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BenchLM

Gemini 3.1 Pro vs MiniMax M2.7

Decision reading

Gemini 3.1 Pro has the higher public score estimate, 70.14 versus 55.01, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.

10 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

Google logo
Model A
Gemini 3.1 Pro

Google

70.14/100

Supported · Public rank #18

90% interval 63.4–76.9

MiniMax logo
Model B
MiniMax M2.7

MiniMax

55.01/100

Supported · Public rank #96

90% interval 43.4–66.6

Updated September 18, 2026. Rank says Gemini 3.1 Pro is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.

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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 3.1 Pro

    Gemini 3.1 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

    MiniMax M2.7 is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like.

    Confidence: limited

  • Agentic work

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

    Not enough matched evidence

    MiniMax M2.7 is scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.

    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

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.

46.1Gemini 3.1 Pro48.5MiniMax M2.7

Directional only · BenchAlign

MiniMax M2.7 scores higher, but at least one score rests on Estimated evidence or a different benchmark set. Directional only, no winner.

Coding scores combine specific tasks and setups. Match the editor, harness, and effort to your workflow.

Open the coding leaderboardShortlist models for this job

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
10
Gemini 3.1 Pro only
18
MiniMax M2.7 only
13
Like-for-like categories
1 / 8

2 categories rest on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.

Category results, on a stated basis

Each row shows the public-lane category score for both models: the BenchAlign 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.

Knowledge

Like-for-like
Gemini 3.1 Pro
66.0
Supported · #22/184
MiniMax M2.7
48.7
Supported · #93/184
Basis
BenchAlign lane · 6 vs 4 public rows
Reading
Gemini 3.1 Pro leads · intervals overlap

Agentic

Directional only
Gemini 3.1 Pro
39.7
Supported · #121/154
MiniMax M2.7
41.0
Estimated · #113/154
Basis
BenchAlign lane · 6 vs 7 public rows
Reading
Directional only

Coding

Directional only
Gemini 3.1 Pro
46.1
Supported · #88/154
MiniMax M2.7
48.5
Estimated · #70/154
Basis
BenchAlign lane · 5 vs 11 public rows
Reading
Directional only

Reasoning

Not comparable
Gemini 3.1 Pro
50.7
Unranked · 2 rankable rows
MiniMax M2.7
74.8
Unranked · 2 rankable rows
Basis
Provisional lane · 2 vs 0 weighted rows
Reading
Not comparable

Math

Not comparable
Gemini 3.1 Pro
54.2
Unranked · 2 rankable rows
MiniMax M2.7
Not ranked
Basis
Provisional lane · 2 vs 0 weighted rows
Reading
Not comparable

Multilingual

Not comparable
Gemini 3.1 Pro
Not ranked
MiniMax M2.7
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Multimodal

Not comparable
Gemini 3.1 Pro
79.1
#12/48
MiniMax M2.7
Not ranked
Basis
Provisional lane · 2 vs 0 weighted rows
Reading
Not comparable

Instruction following

Not comparable
Gemini 3.1 Pro
Not ranked
MiniMax M2.7
91.6
#10/124
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) differ from the provisional-lane categories. Unranked scores sit on the lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.

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

Gemini 3.1 Pro
$0.008
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 3.1 Pro
$0.136
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 3.1 Pro
$0.2
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.

Cached-input rate

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

Gemini 3.1 Pro

$0.2 per 1M cached input tokens

Google Gemini API pricing

MiniMax M2.7

Not published

Reasoning profile

Gemini 3.1 Pro

Reasoning

MiniMax M2.7

Non-Reasoning

Weight access

Gemini 3.1 Pro

Proprietary

MiniMax M2.7

Open Weight

License

Gemini 3.1 Pro

Proprietary

MiniMax M2.7

Open Weight

Release date

Gemini 3.1 Pro

2026-02-19

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
Gemini 3.1 Pro has the higher public score estimate, 70.14 versus 55.01, but the 90% score intervals overlap.
Workload cost
Repository review: $0.136 vs $0.0186. Cache-heavy agent loop: $0.2 vs $0.078.
Context tradeoff
Gemini 3.1 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 evidence41 rows

Agentic

  • Claw-Eval

    Shared source
    Gemini 3.1 Pro57.8%
    MiniMax M2.748.7%

    Gemini 3.1 Pro leads this result

  • DeepSearchQA

    Gemini 3.1 Pro69.7%
    Source
    MiniMax M2.7—

    Not directly comparable

  • τ²-bench results

    Gemini 3.1 Pro95.6%
    Source
    MiniMax M2.7—

    Not directly comparable

  • Gert Labs

    Shared source
    Gemini 3.1 Pro56.87%
    MiniMax M2.740.40%

    Gemini 3.1 Pro leads this result

  • ResearchClawBench

    Gemini 3.1 Pro13.3%
    Source
    MiniMax M2.7—

    Not directly comparable

  • Terminal-Bench 2.1 (Vals)

    Gemini 3.1 Pro70.8%
    Source
    MiniMax M2.748.7%
    Source

    Gemini 3.1 Pro leads this result

  • Terminal-Bench 2.0

    Gemini 3.1 Pro—
    MiniMax M2.757%
    Source

    Not directly comparable

  • Toolathlon

    Gemini 3.1 Pro—
    MiniMax M2.746.3%
    Source

    Not directly comparable

  • MLE-Bench Lite

    Gemini 3.1 Pro—
    MiniMax M2.766.6%
    Source

    Not directly comparable

  • MM-ClawBench

    Gemini 3.1 Pro—
    MiniMax M2.762.7%
    Source

    Not directly comparable

Coding

  • LiveCodeBench Pro

    Gemini 3.1 Pro82.9%
    Source
    MiniMax M2.7—

    Not directly comparable

  • React Native Evals

    Shared source
    Gemini 3.1 Pro78.9%
    MiniMax M2.771.4%

    Gemini 3.1 Pro leads this result

  • Vibe Code Bench

    Shared source
    Gemini 3.1 Pro32.03%
    MiniMax M2.727.04%

    Gemini 3.1 Pro leads this result

  • LiveCodeBench (Vals)

    Gemini 3.1 Pro88.5%
    Source
    MiniMax M2.779.9%
    Source

    Gemini 3.1 Pro leads this result

  • SWE-bench (Vals)

    Gemini 3.1 Pro78.8%
    Source
    MiniMax M2.773.8%
    Source

    Gemini 3.1 Pro leads this result

  • SWE-bench Verified*

    Gemini 3.1 Pro—
    MiniMax M2.775.4%
    Source

    Not directly comparable

  • SWE-bench Pro

    Gemini 3.1 Pro—
    MiniMax M2.756.2%
    Source

    Not directly comparable

  • SWE-Rebench

    Gemini 3.1 Pro—
    MiniMax M2.751.9%
    Source

    Not directly comparable

  • SWE Multilingual

    Gemini 3.1 Pro—
    MiniMax M2.776.5%
    Source

    Not directly comparable

  • Multi-SWE Bench

    Gemini 3.1 Pro—
    MiniMax M2.752.7%
    Source

    Not directly comparable

  • VIBE-Pro

    Gemini 3.1 Pro—
    MiniMax M2.755.6%
    Source

    Not directly comparable

  • NL2Repo

    Gemini 3.1 Pro—
    MiniMax M2.739.8%
    Source

    Not directly comparable

Reasoning

  • ARC-AGI-2

    Gemini 3.1 Pro77.1%
    Source
    MiniMax M2.7—

    Not directly comparable

  • ARC-AGI-3

    Gemini 3.1 Pro0.4%
    Source
    MiniMax M2.7—

    Not directly comparable

Knowledge

  • GPQA-D

    Gemini 3.1 Pro94.3%
    Source
    MiniMax M2.787.0%
    Source

    Gemini 3.1 Pro leads this result

  • HLE w/o tools

    Gemini 3.1 Pro45.4%
    Source
    MiniMax M2.7—

    Not directly comparable

  • HealthBench Hard

    Gemini 3.1 Pro20.6%
    Source
    MiniMax M2.7—

    Not directly comparable

  • MedXpertQA (Text)

    Gemini 3.1 Pro71.5%
    Source
    MiniMax M2.7—

    Not directly comparable

  • GPQA Diamond (Vals)

    Gemini 3.1 Pro95.5%
    Source
    MiniMax M2.786.6%
    Source

    Gemini 3.1 Pro leads this result

  • MMLU-Pro (Vals)

    Gemini 3.1 Pro91.0%
    Source
    MiniMax M2.780.4%
    Source

    Gemini 3.1 Pro leads this result

  • MMLU-Pro (Arcee)

    Gemini 3.1 Pro—
    MiniMax M2.780.8%
    Source

    Not directly comparable

Math

  • FrontierMath v2 (Tiers 1-3)

    Gemini 3.1 Pro36.900%
    Source
    MiniMax M2.7—

    Not directly comparable

  • FrontierMath v2 (Tier 4)

    Gemini 3.1 Pro16.700%
    Source
    MiniMax M2.7—

    Not directly comparable

  • AIME25 (Arcee)

    Gemini 3.1 Pro—
    MiniMax M2.780.0%
    Source

    Not directly comparable

Multimodal

  • MMMU-Pro

    Gemini 3.1 Pro83.9%
    Source
    MiniMax M2.7—

    Not directly comparable

  • CharXiv

    Gemini 3.1 Pro80.2%
    Source
    MiniMax M2.7—

    Not directly comparable

  • ERQA

    Gemini 3.1 Pro69.4%
    Source
    MiniMax M2.7—

    Not directly comparable

  • SimpleVQA

    Gemini 3.1 Pro72.4%
    Source
    MiniMax M2.7—

    Not directly comparable

  • ScreenSpot Pro

    Gemini 3.1 Pro84.4%
    Source
    MiniMax M2.7—

    Not directly comparable

  • ZeroBench

    Gemini 3.1 Pro29.0%
    Source
    MiniMax M2.7—

    Not directly comparable

  • MedXpertQA (MM)

    Gemini 3.1 Pro81.3%
    Source
    MiniMax M2.7—

    Not directly comparable

Questions

Which is better, Gemini 3.1 Pro or MiniMax M2.7?

Gemini 3.1 Pro has the higher public score estimate, 70.14 versus 55.01, 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 3.1 Pro or MiniMax M2.7?

MiniMax M2.7 scores higher for coding on the public lane, 48.5 to 46.1. MiniMax M2.7 is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like. BenchLM does not name a winner for coding from a directional reading; read the shared benchmark rows directly and test the models on the same task set.

Which is better for agentic tasks, Gemini 3.1 Pro or MiniMax M2.7?

MiniMax M2.7 scores higher for agentic tasks on the public lane, 41 to 39.7. MiniMax M2.7 is scored on Estimated evidence for agentic tasks, so the reading is directional rather than like-for-like. BenchLM does not name a winner for agentic tasks from a directional reading; read the shared benchmark rows directly and test the models on the same task set.

Which costs less, Gemini 3.1 Pro or MiniMax M2.7?

For the stated presets, chat costs $0.008 on Gemini 3.1 Pro and $0.0009 on MiniMax M2.7; repository review costs $0.136 and $0.0186; the cache-heavy agent loop costs $0.2 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 3.1 Pro or MiniMax M2.7?

Gemini 3.1 Pro has the larger documented context window: 1M, compared with 200K.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated September 18, 2026

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