Agentic
Directional only- LFM2.5-2.6B
- 37.2
- Estimated · #131/154
- MiniMax M2.7
- 41.0
- Estimated · #113/154
- Basis
- BenchAlign lane · 4 vs 7 public rows
- Reading
- Directional only
LFM2.5-2.6B vs MiniMax M2.7
Decision reading
MiniMax M2.7 has the higher public score estimate, 55.01 versus 39.9, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.
1 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
Updated September 18, 2026. Rank says MiniMax M2.7 is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.
Both of these models will change. Get the price, version and retirement notices for the pair, each with its source. Follow model changes
Share or export
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
LFM2.5-2.6B and MiniMax M2.7 are 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
LFM2.5-2.6B and MiniMax M2.7 are scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.
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. LFM2.5-2.6B 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. LFM2.5-2.6B 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.
The same task catalog as the LLM Selector. Each job names the evidence surface it rests on; nothing here adds a new score.
39.2LFM2.5-2.6B48.5MiniMax 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.
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
6 LFM2.5-2.6B only
1 Shared
22 MiniMax M2.7 only
- Shared results
- 1
- LFM2.5-2.6B only
- 6
- MiniMax M2.7 only
- 22
- Like-for-like categories
- 0 / 8
4 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.
Coding
Directional only- LFM2.5-2.6B
- 39.2
- Estimated · #125/154
- MiniMax M2.7
- 48.5
- Estimated · #70/154
- Basis
- BenchAlign lane · 1 vs 11 public rows
- Reading
- Directional only
Knowledge
Directional only- LFM2.5-2.6B
- 37.0
- Estimated · #151/184
- MiniMax M2.7
- 48.7
- Supported · #93/184
- Basis
- BenchAlign lane · 0 vs 4 public rows
- Reading
- Directional only
Instruction following
Directional only- LFM2.5-2.6B
- 38.1
- #104/124
- MiniMax M2.7
- 91.6
- #10/124
- Basis
- Provisional lane · 1 vs 0 weighted rows
- Reading
- Directional only
Reasoning
Not comparable- LFM2.5-2.6B
- 24.9
- Unranked · 2 rankable rows
- MiniMax M2.7
- 74.8
- Unranked · 2 rankable rows
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- LFM2.5-2.6B
- Not ranked
- MiniMax M2.7
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- LFM2.5-2.6B
- Not ranked
- MiniMax M2.7
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- LFM2.5-2.6B
- Not ranked
- MiniMax M2.7
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
| Category | LFM2.5-2.6B | MiniMax M2.7 | Basis | Reading |
|---|---|---|---|---|
| Agentic | 37.2Estimated · #131/154 | 41.0Estimated · #113/154 | Directional onlyBenchAlign lane · 4 vs 7 public rows | Directional only |
| Coding | 39.2Estimated · #125/154 | 48.5Estimated · #70/154 | Directional onlyBenchAlign lane · 1 vs 11 public rows | Directional only |
| Knowledge | 37.0Estimated · #151/184 | 48.7Supported · #93/184 | Directional onlyBenchAlign lane · 0 vs 4 public rows | Directional only |
| Instruction following | 38.1#104/124 | 91.6#10/124 | Directional onlyProvisional lane · 1 vs 0 weighted rows | Directional only |
| Reasoning | 24.9Unranked · 2 rankable rows | 74.8Unranked · 2 rankable rows | 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 |
| Multilingual | Not ranked | Not ranked | 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 |
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.
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
- LFM2.5-2.6B
- Self-hosted; infrastructure cost varies
- Fits in one request
- MiniMax M2.7
- $0.0009
- Fits in one request
LFM2.5-2.6B has no comparable published API token rate.
Repository review
50K fresh input + 3K output tokens
- LFM2.5-2.6B
- Self-hosted; infrastructure cost varies
- Fits in one request
- MiniMax M2.7
- $0.0186
- Fits in one request
LFM2.5-2.6B has no comparable published API token rate.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- LFM2.5-2.6B
- 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
LFM2.5-2.6B 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. LFM2.5-2.6B 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.
SpecificationLFM2.5-2.6BMiniMax M2.7
Context window
Maximum documented context; output-token limits may be lower.
LFM2.5-2.6B
MiniMax M2.7
200K
API model ID
LFM2.5-2.6B
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.
LFM2.5-2.6B
No comparable hosted API rate
LiquidAI Hugging Face model cardMiniMax M2.7
Not published
Documented inputs
LFM2.5-2.6B
Not sourced
MiniMax M2.7
Not sourced
Documented outputs
LFM2.5-2.6B
Not sourced
MiniMax M2.7
Not sourced
Provider availability
LFM2.5-2.6B
Not sourced
MiniMax M2.7
Not sourced
Reasoning profile
LFM2.5-2.6B
Reasoning
MiniMax M2.7
Non-Reasoning
Weight access
LFM2.5-2.6B
Open Weight
MiniMax M2.7
Open Weight
License
LFM2.5-2.6B
Open Weight
MiniMax M2.7
Open Weight
Release date
LFM2.5-2.6B
2026-08-04
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 score estimate, 55.01 versus 39.9, but the 90% score intervals overlap.
- 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 evidence29 rows
Agentic
BFCL v4
LFM2.5-2.6B56.9%SourceMiniMax M2.7—Not directly comparable
τ³-bench results
LFM2.5-2.6B5.7%SourceMiniMax M2.7—Not directly comparable
Claw-Eval
LFM2.5-2.6B62.9%SourceMiniMax M2.748.7%SourceLFM2.5-2.6B leads this result
PinchBench
LFM2.5-2.6B68.2%SourceMiniMax M2.7—Not directly comparable
Terminal-Bench 2.0
LFM2.5-2.6B—MiniMax M2.757%SourceNot directly comparable
Toolathlon
LFM2.5-2.6B—MiniMax M2.746.3%SourceNot directly comparable
MLE-Bench Lite
LFM2.5-2.6B—MiniMax M2.766.6%SourceNot directly comparable
MM-ClawBench
LFM2.5-2.6B—MiniMax M2.762.7%SourceNot directly comparable
Gert Labs
LFM2.5-2.6B—MiniMax M2.740.40%SourceNot directly comparable
Terminal-Bench 2.1 (Vals)
LFM2.5-2.6B—MiniMax M2.748.7%SourceNot directly comparable
Coding
LiveCodeBench v6
LFM2.5-2.6B59.4%SourceMiniMax M2.7—Not directly comparable
SWE-bench Verified*
LFM2.5-2.6B—MiniMax M2.775.4%SourceNot directly comparable
SWE-bench Pro
LFM2.5-2.6B—MiniMax M2.756.2%SourceNot directly comparable
SWE-Rebench
LFM2.5-2.6B—MiniMax M2.751.9%SourceNot directly comparable
SWE Multilingual
LFM2.5-2.6B—MiniMax M2.776.5%SourceNot directly comparable
Multi-SWE Bench
LFM2.5-2.6B—MiniMax M2.752.7%SourceNot directly comparable
VIBE-Pro
LFM2.5-2.6B—MiniMax M2.755.6%SourceNot directly comparable
NL2Repo
LFM2.5-2.6B—MiniMax M2.739.8%SourceNot directly comparable
Vibe Code Bench
LFM2.5-2.6B—MiniMax M2.727.04%SourceNot directly comparable
React Native Evals
LFM2.5-2.6B—MiniMax M2.771.4%SourceNot directly comparable
LiveCodeBench (Vals)
LFM2.5-2.6B—MiniMax M2.779.9%SourceNot directly comparable
SWE-bench (Vals)
LFM2.5-2.6B—MiniMax M2.773.8%SourceNot directly comparable
Knowledge
Math
Instruction following
IFBench
LFM2.5-2.6B59.2%SourceMiniMax M2.7—Not directly comparable
Questions
Which is better, LFM2.5-2.6B or MiniMax M2.7?
MiniMax M2.7 has the higher public score estimate, 55.01 versus 39.9, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.
Which is better for coding, LFM2.5-2.6B or MiniMax M2.7?
MiniMax M2.7 scores higher for coding on the public lane, 48.5 to 39.2. LFM2.5-2.6B and MiniMax M2.7 are 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, LFM2.5-2.6B or MiniMax M2.7?
MiniMax M2.7 scores higher for agentic tasks on the public lane, 41 to 37.2. LFM2.5-2.6B and MiniMax M2.7 are 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, LFM2.5-2.6B 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, LFM2.5-2.6B or MiniMax M2.7?
MiniMax M2.7 has the larger documented context window: 200K, compared with 128K.
Related comparisons
Last updated September 18, 2026
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