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LFM2.5-8B-A1B

CurrentReleased May 28, 2026Open WeightReasoning128K context

Released May 28, 2026 see all recent releases

Decision reading
LFM2.5-8B-A1B scores 41.8 out of 100 and ranks #188 of 228. This profile shows 7 source-displayable benchmark rows; its strongest eligible category is Instruction Following at #33. Published weights can be self-hosted, but infrastructure cost varies and is not a comparable API token rate.

Data as of August 30, 2026 · How the score is built

Strongest published evidence

Instruction Following ranks #33. A well-rounded choice across a range of tasks.

Validate before choosing

7 published rows leave some tracked benchmark slots empty. No comparable first-party API token rate is published.

Decision snapshot

Each value carries a field reference instead of floating alone. Markers compare this model with the current ranked and priced catalog; they are not absolute quality thresholds.

Capability

41.8/100

field median 58.8

#188 of 228 ranked models

Price

Self-hosted; infrastructure cost varies

input median $1

No comparable first-party hosted token rate

Speed

336tok/s

field median 86 tok/s

First token 7.56 s

Context

128Ktokens

field median 256,000

Reported for this model; direct source link not stored

Capability shape

Each axis shows percentile within that category’s eligible cohort. The comparison outline is the median of the six nearest public-score peers; a collapsed vertex means the category is not rank-eligible.

LFM2.5-8B-A1B category percentile values

  • Agentic43rd percentile
  • CodingNot eligible
  • ReasoningNot eligible
  • KnowledgeNot eligible
  • MathNot eligible
  • MultilingualNot eligible
  • MultimodalNot eligible
  • Instruction following22nd percentile

The dashed outline is median of 6 nearest peers.

Eligible category ranks

  1. Agentic#80/140
  2. CodingNot ranked
  3. ReasoningNot ranked
  4. KnowledgeNot ranked
  5. MathNot ranked
  6. MultilingualNot ranked
  7. MultimodalNot ranked
  8. Inst. Following#33/42
Top decileTop quartileMid-fieldNot eligible

How much of this is verified

Coverage is split by category so a strong number never hides a thin evidence base. Verified means the row is tied to a published source; provisional rows remain visible but separate.

  1. Agentic2/2 verified
  2. CodingNot measured
  3. ReasoningNot measured
  4. KnowledgeNot measured
  5. Math3/3 verified
  6. MultilingualNot measured
  7. MultimodalNot measured
  8. Inst. Following2/2 verified
Verified sourceProvisionalNot measured

Spec sheet

Each documented value carries its source. Missing fields stay visible as not sourced or not published, rather than disappearing from the page.

API model ID
Not published
Context window
128K
Maximum output
Not sourced yet
Knowledge cutoff
Not sourced yet
Input modalities
Not sourced yet
Output modalities
Not sourced yet
Parameters
Not sourced yet
Availability
Not sourced yet
Cloud regions
Not tracked yet
Lifecycle
Current
API capabilities
Tool calling, structured outputs, and batch support are not tracked yet
Prompt caching
Not documented in the pricing record
Self-host
Open weights available; hardware estimate not sourced
Rate limits
Not tracked yet

Deployment options

Self-host and provider-specific paths stay separate from benchmark evidence so operating constraints are visible before a score becomes the whole decision.

Published weights are available, but BenchLM does not yet have a sourced parameter and VRAM profile for this exact model. Hardware cost estimates stay unavailable until that sizing record is complete.

Estimate VRAM from known parameters

Category score record

Scores and ranks appear only where published evidence can be displayed. The table keeps the score, weight, cohort, and evidence state together.

Category scores, ranks, weighting, benchmark coverage, and evidence status
CategoryScore
AgenticRank #80 of 140Percentile 43rdWeight 22%2 benchmarksVerified46.5
CodingWeight 20%0 benchmarksNot measuredNot measured
ReasoningWeight 17%0 benchmarksNot measuredNot measured
KnowledgeWeight 12%0 benchmarksNot measuredNot measured
MathRank Not rankedWeight 5%3 benchmarksVerified27.8
MultilingualWeight 7%0 benchmarksNot measuredNot measured
MultimodalWeight 12%0 benchmarksNot measuredNot measured
Inst. FollowingRank #33 of 42Percentile 22ndWeight 5%2 benchmarksVerified55.7

Benchmark ledger

Agentic opens by default. The marker compares each value with the best source-verified result in the catalog; provisional leaders do not set the reference. Expand the remaining categories for every published row.

Agentic2 rows
Agentic benchmark values, best verified comparison, weight, and source status
BFCL v4Berkeley Function Calling Leaderboard v4Score49.7%Versus best verified row

Best verified: Qwen3.7 Max · 75.0%

Gap25.3 behindWeightDisplay only
τ²-bench resultsτ²-Bench Tool-Agent-User EvaluationScore88.1%Versus best verified row

Best verified: GPT-5.4 · 98.9%

Gap10.8 behindWeightDisplay only
Math3 rows
Math benchmark values, best verified comparison, weight, and source status
AIME26AIME 2026Score50.0%Versus best verified row

Best verified: GLM-5.2 · 99.2%

Gap49.2 behindWeightWeighted 25%
MATH-500MATH-500 Problem SetScore88.8%Versus best verified row

Best verified: MiniCPM5-1B · 91.6%

Gap2.8 behindWeightDisplay only
AIME 2025American Invitational Mathematics Examination 2025Score42.5%Versus best verified row

Best verified: MAI-Thinking-1 · 97%

Gap54.5 behindWeightDisplay only
Inst. Following2 rows
Inst. Following benchmark values, best verified comparison, weight, and source status
IFBenchInstruction Following BenchmarkScore56.5%Versus best verified row

Best verified: MAI-Thinking-1 · 85%

Gap28.5 behindWeightWeighted 65%
IFEvalInstruction-Following EvalScore91.8%Versus best verified row

Best verified: Qwen3.5-27B · 95%

Gap3.2 behindWeightWeighted 35%

Lineage

The sequence follows explicit supersedes links. Scores and prices remain blank when the corresponding public row or first-party rate is unavailable.

  1. May 28, 2026 · you are here

    LFM2.5-8B-A1B

    Score 41.8 · Price not listed

Reasoning

How to read this profile

The visual layer above carries the decisions. These notes preserve the model, ranking, coverage, and family context behind the numbers.

LFM2.5-8B-A1B ranks #188 of 228 on the public leaderboard with a score of 41.82/100. It does not yet have enough sourced coverage for a verified position.

LFM2.5-8B-A1B is a open weight model with a 128K context window. It uses an explicit reasoning mode, which can improve complex problem solving while adding latency and token use.

7 of 408 tracked benchmark slots currently have displayable evidence. Missing categories stay blank.

Its strongest eligible category is Instruction Following at #33, while its lowest eligible position is Agentic at #80. a well-rounded choice across a range of tasks.

Radar

LFM2.5-8B-A1B release history

Full release history

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Frequently asked questions

How does LFM2.5-8B-A1B perform overall in AI benchmarks?

LFM2.5-8B-A1B ranks #188 out of 228 models on the public BenchAlign leaderboard, with a score of 41.82/100. Its evidence status is Estimated, and this profile shows 7 source-displayable benchmark rows. The label describes evidence depth, not a provider quality claim; inspect category rows before choosing a workload.

Is LFM2.5-8B-A1B good for mathematics?

LFM2.5-8B-A1B has source-displayable benchmark coverage for mathematics, but the public category table does not assign it a rank there. The individual rows remain available for inspection. A missing category position means the evidence threshold was not met; it does not convert the model's unmeasured work into a zero.

Is LFM2.5-8B-A1B good for agentic tool use and computer tasks?

LFM2.5-8B-A1B ranks #80 out of 140 eligible models for agentic tool use and computer tasks, with a public category score of 46.5/100. Higher-ranked alternatives are available for workloads where this category decides the choice. Check the underlying rows before treating the aggregate as a workload guarantee.

Is LFM2.5-8B-A1B good for instruction following?

LFM2.5-8B-A1B ranks #33 out of 42 eligible models for instruction following, with a public category score of 55.7/100. Higher-ranked alternatives are available for workloads where this category decides the choice. Check the underlying rows before treating the aggregate as a workload guarantee.

Is LFM2.5-8B-A1B open source?

LFM2.5-8B-A1B is an open-weight model from LiquidAI. Its weights can be downloaded for local or hosted deployment, subject to the published license. Open weight does not automatically mean open source: training data and training code may remain private, and commercial restrictions can still apply.

Does LFM2.5-8B-A1B have full benchmark coverage on BenchLM?

No. LFM2.5-8B-A1B currently has 17 source-displayable rows across 408 tracked benchmark slots. The profile exposes published, non-generated evidence and leaves missing categories blank until an exact evaluation is available. Coverage describes how much was measured; it is not a penalty added to an individual benchmark result.

What is the context window size of LFM2.5-8B-A1B?

LFM2.5-8B-A1B has a reported context window of 128K in the exact-model catalog record. The value stays visible, but the profile marks its source link as unavailable instead of presenting it as directly documented. Maximum output length remains separate because providers often publish a different limit.

Compare LFM2.5-8B-A1B with every tracked model399 comparisons

Last updated August 30, 2026. Runtime fields remain blank until a sourced snapshot exists.

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