Gemma 4 12B vs MiniMax M2.7

Updated October 20, 2026. Rank says MiniMax M2.7 is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.

Decision reading

MiniMax M2.7 has the higher public point estimate, 48.15 versus 36.1. Their conditional score ranges overlap. These ranges do not establish rank confidence. 2 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

Model A

Gemma 4 12B

Google

36.1/100

Estimated · Public rank #155

Conditional range 21.8–50.5

Model B

MiniMax M2.7

MiniMax

48.15/100

Supported · Public rank #107

90% interval 36.8–59.5

Shared results: 2
Gemma 4 12B only: 10
MiniMax M2.7 only: 27
Like-for-like categories: 1 / 8

Estimated: Gemma 4 12B · Supported: MiniMax M2.7. 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
Gemma 4 12B
Gemma 4 12B 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
Gemma 4 12B 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
Gemma 4 12B is not ranked on the public lane for agentic, so no winner is named for agentic.
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. Gemma 4 12B 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

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.

Pick the job you would use them for

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

22.7Gemma 4 12B34.4MiniMax M2.7

Directional only · BenchAlign v5.8

MiniMax M2.7 has the higher coding point estimate. Conditional score ranges do not establish rank confidence.

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

Open the coding leaderboard Shortlist models for this job

Same basis rules as the category table below

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.

AIME26Math
Normalized gap 12.3
Gemma 4 12B:77.5%
MiniMax M2.7:89.8%
Gemma 4 12B source MiniMax M2.7 source

Bars run 0–100 on each benchmark’s normalized display scale

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, 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

Gemma 4 12B: 35.4; Supported · #137/187
MiniMax M2.7: 46.0; Supported · #95/187
Basis: BenchAlign v5.8 lane · 5 vs 5 public rows
Reading: MiniMax M2.7 leads · intervals overlap

Coding

Directional only

Gemma 4 12B: 22.7; Estimated · #115/152
MiniMax M2.7: 34.4; Supported · #81/152
Basis: BenchAlign v5.8 lane · 1 vs 12 public rows
Reading: Directional only

Instruction following

Directional only

Gemma 4 12B: 90.1; #25/149
MiniMax M2.7: 93.0; #13/149
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Directional only

Agentic

Not comparable

Gemma 4 12B: Not ranked
MiniMax M2.7: 32.3; Supported · #93/131
Basis: BenchAlign v5.8 lane · 0 vs 8 public rows
Reading: Not comparable

Reasoning

Not comparable

Gemma 4 12B: 51.8; Unranked · 3 rankable rows
MiniMax M2.7: 78.8; Unranked · 2 rankable rows
Basis: Provisional lane · 1 vs 0 weighted rows
Reading: Not comparable

Multimodal

Not comparable

Gemma 4 12B: 59.4; #45/55
MiniMax M2.7: Not ranked
Basis: Provisional lane · 1 vs 0 weighted rows
Reading: Not comparable

Multilingual

Not comparable

Gemma 4 12B: Not ranked
MiniMax M2.7: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Math

Not comparable

Gemma 4 12B: 61.3; Unranked · 1 rankable row
MiniMax M2.7: 70.5; Unranked · 4 rankable rows
Basis: Provisional lane · 1 vs 2 weighted rows
Reading: Not comparable

Category scores with the server-provided evidence basis for Gemma 4 12B and MiniMax M2.7
Category	Gemma 4 12B	MiniMax M2.7	Basis	Reading
Knowledge	35.4Supported · #137/187	46.0Supported · #95/187	Like-for-likeBenchAlign v5.8 lane · 5 vs 5 public rows	MiniMax M2.7 leads · intervals overlap
Coding	22.7Estimated · #115/152	34.4Supported · #81/152	Directional onlyBenchAlign v5.8 lane · 1 vs 12 public rows	Directional only
Instruction following	90.1#25/149	93.0#13/149	Directional onlyProvisional lane · 0 vs 0 weighted rows	Directional only
Agentic	Not ranked	32.3Supported · #93/131	Not comparableBenchAlign v5.8 lane · 0 vs 8 public rows	Not comparable
Reasoning	51.8Unranked · 3 rankable rows	78.8Unranked · 2 rankable rows	Not comparableProvisional lane · 1 vs 0 weighted rows	Not comparable
Multimodal	59.4#45/55	Not ranked	Not comparableProvisional lane · 1 vs 0 weighted rows	Not comparable
Multilingual	Not ranked	Not ranked	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Math	61.3Unranked · 1 rankable row	70.5Unranked · 4 rankable rows	Not comparableProvisional lane · 1 vs 2 weighted rows	Not comparable

Ranks count the models scored in each category’s lane, so the agentic, coding, 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

Gemma 4 12B: API rate not published; Fits in one request
MiniMax M2.7: $0.0009; Fits in one request

Gemma 4 12B has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

Gemma 4 12B: API rate not published; Fits in one request
MiniMax M2.7: $0.0186; Fits in one request

Gemma 4 12B has no comparable published API token rate.

Cache-heavy agent loop

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

Gemma 4 12B: API rate not published; 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. Gemma 4 12B has no comparable published API token 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.

SpecificationGemma 4 12BMiniMax M2.7

Context window

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

Gemma 4 12B

256K

Google Gemma 4 model documentation

MiniMax M2.7

200K

Gemma 4 12B

Reasoning

MiniMax M2.7

Non-Reasoning

Weight access

Gemma 4 12B

Open Weight

MiniMax M2.7

Open Weight

License

Gemma 4 12B

Open Weight

MiniMax M2.7

Open Weight

Release date

Gemma 4 12B

2026-06-03

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 point estimate, 48.15 versus 36.1. Their conditional score ranges overlap. These ranges do not establish rank confidence.
Workload cost: A complete comparable API-rate estimate is not available for both models.
Context tradeoff: Gemma 4 12B has the larger documented window (256K).

Run the same representative tasks against both endpoints before changing production traffic.

Questions

Which is better, Gemma 4 12B or MiniMax M2.7?

MiniMax M2.7 has the higher public point estimate, 48.15 versus 36.1. Their conditional score ranges overlap. These ranges do not establish rank confidence. The page therefore keeps the decision tied to the specific documented workload.

Which is better for coding, Gemma 4 12B or MiniMax M2.7?

MiniMax M2.7 scores higher for coding on the public lane, 34.4 to 22.7. Gemma 4 12B 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, Gemma 4 12B or MiniMax M2.7?

Gemma 4 12B is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.

Which costs less, Gemma 4 12B 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, Gemma 4 12B or MiniMax M2.7?

Gemma 4 12B has the larger documented context window: 256K, 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 evidence39 rows

Agentic

Terminal-Bench 2.0
Gemma 4 12B—
MiniMax M2.757%
Source
Not directly comparable
τ³-bench results
Gemma 4 12B—
MiniMax M2.767.6%
Source
Not directly comparable
Toolathlon
Gemma 4 12B—
MiniMax M2.746.3%
Source
Not directly comparable
MLE-Bench Lite
Gemma 4 12B—
MiniMax M2.766.6%
Source
Not directly comparable
MM-ClawBench
Gemma 4 12B—
MiniMax M2.762.7%
Source
Not directly comparable
Claw-Eval
Gemma 4 12B—
MiniMax M2.748.7%
Source
Not directly comparable
Gert Labs
Gemma 4 12B—
MiniMax M2.740.40%
Source
Not directly comparable
Terminal-Bench 2.1 (Vals)
Gemma 4 12B—
MiniMax M2.748.7%
Source
Not directly comparable

Coding

LiveCodeBench v6
Gemma 4 12B72.0%
Source
MiniMax M2.7—
Not directly comparable
SWE-bench Verified*
Gemma 4 12B—
MiniMax M2.775.4%
Source
Not directly comparable
SWE-bench Pro
Gemma 4 12B—
MiniMax M2.756.2%
Source
Not directly comparable
SWE-Rebench
Gemma 4 12B—
MiniMax M2.751.9%
Source
Not directly comparable
SWE Multilingual
Gemma 4 12B—
MiniMax M2.776.5%
Source
Not directly comparable
Multi-SWE Bench
Gemma 4 12B—
MiniMax M2.752.7%
Source
Not directly comparable
VIBE-Pro
Gemma 4 12B—
MiniMax M2.755.6%
Source
Not directly comparable
NL2Repo
Gemma 4 12B—
MiniMax M2.739.8%
Source
Not directly comparable
Terminal-Bench 2.0
Gemma 4 12B—
MiniMax M2.757.0%
Source
Not directly comparable
Vibe Code Bench
Gemma 4 12B—
MiniMax M2.727.04%
Source
Not directly comparable
React Native Evals
Gemma 4 12B—
MiniMax M2.771.4%
Source
Not directly comparable
LiveCodeBench (Vals)
Gemma 4 12B—
MiniMax M2.779.9%
Source
Not directly comparable
SWE-bench (Vals)
Gemma 4 12B—
MiniMax M2.773.8%
Source
Not directly comparable

Reasoning

MRCRv2
Gemma 4 12B43.4%
Source
MiniMax M2.7—
Not directly comparable

Multimodal

ExtractBench (LlamaIndex)
Gemma 4 12B45.39%
Source
MiniMax M2.7—
Not directly comparable
MMMU-Pro
Gemma 4 12B69.1%
Source
MiniMax M2.7—
Not directly comparable
MathVision
Gemma 4 12B79.7%
Source
MiniMax M2.7—
Not directly comparable
MedXpertQA (MM)
Gemma 4 12B48.7%
Source
MiniMax M2.7—
Not directly comparable

Knowledge

GPQA
Gemma 4 12B78.8%
Source
MiniMax M2.7—
Not directly comparable
GPQA-D
Gemma 4 12B78.8%
Source
MiniMax M2.787.0%
Source
MiniMax M2.7 leads this result
MMLU-Pro
Gemma 4 12B77.2%
Source
MiniMax M2.7—
Not directly comparable
HLE w/o tools
Gemma 4 12B5.2%
Source
MiniMax M2.7—
Not directly comparable
MMMLU
Gemma 4 12B83.4%
Source
MiniMax M2.7—
Not directly comparable
HLE
Gemma 4 12B—
MiniMax M2.728%
Source
Not directly comparable
MMLU-Pro (Arcee)
Gemma 4 12B—
MiniMax M2.780.8%
Source
Not directly comparable
GPQA Diamond (Vals)
Gemma 4 12B—
MiniMax M2.786.6%
Source
Not directly comparable
MMLU-Pro (Vals)
Gemma 4 12B—
MiniMax M2.780.4%
Source
Not directly comparable

Math

AIME26
Gemma 4 12B77.5%
Source
MiniMax M2.789.8%
Source
MiniMax M2.7 leads this result
AIME25 (Arcee)
Gemma 4 12B—
MiniMax M2.780.0%
Source
Not directly comparable
HMMT Nov 2025
Gemma 4 12B—
MiniMax M2.781.0%
Source
Not directly comparable
HMMT Feb 2026
Gemma 4 12B—
MiniMax M2.772.7%
Source
Not directly comparable

39 public results · 2 shared

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Last updated October 20, 2026

Gemma 4 12B vs MiniMax M2.7

Decision reading

Which one for your work

Long documents

Coding work

Agentic work

Chat turn cost

Cache-heavy agent loop cost

Repository review cost

Which one for a specific job

What is actually comparable

Shape of the matched evidence

Category results, on a stated basis

Knowledge

Coding

Instruction following

Agentic

Reasoning

Multimodal

Multilingual

Math

What each workload costs

Chat turn

Repository review

Cache-heavy agent loop

Specification differences

Context window

API model ID

Cached-input rate

Documented inputs

Documented outputs

Provider availability

Reasoning profile

Weight access

License

Release date

If you already use one of these models

Questions

Which is better, Gemma 4 12B or MiniMax M2.7?

Which is better for coding, Gemma 4 12B or MiniMax M2.7?

Which is better for agentic tasks, Gemma 4 12B or MiniMax M2.7?

Which costs less, Gemma 4 12B or MiniMax M2.7?

Which has the larger context window, Gemma 4 12B or MiniMax M2.7?

Benchmark evidence

Agentic

Coding

Reasoning

Multimodal

Knowledge

Math

Related comparisons

Watch Gemma 4 12B vs MiniMax M2.7