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Model comparison

Gemini 2.5 Pro vs MiniMax M2.7

Updated August 1, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

20 confirmed releases in the last 30 daysSee provider release alerts
Gemini 2.5 Pro

Google

56.6/100

Supported · Public rank #77

90% interval 39.2–74.0

MiniMax M2.7

MiniMax

63.1/100

Supported · Public rank #40

90% interval 56.5–69.7

MiniMax M2.7 has the higher public score estimate, 63.09 versus 56.59, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.

2 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

    Gemini 2.5 Pro

    Gemini 2.5 Pro 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

    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

  • 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.

    Confidence: rate-fallback

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
2
Gemini 2.5 Pro only
5
MiniMax M2.7 only
16
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
Gemini 2.5 Pro
Not measured
MiniMax M2.7
57.0
Weighted basis
0 vs 1 rows
Reading
Not comparable

Coding

Not comparable
Gemini 2.5 Pro
63.8
MiniMax M2.7
53.3
Weighted basis
1 vs 2 rows
Reading
Not comparable

Reasoning

Not comparable
Gemini 2.5 Pro
Not measured
MiniMax M2.7
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Knowledge

Not comparable
Gemini 2.5 Pro
27.4
MiniMax M2.7
Not measured
Weighted basis
2 vs 0 rows
Reading
Not comparable

Math

Not comparable
Gemini 2.5 Pro
11.6
MiniMax M2.7
Not measured
Weighted basis
2 vs 0 rows
Reading
Not comparable

Multilingual

Not comparable
Gemini 2.5 Pro
Not measured
MiniMax M2.7
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multimodal

Not comparable
Gemini 2.5 Pro
Not measured
MiniMax M2.7
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Instruction following

Not comparable
Gemini 2.5 Pro
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

Gemini 2.5 Pro
$0.00625
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

Gemini 2.5 Pro
$0.0925
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

Gemini 2.5 Pro
$0.15
Fits in one request
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.

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.

MiniMax M2.7

200K

Cached-input rate

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

Gemini 2.5 Pro

$0.125 per 1M cached input tokens

Google Gemini API pricing

MiniMax M2.7

Not published

Documented inputs

Gemini 2.5 Pro

Not sourced

MiniMax M2.7

Not sourced

Documented outputs

Gemini 2.5 Pro

Not sourced

MiniMax M2.7

Not sourced

Provider availability

Gemini 2.5 Pro

Not sourced

MiniMax M2.7

Not sourced

Reasoning profile

Gemini 2.5 Pro

Non-Reasoning

MiniMax M2.7

Non-Reasoning

Weight access

Gemini 2.5 Pro

Proprietary

MiniMax M2.7

Open Weight

License

Gemini 2.5 Pro

Proprietary

MiniMax M2.7

Open Weight

Release date

Gemini 2.5 Pro

2025-03-01

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
MiniMax M2.7 has the higher public score estimate, 63.09 versus 56.59, but the 90% score intervals overlap.
Workload cost
Repository review: $0.0925 vs $0.0186. Cache-heavy agent loop: $0.15 vs $0.078.
Context tradeoff
Gemini 2.5 Pro has the larger documented window (1M).

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 evidence23 rows

Agentic

  • Gert Labs

    Shared source
    Gemini 2.5 Pro42.01%
    MiniMax M2.740.40%

    Gemini 2.5 Pro leads this result

  • Terminal-Bench 2.0

    Gemini 2.5 Pro—
    MiniMax M2.757%
    Source

    Not directly comparable

  • Toolathlon

    Gemini 2.5 Pro—
    MiniMax M2.746.3%
    Source

    Not directly comparable

  • MLE-Bench Lite

    Gemini 2.5 Pro—
    MiniMax M2.766.6%
    Source

    Not directly comparable

  • MM-ClawBench

    Gemini 2.5 Pro—
    MiniMax M2.762.7%
    Source

    Not directly comparable

  • Claw-Eval

    Gemini 2.5 Pro—
    MiniMax M2.748.7%
    Source

    Not directly comparable

Coding

  • SWE-bench Verified

    Gemini 2.5 Pro63.8%
    Source
    MiniMax M2.7—

    Not directly comparable

  • Vibe Code Bench

    Shared source
    Gemini 2.5 Pro0.40%
    MiniMax M2.727.04%

    MiniMax M2.7 leads this result

  • SWE-bench Verified*

    Gemini 2.5 Pro—
    MiniMax M2.775.4%
    Source

    Not directly comparable

  • SWE-bench Pro

    Gemini 2.5 Pro—
    MiniMax M2.756.2%
    Source

    Not directly comparable

  • SWE-Rebench

    Gemini 2.5 Pro—
    MiniMax M2.751.9%
    Source

    Not directly comparable

  • SWE Multilingual

    Gemini 2.5 Pro—
    MiniMax M2.776.5%
    Source

    Not directly comparable

  • Multi-SWE Bench

    Gemini 2.5 Pro—
    MiniMax M2.752.7%
    Source

    Not directly comparable

  • VIBE-Pro

    Gemini 2.5 Pro—
    MiniMax M2.755.6%
    Source

    Not directly comparable

  • NL2Repo

    Gemini 2.5 Pro—
    MiniMax M2.739.8%
    Source

    Not directly comparable

  • React Native Evals

    Gemini 2.5 Pro—
    MiniMax M2.771.4%
    Source

    Not directly comparable

Knowledge

  • GPQA

    Gemini 2.5 Pro83%
    Source
    MiniMax M2.7—

    Not directly comparable

  • HLE

    Gemini 2.5 Pro18.8%
    Source
    MiniMax M2.7—

    Not directly comparable

  • GPQA-D

    Gemini 2.5 Pro—
    MiniMax M2.787.0%
    Source

    Not directly comparable

  • MMLU-Pro (Arcee)

    Gemini 2.5 Pro—
    MiniMax M2.780.8%
    Source

    Not directly comparable

Math

  • FrontierMath v2 (Tiers 1-3)

    Gemini 2.5 Pro14.138%
    Source
    MiniMax M2.7—

    Not directly comparable

  • FrontierMath v2 (Tier 4)

    Gemini 2.5 Pro4.167%
    Source
    MiniMax M2.7—

    Not directly comparable

  • AIME25 (Arcee)

    Gemini 2.5 Pro—
    MiniMax M2.780.0%
    Source

    Not directly comparable

Frequently asked questions

Which is better, Gemini 2.5 Pro or MiniMax M2.7?

MiniMax M2.7 has the higher public score estimate, 63.09 versus 56.59, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.

Which is better for coding, Gemini 2.5 Pro 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, Gemini 2.5 Pro 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, Gemini 2.5 Pro or MiniMax M2.7?

For the stated presets, chat costs $0.00625 on Gemini 2.5 Pro and $0.0009 on MiniMax M2.7; repository review costs $0.0925 and $0.0186; the cache-heavy agent loop costs $0.15 and $0.078. 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.

Which has the larger context window, Gemini 2.5 Pro or MiniMax M2.7?

Gemini 2.5 Pro has the larger documented context window: 1M, compared with 200K.

Related comparisons

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Last updated August 1, 2026

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