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Gemini Decision 1 vs MiniMax M3.1 Flash

Updated November 11, 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

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 resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

Model A
Google logo
Gemini Decision 1

Google

—

Evidence status unavailable

90% interval unavailable

Model B
MiniMax logo
MiniMax M3.1 Flash

MiniMax

60.06/100

Estimated · Public rank #53

Conditional range 45.7–74.4

API price per 1M tokens

Gemini Decision 1: $0.10 input / $0 output

MiniMax M3.1 Flash: $0.15 input / $0.60 output

Context window

Gemini Decision 1: 262K

MiniMax M3.1 Flash: 1M

Released

Gemini Decision 1: November 11, 2026

MiniMax M3.1 Flash: October 19, 2026

Shared results
0
Gemini Decision 1 only
0
MiniMax M3.1 Flash only
14
Like-for-like categories
0 / 8
Estimated: MiniMax M3.1 Flash. Conditional ranges do not establish rank confidence.How 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

    MiniMax M3.1 Flash

    MiniMax M3.1 Flash has the larger documented context window.

    Confidence: documented

  • Chat turn cost

    1K fresh input + 500 output tokens

    Gemini Decision 1

    Gemini Decision 1 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.

    Confidence: listed-rates

  • Cache-heavy agent loop cost

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

    MiniMax M3.1 Flash

    MiniMax M3.1 Flash has the lower estimated token cost for this stated workload. Gemini Decision 1 has no published cached-input rate, so cached tokens use its listed input rate.

    Confidence: rate-fallback
Show secondary and unsupported calls

  • Repository review cost

    50K fresh input + 3K output tokens

    Gemini Decision 1

    Gemini Decision 1 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

    Gemini Decision 1 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

    Gemini Decision 1 is not ranked on the public lane for agentic, so no winner is named for agentic.

    Confidence: limited

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.

—Gemini Decision 153.9MiniMax M3.1 Flash

Not comparable · BenchAlign v5.8

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

Or describe your own job

The LLM Selector reads what you need a model for and returns a shortlist, ranked in the same order the job pages publish.

Describe the task and constraints. This selector recommends models; it cannot carry out the task or access your files and accounts.

We keep what you type here, drafts included, for 180 days, and after that only the job and requirements read from it. Nothing is tied to your IP address or account. Leave out names and other personal details. Privacy

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 v5.8 lane for agentic, coding, reasoning, multimodal, 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
Gemini Decision 1
Not ranked
MiniMax M3.1 Flash
55.6
Estimated · #42/132
Basis
BenchAlign v5.8 lane · 0 vs 8 public rows
Reading
Not comparable

Coding

Not comparable
Gemini Decision 1
Not ranked
MiniMax M3.1 Flash
53.9
Supported · #37/158
Basis
BenchAlign v5.8 lane · 0 vs 3 public rows
Reading
Not comparable

Reasoning

Not comparable
Gemini Decision 1
Not ranked
MiniMax M3.1 Flash
55.1
Estimated · #45/158
Basis
BenchAlign v5.8 lane · 0 vs 0 public rows
Reading
Not comparable

Multimodal

Not comparable
Gemini Decision 1
Not ranked
MiniMax M3.1 Flash
64.6
Supported · #26/67
Basis
BenchAlign v5.8 lane · 0 vs 2 public rows
Reading
Not comparable

Knowledge

Not comparable
Gemini Decision 1
Not ranked
MiniMax M3.1 Flash
63.8
Supported · #34/179
Basis
BenchAlign v5.8 lane · 0 vs 1 public rows
Reading
Not comparable

Multilingual

Not comparable
Gemini Decision 1
Not ranked
MiniMax M3.1 Flash
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Instruction following

Not comparable
Gemini Decision 1
Not ranked
MiniMax M3.1 Flash
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Math

Not comparable
Gemini Decision 1
Not ranked
MiniMax M3.1 Flash
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, reasoning, multimodal, and knowledge denominators (BenchAlign v5.8) 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

Gemini Decision 1
$0.0001
Fits in one request
MiniMax M3.1 Flash
$0.00045
Fits in one request

Gemini Decision 1 has the lower modeled cost

Costs use the listed standard API rates.

Repository review

50K fresh input + 3K output tokens

Gemini Decision 1
$0.005
Fits in one request
MiniMax M3.1 Flash
$0.0093
Fits in one request

Gemini Decision 1 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 Decision 1
$0.022
Fits in one request
Cached input priced at the published list-input rate
MiniMax M3.1 Flash
$0.012
Fits in one request

MiniMax M3.1 Flash has the lower modeled cost

Gemini Decision 1 has no published cached-input rate, so cached tokens use its listed input rate.

Your workload

Enter a month of tokens to price it at both models’ listed API rates.

Gemini Decision 1
$1.00
per month
MiniMax M3.1 Flash
$2.70
per month

Standard listed rates, no batch or volume discounts.

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.

Reasoning profile

Gemini Decision 1

Unspecified

MiniMax M3.1 Flash

Reasoning

Weight access

Gemini Decision 1

Proprietary

MiniMax M3.1 Flash

Proprietary

License

Gemini Decision 1

Proprietary

MiniMax M3.1 Flash

Proprietary

Release date

Gemini Decision 1

2026-11-11

MiniMax M3.1 Flash

2026-10-19

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
Repository review: $0.005 vs $0.0093. Cache-heavy agent loop: $0.022 vs $0.012.
Context tradeoff
MiniMax M3.1 Flash has the larger documented window (1M).
Run the same representative tasks against both endpoints before changing production traffic.

Questions

Which is better, Gemini Decision 1 or MiniMax M3.1 Flash?

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, Gemini Decision 1 or MiniMax M3.1 Flash?

Gemini Decision 1 is not ranked on the public lane for coding, so no winner is named for coding.

Which is better for agentic tasks, Gemini Decision 1 or MiniMax M3.1 Flash?

Gemini Decision 1 is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.

Which costs less, Gemini Decision 1 or MiniMax M3.1 Flash?

For the stated presets, chat costs $0.0001 on Gemini Decision 1 and $0.00045 on MiniMax M3.1 Flash; repository review costs $0.005 and $0.0093; the cache-heavy agent loop costs $0.022 and $0.012. Gemini Decision 1 has no published cached-input rate, so cached tokens use its listed input rate.

Which has the larger context window, Gemini Decision 1 or MiniMax M3.1 Flash?

MiniMax M3.1 Flash has the larger documented context window: 1M, compared with 262K.

Benchmark evidence

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

Browse raw public benchmark evidence14 rows

Agentic

  • LOCA-Bench 256K

    Gemini Decision 1—
    MiniMax M3.1 Flash60.3
    Source

    Not directly comparable

  • SWE Refactor Bench

    Gemini Decision 1—
    MiniMax M3.1 Flash23.0
    Source

    Not directly comparable

  • AutomationBench

    Gemini Decision 1—
    MiniMax M3.1 Flash62.2%
    Source

    Not directly comparable

  • BankerToolBench

    Gemini Decision 1—
    MiniMax M3.1 Flash76.2%
    Source

    Not directly comparable

  • Terminal-Bench 4.0

    Gemini Decision 1—
    MiniMax M3.1 Flash38.10%
    Source

    Not directly comparable

  • Agents' Last Exam

    Gemini Decision 1—
    MiniMax M3.1 Flash29.6%
    Source

    Not directly comparable

  • OSWorld 2.0

    Gemini Decision 1—
    MiniMax M3.1 Flash61.7%
    Source

    Not directly comparable

  • CyberGym

    Gemini Decision 1—
    MiniMax M3.1 Flash82.2%
    Source

    Not directly comparable

Coding

  • PostTrainBench v1.1

    Gemini Decision 1—
    MiniMax M3.1 Flash39.3%
    Source

    Not directly comparable

  • DeepSWE

    Gemini Decision 1—
    MiniMax M3.1 Flash73.7%
    Source

    Not directly comparable

  • NL2Repo

    Gemini Decision 1—
    MiniMax M3.1 Flash61.2%
    Source

    Not directly comparable

Multimodal

  • BabyVision w/ Python

    Gemini Decision 1—
    MiniMax M3.1 Flash91.0%
    Source

    Not directly comparable

  • CharXiv

    Gemini Decision 1—
    MiniMax M3.1 Flash82.5%
    Source

    Not directly comparable

Knowledge

  • HLE w/o tools

    Gemini Decision 1—
    MiniMax M3.1 Flash43.4%
    Source

    Not directly comparable

14 public results · 0 shared

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Last updated November 11, 2026