub 0.5B Omni or MiniMax M2.7?

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, Mair-hub 0.5B Omni or MiniMax M2.7?

The published evidence does not provide a shared weighted coding basis for both models, so BenchLM does not name a coding winner.

Which is better for agentic tasks, Mair-hub 0.5B Omni or MiniMax M2.7?

The published evidence does not provide a shared weighted agentic tasks basis for both models, so BenchLM does not name a agentic tasks winner.

Which costs less, Mair-hub 0.5B Omni or MiniMax M2.7?

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, Mair-hub 0.5B Omni or MiniMax M2.7?

A complete documented context-window comparison is not available.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated August 4, 2026

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