Agentic
Not comparable- LFM2.5-230M
- Not measured
- MiniMax M2.7
- 57.0
- Weighted basis
- 0 vs 1 rows
- Reading
- Not comparable
LFM2.5-230M vs MiniMax M2.7
Updated August 13, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
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 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
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
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
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
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
5 LFM2.5-230M only
1 Shared
17 MiniMax M2.7 only
- Shared results
- 1
- LFM2.5-230M only
- 5
- MiniMax M2.7 only
- 17
- 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.
Coding
Not comparable- LFM2.5-230M
- Not measured
- MiniMax M2.7
- 53.3
- Weighted basis
- 0 vs 2 rows
- Reading
- Not comparable
Reasoning
Not comparable- LFM2.5-230M
- Not measured
- MiniMax M2.7
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Knowledge
Not comparable- LFM2.5-230M
- 21.2
- MiniMax M2.7
- Not measured
- Weighted basis
- 2 vs 0 rows
- Reading
- Not comparable
Math
Not comparable- LFM2.5-230M
- Not measured
- MiniMax M2.7
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Multilingual
Not comparable- LFM2.5-230M
- Not measured
- MiniMax M2.7
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Multimodal
Not comparable- LFM2.5-230M
- Not measured
- MiniMax M2.7
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Instruction following
Not comparable- LFM2.5-230M
- 50.1
- MiniMax M2.7
- Not measured
- Weighted basis
- 2 vs 0 rows
- Reading
- Not comparable
| Category | LFM2.5-230M | MiniMax M2.7 | Weighted basis | Reading |
|---|---|---|---|---|
| Agentic | Not measured | 57.0 | Not comparable0 vs 1 rows | Not comparable |
| Coding | Not measured | 53.3 | Not comparable0 vs 2 rows | Not comparable |
| Reasoning | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Knowledge | 21.2 | Not measured | Not comparable2 vs 0 rows | Not comparable |
| Math | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Multilingual | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Multimodal | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Instruction following | 50.1 | Not measured | Not comparable2 vs 0 rows | 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
- 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.
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.
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
BFCL v4
LFM2.5-230M21.0%SourceMiniMax M2.7—Not directly comparable
Terminal-Bench 2.0
LFM2.5-230M—MiniMax M2.757%SourceNot directly comparable
Toolathlon
LFM2.5-230M—MiniMax M2.746.3%SourceNot directly comparable
MLE-Bench Lite
LFM2.5-230M—MiniMax M2.766.6%SourceNot directly comparable
MM-ClawBench
LFM2.5-230M—MiniMax M2.762.7%SourceNot directly comparable
Claw-Eval
LFM2.5-230M—MiniMax M2.748.7%SourceNot directly comparable
Gert Labs
LFM2.5-230M—MiniMax M2.740.40%SourceNot directly comparable
Coding
SWE-bench Verified*
LFM2.5-230M—MiniMax M2.775.4%SourceNot directly comparable
SWE-bench Pro
LFM2.5-230M—MiniMax M2.756.2%SourceNot directly comparable
SWE-Rebench
LFM2.5-230M—MiniMax M2.751.9%SourceNot directly comparable
SWE Multilingual
LFM2.5-230M—MiniMax M2.776.5%SourceNot directly comparable
Multi-SWE Bench
LFM2.5-230M—MiniMax M2.752.7%SourceNot directly comparable
VIBE-Pro
LFM2.5-230M—MiniMax M2.755.6%SourceNot directly comparable
NL2Repo
LFM2.5-230M—MiniMax M2.739.8%SourceNot directly comparable
Vibe Code Bench
LFM2.5-230M—MiniMax M2.727.04%SourceNot directly comparable
React Native Evals
LFM2.5-230M—MiniMax M2.771.4%SourceNot directly comparable
Knowledge
Math
AIME25 (Arcee)
LFM2.5-230M—MiniMax M2.780.0%SourceNot directly comparable
Instruction following
Frequently asked 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?
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, LFM2.5-230M 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, 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.
Related comparisons
Last updated August 13, 2026
Watch LFM2.5-230M vs MiniMax M2.7
One weekly email when material rank, price, or benchmark evidence changes make this matchup worth revisiting.
Read a sample issueJoin 2,000+ readers.