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BenchLM
Model A
dots3-note Preview

Dots Studio

—

Evidence status unavailable

90% interval unavailable

dots3-note Preview vs MiniMax M2.7

Updated August 14, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

Model B
MiniMax M2.7

MiniMax

63.1/100

Supported · Public rank #46

90% interval 56.0–70.1

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.

4 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.

  • Long documents

    Prompts that approach the documented context limit

    dots3-note Preview

    dots3-note Preview 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

    The category averages use different weighted benchmark sets, so they are directional rather than like-for-like.

    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

  • 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. dots3-note Preview 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
4
dots3-note Preview only
27
MiniMax M2.7 only
14
Like-for-like categories
0 / 8

1 category uses different evidence sets. Those rows remain visible for coverage context but do not name a winner.

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.

Coding

Directional only
dots3-note Preview
71.7
MiniMax M2.7
53.3
Weighted basis
2 vs 2 rows
Reading
Directional only

Agentic

Not comparable
dots3-note Preview
83.3
MiniMax M2.7
57.0
Weighted basis
1 vs 1 rows
Reading
Not comparable

Reasoning

Not comparable
dots3-note Preview
81.4
MiniMax M2.7
Not measured
Weighted basis
1 vs 0 rows
Reading
Not comparable

Knowledge

Not comparable
dots3-note Preview
52.6
MiniMax M2.7
Not measured
Weighted basis
1 vs 0 rows
Reading
Not comparable

Math

Not comparable
dots3-note Preview
Not measured
MiniMax M2.7
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multilingual

Not comparable
dots3-note Preview
Not measured
MiniMax M2.7
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multimodal

Not comparable
dots3-note Preview
79.1
MiniMax M2.7
Not measured
Weighted basis
1 vs 0 rows
Reading
Not comparable

Instruction following

Not comparable
dots3-note Preview
85.1
MiniMax M2.7
Not measured
Weighted basis
2 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.

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.

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

dots3-note Preview
Self-hosted; infrastructure cost varies
Fits in one request
MiniMax M2.7
$0.0009
Fits in one request

dots3-note Preview has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

dots3-note Preview
Self-hosted; infrastructure cost varies
Fits in one request
MiniMax M2.7
$0.0186
Fits in one request

dots3-note Preview has no comparable published API token rate.

Cache-heavy agent loop

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

dots3-note Preview
Self-hosted; infrastructure cost varies
Fits in one request
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. dots3-note Preview 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.

API model ID

dots3-note Preview

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.

dots3-note Preview

No comparable hosted API rate

dots3-note Preview model card

MiniMax M2.7

Not published

Documented inputs

dots3-note Preview

Not sourced

MiniMax M2.7

Not sourced

Documented outputs

dots3-note Preview

Not sourced

MiniMax M2.7

Not sourced

Provider availability

dots3-note Preview

Not sourced

MiniMax M2.7

Not sourced

Reasoning profile

dots3-note Preview

Reasoning

MiniMax M2.7

Non-Reasoning

Weight access

dots3-note Preview

Open Weight

MiniMax M2.7

Open Weight

License

dots3-note Preview

Open Weight

MiniMax M2.7

Open Weight

Release date

dots3-note Preview

2026-08-14

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
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
A complete comparable API-rate estimate is not available for both models.
Context tradeoff
dots3-note Preview has the larger documented window (512K).

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 evidence45 rows

Agentic

  • Claw-Eval

    dots3-note Preview73.4%
    Source
    MiniMax M2.748.7%
    Source

    dots3-note Preview leads this result

  • Terminal-Bench 2.1

    dots3-note Preview75.1%
    Source
    MiniMax M2.7—

    Not directly comparable

  • Toolathlon-Verified

    dots3-note Preview55.6%
    Source
    MiniMax M2.7—

    Not directly comparable

  • skillsBench

    dots3-note Preview52.8%
    Source
    MiniMax M2.7—

    Not directly comparable

  • APEX-Agents

    dots3-note Preview30.8%
    Source
    MiniMax M2.7—

    Not directly comparable

  • BrowseComp

    dots3-note Preview83.3%
    Source
    MiniMax M2.7—

    Not directly comparable

  • HLE w/ tools

    dots3-note Preview52.6%
    Source
    MiniMax M2.7—

    Not directly comparable

  • DeepSearchQA

    dots3-note Preview92.1%
    Source
    MiniMax M2.7—

    Not directly comparable

  • WideResearch

    dots3-note Preview78.9%
    Source
    MiniMax M2.7—

    Not directly comparable

  • Terminal-Bench 2.0

    dots3-note Preview—
    MiniMax M2.757%
    Source

    Not directly comparable

  • Toolathlon

    dots3-note Preview—
    MiniMax M2.746.3%
    Source

    Not directly comparable

  • MLE-Bench Lite

    dots3-note Preview—
    MiniMax M2.766.6%
    Source

    Not directly comparable

  • MM-ClawBench

    dots3-note Preview—
    MiniMax M2.762.7%
    Source

    Not directly comparable

  • Gert Labs

    dots3-note Preview—
    MiniMax M2.740.40%
    Source

    Not directly comparable

Coding

  • Codeforces

    dots3-note Preview3056.0
    Source
    MiniMax M2.7—

    Not directly comparable

  • LiveCodeBench v6

    dots3-note Preview91.5%
    Source
    MiniMax M2.7—

    Not directly comparable

  • Terminal-Bench 2.1

    dots3-note Preview75.1%
    Source
    MiniMax M2.7—

    Not directly comparable

  • SWE-bench Verified

    dots3-note Preview78.4%
    Source
    MiniMax M2.7—

    Not directly comparable

  • SWE Multilingual

    dots3-note Preview75.7%
    Source
    MiniMax M2.776.5%
    Source

    MiniMax M2.7 leads this result

  • SWE-bench Pro

    dots3-note Preview61%
    Source
    MiniMax M2.756.2%
    Source

    dots3-note Preview leads this result

  • NL2Repo

    dots3-note Preview49.8%
    Source
    MiniMax M2.739.8%
    Source

    dots3-note Preview leads this result

  • SWE-bench Verified*

    dots3-note Preview—
    MiniMax M2.775.4%
    Source

    Not directly comparable

  • SWE-Rebench

    dots3-note Preview—
    MiniMax M2.751.9%
    Source

    Not directly comparable

  • Multi-SWE Bench

    dots3-note Preview—
    MiniMax M2.752.7%
    Source

    Not directly comparable

  • VIBE-Pro

    dots3-note Preview—
    MiniMax M2.755.6%
    Source

    Not directly comparable

  • Vibe Code Bench

    dots3-note Preview—
    MiniMax M2.727.04%
    Source

    Not directly comparable

  • React Native Evals

    dots3-note Preview—
    MiniMax M2.771.4%
    Source

    Not directly comparable

Reasoning

  • ARC-AGI-2

    dots3-note Preview81.4%
    Source
    MiniMax M2.7—

    Not directly comparable

Knowledge

  • HLE

    dots3-note Preview52.6%
    Source
    MiniMax M2.7—

    Not directly comparable

  • GPQA-D

    dots3-note Preview—
    MiniMax M2.787.0%
    Source

    Not directly comparable

  • MMLU-Pro (Arcee)

    dots3-note Preview—
    MiniMax M2.780.8%
    Source

    Not directly comparable

Math

  • IMOAnswerBench

    dots3-note Preview90.9%
    Source
    MiniMax M2.7—

    Not directly comparable

  • AIME25 (Arcee)

    dots3-note Preview—
    MiniMax M2.780.0%
    Source

    Not directly comparable

Multimodal

  • SimpleVQA

    dots3-note Preview72.5%
    Source
    MiniMax M2.7—

    Not directly comparable

  • MMMU-Pro

    dots3-note Preview79.1%
    Source
    MiniMax M2.7—

    Not directly comparable

  • MathVision

    dots3-note Preview87.7%
    Source
    MiniMax M2.7—

    Not directly comparable

  • ZeroBench

    dots3-note Preview19.0%
    Source
    MiniMax M2.7—

    Not directly comparable

  • CharXiv w/o tools

    dots3-note Preview83.1%
    Source
    MiniMax M2.7—

    Not directly comparable

  • GDP.pdf (no tools)

    dots3-note Preview60.7%
    Source
    MiniMax M2.7—

    Not directly comparable

  • PerceptionBench

    dots3-note Preview53.4%
    Source
    MiniMax M2.7—

    Not directly comparable

  • BabyVision

    dots3-note Preview50.0%
    Source
    MiniMax M2.7—

    Not directly comparable

  • MMVU

    dots3-note Preview79.9%
    Source
    MiniMax M2.7—

    Not directly comparable

  • VideoMMMU

    dots3-note Preview86.8%
    Source
    MiniMax M2.7—

    Not directly comparable

Instruction following

  • IFBench

    dots3-note Preview80.4%
    Source
    MiniMax M2.7—

    Not directly comparable

  • IFEval

    dots3-note Preview93.9%
    Source
    MiniMax M2.7—

    Not directly comparable

Frequently asked questions

Which is better, dots3-note Preview or MiniMax M2.7?

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, dots3-note Preview or MiniMax M2.7?

The current coding averages use different weighted benchmark sets, so BenchLM does not name a winner from them. Read the shared benchmark rows directly and test the models on the same task set.

Which is better for agentic tasks, dots3-note Preview 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, dots3-note Preview 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, dots3-note Preview or MiniMax M2.7?

dots3-note Preview has the larger documented context window: 512K, compared with 200K.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated August 14, 2026

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