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BenchLM

Interfaze Beta vs MiniMax M2.7

Updated September 24, 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
Interfaze logo
Interfaze Beta

Interfaze

—

Evidence status unavailable

90% interval unavailable

Model B
MiniMax logo
MiniMax M2.7

MiniMax

48.08/100

Supported · Public rank #83

90% interval 37.2–58.9

Shared results
1
Interfaze Beta only
8
MiniMax M2.7 only
22
Like-for-like categories
0 / 8
Supported: MiniMax M2.7How 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

    Interfaze Beta

    Interfaze Beta has the larger documented context window.

    Confidence: documented

  • Chat turn cost

    1K fresh input + 500 output tokens

    MiniMax M2.7

    MiniMax M2.7 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 M2.7

    MiniMax M2.7 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

    Interfaze Beta 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

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

    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. MiniMax M2.7 does not fit this workload in one request. Interfaze Beta has no published cached-input rate, so cached tokens use its listed input rate. MiniMax M2.7 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.

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

—Interfaze Beta36.1MiniMax M2.7

Not comparable · BenchAlign v5.7

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.

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 v5.7 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
Interfaze Beta
Not ranked
MiniMax M2.7
25.2
Supported · #79/105
Basis
BenchAlign v5.7 lane · 0 vs 7 public rows
Reading
Not comparable

Coding

Not comparable
Interfaze Beta
Not ranked
MiniMax M2.7
36.1
Supported · #70/135
Basis
BenchAlign v5.7 lane · 1 vs 11 public rows
Reading
Not comparable

Reasoning

Not comparable
Interfaze Beta
Not ranked
MiniMax M2.7
75.9
Unranked · 2 rankable rows
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Multimodal

Not comparable
Interfaze Beta
27.6
Unranked · 5 rankable rows
MiniMax M2.7
Not ranked
Basis
Provisional lane · 1 vs 0 weighted rows
Reading
Not comparable

Knowledge

Not comparable
Interfaze Beta
Not ranked
MiniMax M2.7
43.0
Supported · #79/158
Basis
BenchAlign v5.7 lane · 3 vs 4 public rows
Reading
Not comparable

Multilingual

Not comparable
Interfaze Beta
Not ranked
MiniMax M2.7
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Instruction following

Not comparable
Interfaze Beta
Not ranked
MiniMax M2.7
91.6
#10/124
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Math

Not comparable
Interfaze Beta
Not ranked
MiniMax M2.7
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign v5.7) 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

Interfaze Beta
$0.00325
Fits in one request
MiniMax M2.7
$0.0009
Fits in one request

MiniMax M2.7 has the lower modeled cost

Costs use the listed standard API rates.

Repository review

50K fresh input + 3K output tokens

Interfaze Beta
$0.0855
Fits in one request
MiniMax M2.7
$0.0186
Fits in one request

MiniMax M2.7 has the lower modeled cost

Costs use the listed standard API rates.

Cache-heavy agent loop

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

Interfaze Beta
$0.365
Fits in one request
Cached input priced at the published list-input rate
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. Interfaze Beta has no published cached-input rate, so cached tokens use its listed input rate. MiniMax M2.7 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.

Context window

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

Interfaze Beta

1M

MiniMax M2.7

200K

API model ID

Interfaze Beta

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.

Interfaze Beta

Not published

MiniMax M2.7

Not published

Documented inputs

Interfaze Beta

Not sourced

MiniMax M2.7

Not sourced

Documented outputs

Interfaze Beta

Not sourced

MiniMax M2.7

Not sourced

Provider availability

Interfaze Beta

Not sourced

MiniMax M2.7

Not sourced

Reasoning profile

Interfaze Beta

Reasoning

MiniMax M2.7

Non-Reasoning

Weight access

Interfaze Beta

Proprietary

MiniMax M2.7

Open Weight

License

Interfaze Beta

Proprietary

MiniMax M2.7

Open Weight

Release date

Interfaze Beta

2026-05-11

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
Repository review: $0.0855 vs $0.0186. Cache-heavy agent loop: $0.365 vs $0.078.
Context tradeoff
Interfaze Beta has the larger documented window (1M).
Run the same representative tasks against both endpoints before changing production traffic.

Questions

Which is better, Interfaze Beta 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, Interfaze Beta or MiniMax M2.7?

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

Which is better for agentic tasks, Interfaze Beta or MiniMax M2.7?

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

Which costs less, Interfaze Beta or MiniMax M2.7?

For the stated presets, chat costs $0.00325 on Interfaze Beta and $0.0009 on MiniMax M2.7; repository review costs $0.0855 and $0.0186; the cache-heavy agent loop costs $0.365 and $0.078. MiniMax M2.7 does not fit this workload in one request. Interfaze Beta has no published cached-input rate, so cached tokens use its listed input rate. MiniMax M2.7 has no published cached-input rate, so cached tokens use its listed input rate.

Which has the larger context window, Interfaze Beta or MiniMax M2.7?

Interfaze Beta 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 evidence31 rows

Agentic

  • Terminal-Bench 2.0

    Interfaze Beta—
    MiniMax M2.757%
    Source

    Not directly comparable

  • Toolathlon

    Interfaze Beta—
    MiniMax M2.746.3%
    Source

    Not directly comparable

  • MLE-Bench Lite

    Interfaze Beta—
    MiniMax M2.766.6%
    Source

    Not directly comparable

  • MM-ClawBench

    Interfaze Beta—
    MiniMax M2.762.7%
    Source

    Not directly comparable

  • Claw-Eval

    Interfaze Beta—
    MiniMax M2.748.7%
    Source

    Not directly comparable

  • Gert Labs

    Interfaze Beta—
    MiniMax M2.740.40%
    Source

    Not directly comparable

  • Terminal-Bench 2.1 (Vals)

    Interfaze Beta—
    MiniMax M2.748.7%
    Source

    Not directly comparable

Coding

  • Spider 2.0-Lite

    Interfaze Beta52.9%
    Source
    MiniMax M2.7—

    Not directly comparable

  • SWE-bench Verified*

    Interfaze Beta—
    MiniMax M2.775.4%
    Source

    Not directly comparable

  • SWE-bench Pro

    Interfaze Beta—
    MiniMax M2.756.2%
    Source

    Not directly comparable

  • SWE-Rebench

    Interfaze Beta—
    MiniMax M2.751.9%
    Source

    Not directly comparable

  • SWE Multilingual

    Interfaze Beta—
    MiniMax M2.776.5%
    Source

    Not directly comparable

  • Multi-SWE Bench

    Interfaze Beta—
    MiniMax M2.752.7%
    Source

    Not directly comparable

  • VIBE-Pro

    Interfaze Beta—
    MiniMax M2.755.6%
    Source

    Not directly comparable

  • NL2Repo

    Interfaze Beta—
    MiniMax M2.739.8%
    Source

    Not directly comparable

  • Vibe Code Bench

    Interfaze Beta—
    MiniMax M2.727.04%
    Source

    Not directly comparable

  • React Native Evals

    Interfaze Beta—
    MiniMax M2.771.4%
    Source

    Not directly comparable

  • LiveCodeBench (Vals)

    Interfaze Beta—
    MiniMax M2.779.9%
    Source

    Not directly comparable

  • SWE-bench (Vals)

    Interfaze Beta—
    MiniMax M2.773.8%
    Source

    Not directly comparable

Multimodal

  • OCRBench V2

    Interfaze Beta70.7%
    Source
    MiniMax M2.7—

    Not directly comparable

  • olmOCR

    Interfaze Beta85.7%
    Source
    MiniMax M2.7—

    Not directly comparable

  • RefCOCO (avg)

    Interfaze Beta82.1%
    Source
    MiniMax M2.7—

    Not directly comparable

  • MMMU-Pro

    Interfaze Beta71.1%
    Source
    MiniMax M2.7—

    Not directly comparable

Knowledge

  • GPQA

    Interfaze Beta89.9%
    Source
    MiniMax M2.7—

    Not directly comparable

  • GPQA-D

    Interfaze Beta89.9%
    Source
    MiniMax M2.787.0%
    Source

    Interfaze Beta leads this result

  • MMMLU

    Interfaze Beta90.9%
    Source
    MiniMax M2.7—

    Not directly comparable

  • MMLU-Pro (Arcee)

    Interfaze Beta—
    MiniMax M2.780.8%
    Source

    Not directly comparable

  • GPQA Diamond (Vals)

    Interfaze Beta—
    MiniMax M2.786.6%
    Source

    Not directly comparable

  • MMLU-Pro (Vals)

    Interfaze Beta—
    MiniMax M2.780.4%
    Source

    Not directly comparable

Instruction following

  • SOB Value Acc

    Interfaze Beta79.5%
    Source
    MiniMax M2.7—

    Not directly comparable

Math

  • AIME25 (Arcee)

    Interfaze Beta—
    MiniMax M2.780.0%
    Source

    Not directly comparable

31 public results · 1 shared

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Last updated September 24, 2026