LFM2.5-230M vs MiniMax M2.7

Updated September 27, 2026. We do not rank this pair: at least one has no public score. Public scores include evidence status and uncertainty.

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

LFM2.5-230M

LiquidAI

—

Evidence status unavailable

90% interval unavailable

Model B

MiniMax M2.7

MiniMax

48.08/100

Supported · Public rank #83

90% interval 37.2–58.9

Shared results: 1
LFM2.5-230M only: 5
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
MiniMax M2.7
MiniMax M2.7 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
LFM2.5-230M 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
LFM2.5-230M 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. LFM2.5-230M does not fit this 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. LFM2.5-230M has no comparable published API token rate.
Confidence: rate-fallback
Repository review cost
50K fresh input + 3K 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. LFM2.5-230M does not fit this workload in one request. LFM2.5-230M has no comparable published API token rate.
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.

—LFM2.5-230M36.1MiniMax 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 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.

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.

Instruction following

Directional only

LFM2.5-230M: 10.4; #123/124
MiniMax M2.7: 91.6; #10/124
Basis: Provisional lane · 1 vs 0 weighted rows
Reading: Directional only

Agentic

Not comparable

LFM2.5-230M: Not ranked
MiniMax M2.7: 25.2; Supported · #79/105
Basis: BenchAlign v5.7 lane · 1 vs 7 public rows
Reading: Not comparable

Coding

Not comparable

LFM2.5-230M: Not ranked
MiniMax M2.7: 36.1; Supported · #70/135
Basis: BenchAlign v5.7 lane · 0 vs 11 public rows
Reading: Not comparable

Reasoning

Not comparable

LFM2.5-230M: 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

LFM2.5-230M: Not ranked
MiniMax M2.7: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Knowledge

Not comparable

LFM2.5-230M: 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

LFM2.5-230M: Not ranked
MiniMax M2.7: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Math

Not comparable

LFM2.5-230M: Not ranked
MiniMax M2.7: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Category scores with the server-provided evidence basis for LFM2.5-230M and MiniMax M2.7
Category	LFM2.5-230M	MiniMax M2.7	Basis	Reading
Instruction following	10.4#123/124	91.6#10/124	Directional onlyProvisional lane · 1 vs 0 weighted rows	Directional only
Agentic	Not ranked	25.2Supported · #79/105	Not comparableBenchAlign v5.7 lane · 1 vs 7 public rows	Not comparable
Coding	Not ranked	36.1Supported · #70/135	Not comparableBenchAlign v5.7 lane · 0 vs 11 public rows	Not comparable
Reasoning	Not ranked	75.9Unranked · 2 rankable rows	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Multimodal	Not ranked	Not ranked	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Knowledge	Not ranked	43.0Supported · #79/158	Not comparableBenchAlign v5.7 lane · 3 vs 4 public rows	Not comparable
Multilingual	Not ranked	Not ranked	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Math	Not ranked	Not ranked	Not comparableProvisional lane · 0 vs 0 weighted rows	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

LFM2.5-230M: Self-hosted; infrastructure cost varies; Fits in one request
MiniMax M2.7: $0.0009; Fits in one request

LFM2.5-230M has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

LFM2.5-230M: Self-hosted; infrastructure cost varies; Does not fit in one request
MiniMax M2.7: $0.0186; Fits in one request

LFM2.5-230M does not fit this workload in one request. LFM2.5-230M has no comparable published API token rate.

Cache-heavy agent loop

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

LFM2.5-230M: Self-hosted; infrastructure cost varies; Does not fit 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

LFM2.5-230M does not fit this 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. LFM2.5-230M 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.

SpecificationLFM2.5-230MMiniMax M2.7

Context window

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

LFM2.5-230M

32K

MiniMax M2.7

200K

API model ID

LFM2.5-230M

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.

LFM2.5-230M

No comparable hosted API rate

MiniMax M2.7

Not published

Documented inputs

LFM2.5-230M

Not sourced

MiniMax M2.7

Not sourced

Documented outputs

LFM2.5-230M

Not sourced

MiniMax M2.7

Not sourced

Provider availability

LFM2.5-230M

Not sourced

MiniMax M2.7

Not sourced

Reasoning profile

LFM2.5-230M

Non-Reasoning

MiniMax M2.7

Non-Reasoning

Weight access

LFM2.5-230M

Open Weight

MiniMax M2.7

Open Weight

License

LFM2.5-230M

Open Weight

MiniMax M2.7

Open Weight

Release date

LFM2.5-230M

2026-06-25

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: A complete comparable API-rate estimate is not available for both models.
Context tradeoff: MiniMax M2.7 has the larger documented window (200K).

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

Questions

Which is better, LFM2.5-230M 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, LFM2.5-230M or MiniMax M2.7?

LFM2.5-230M is not ranked on the public lane for coding, so no winner is named for coding.

Which is better for agentic tasks, LFM2.5-230M or MiniMax M2.7?

LFM2.5-230M is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.

Which costs less, LFM2.5-230M 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, LFM2.5-230M or MiniMax M2.7?

MiniMax M2.7 has the larger documented context window: 200K, compared with 32K.

Benchmark evidence

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

Browse raw public benchmark evidence28 rows

Agentic

BFCL v4
LFM2.5-230M21.0%
Source
MiniMax M2.7—
Not directly comparable
Terminal-Bench 2.0
LFM2.5-230M—
MiniMax M2.757%
Source
Not directly comparable
Toolathlon
LFM2.5-230M—
MiniMax M2.746.3%
Source
Not directly comparable
MLE-Bench Lite
LFM2.5-230M—
MiniMax M2.766.6%
Source
Not directly comparable
MM-ClawBench
LFM2.5-230M—
MiniMax M2.762.7%
Source
Not directly comparable
Claw-Eval
LFM2.5-230M—
MiniMax M2.748.7%
Source
Not directly comparable
Gert Labs
LFM2.5-230M—
MiniMax M2.740.40%
Source
Not directly comparable
Terminal-Bench 2.1 (Vals)
LFM2.5-230M—
MiniMax M2.748.7%
Source
Not directly comparable

Coding

SWE-bench Verified*
LFM2.5-230M—
MiniMax M2.775.4%
Source
Not directly comparable
SWE-bench Pro
LFM2.5-230M—
MiniMax M2.756.2%
Source
Not directly comparable
SWE-Rebench
LFM2.5-230M—
MiniMax M2.751.9%
Source
Not directly comparable
SWE Multilingual
LFM2.5-230M—
MiniMax M2.776.5%
Source
Not directly comparable
Multi-SWE Bench
LFM2.5-230M—
MiniMax M2.752.7%
Source
Not directly comparable
VIBE-Pro
LFM2.5-230M—
MiniMax M2.755.6%
Source
Not directly comparable
NL2Repo
LFM2.5-230M—
MiniMax M2.739.8%
Source
Not directly comparable
Vibe Code Bench
LFM2.5-230M—
MiniMax M2.727.04%
Source
Not directly comparable
React Native Evals
LFM2.5-230M—
MiniMax M2.771.4%
Source
Not directly comparable
LiveCodeBench (Vals)
LFM2.5-230M—
MiniMax M2.779.9%
Source
Not directly comparable
SWE-bench (Vals)
LFM2.5-230M—
MiniMax M2.773.8%
Source
Not directly comparable

Knowledge

GPQA
LFM2.5-230M25.4%
Source
MiniMax M2.7—
Not directly comparable
GPQA-D
LFM2.5-230M25.4%
Source
MiniMax M2.787.0%
Source
MiniMax M2.7 leads this result
MMLU-Pro
LFM2.5-230M20.3%
Source
MiniMax M2.7—
Not directly comparable
MMLU-Pro (Arcee)
LFM2.5-230M—
MiniMax M2.780.8%
Source
Not directly comparable
GPQA Diamond (Vals)
LFM2.5-230M—
MiniMax M2.786.6%
Source
Not directly comparable
MMLU-Pro (Vals)
LFM2.5-230M—
MiniMax M2.780.4%
Source
Not directly comparable

Instruction following

IFEval
LFM2.5-230M71.7%
Source
MiniMax M2.7—
Not directly comparable
IFBench
LFM2.5-230M38.4%
Source
MiniMax M2.7—
Not directly comparable

Math

AIME25 (Arcee)
LFM2.5-230M—
MiniMax M2.780.0%
Source
Not directly comparable

28 public results · 1 shared

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Last updated September 27, 2026

LFM2.5-230M 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

Instruction following

Agentic

Coding

Reasoning

Multimodal

Knowledge

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, LFM2.5-230M or MiniMax M2.7?

Which is better for coding, LFM2.5-230M or MiniMax M2.7?

Which is better for agentic tasks, LFM2.5-230M or MiniMax M2.7?

Which costs less, LFM2.5-230M or MiniMax M2.7?

Which has the larger context window, LFM2.5-230M or MiniMax M2.7?

Benchmark evidence

Agentic

Coding

Knowledge

Instruction following

Math

Related comparisons

Watch LFM2.5-230M vs MiniMax M2.7