LFM2.5-8B-A1B released
LiquidAI · Model release
Source confirmed
Data as of October 14, 2026 · How the score is built
CurrentOpen WeightReasoning
Released May 28, 2026128K context
LFM2.5-8B-A1B
Decision readingLFM2.5-8B-A1B scores 30.2 out of 100 and ranks #177 of 221. This profile shows 7 source-displayable benchmark rows; its strongest eligible category is Instruction Following at #119. Published weights can be self-hosted, but infrastructure cost varies and is not a comparable API token rate.
Released May 28, 2026 — see all recent releases
LFM2.5-8B-A1B will be repriced, updated or retired. Get each notice with its source and date. Follow model changes
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
30.2/100
field median 50#177 of 221 ranked models
Public
#177of 221
Verified —
Price
Self-hosted; infrastructure cost varies
input median $0.78No comparable first-party hosted token rate
Speed
335tok/s
field median 86 tok/sFirst token 7.62 s
Context
128Ktokens
field median 256,000Reported for this model; direct source link not stored
Strongest published evidence
Instruction Following ranks #119. 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.
Source-linked · 7 displayable benchmark rows
Follow model changesCategory 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 | Score | Rank | Percentile | Weight | Benchmarks | Evidence |
|---|---|---|---|---|---|---|
| AgenticWeight 22%2 benchmarksVerified | Score pending | 2 benchmarks | Verified | |||
| CodingWeight 20%0 benchmarksNot measured | Not measured | 0 benchmarks | Not measured | |||
| ReasoningWeight 17%0 benchmarksNot measured | Not measured | 0 benchmarks | Not measured | |||
| MultimodalWeight 12%0 benchmarksNot measured | Not measured | 0 benchmarks | Not measured | |||
| KnowledgeWeight 12%0 benchmarksNot measured | Not measured | 0 benchmarks | Not measured | |||
| MultilingualWeight 7%0 benchmarksNot measured | Not measured | 0 benchmarks | Not measured | |||
| Inst. FollowingRank #119 of 144Percentile 17thWeight 5%2 benchmarksVerified | 36.5 | 2 benchmarks | Verified | |||
| MathRank Not rankedWeight 5%3 benchmarksVerified | 24.0 | 3 benchmarks | Verified |
7 of 671 tracked benchmark slots have displayable evidence · bars run 0–100
Coverage detailsHow 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.
- Agentic2/2 verified
- CodingNot measured
- ReasoningNot measured
- MultimodalNot measured
- KnowledgeNot measured
- MultilingualNot measured
- Inst. Following2/2 verified
- Math3/3 verified
Verified sourceProvisionalNot measured
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
- AgenticNot eligible
- CodingNot eligible
- ReasoningNot eligible
- MultimodalNot eligible
- KnowledgeNot eligible
- MultilingualNot eligible
- Instruction following17th percentile
- MathNot eligible
The dashed outline is median of 6 nearest peers.
Eligible category ranks
- AgenticNot ranked
- CodingNot ranked
- ReasoningNot ranked
- MultimodalNot ranked
- KnowledgeNot ranked
- MultilingualNot ranked
- Inst. Following#119/144
- MathNot ranked
Top decileTop quartileMid-fieldNot eligible
Peer-median outline not drawn: no peer percentiles in this snapshotBenchmark 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
| Benchmark | Score | Versus best verified row | Gap | Weight | Evidence |
|---|---|---|---|---|---|
| BFCL v4Berkeley Function Calling Leaderboard v4 | Score49.7% | Versus best verified row Best verified: BTL-3 · 88.5% | Gap38.8 behind | Weight3% ref. weight | Provider exact |
| τ²-bench resultsτ²-Bench Tool-Agent-User Evaluation | Score88.1% | Versus best verified row Best verified: GPT-5.4 · 98.9% | Gap10.8 behind | WeightDisplay only | Provider exact |
Inst. Following2 rows
| Benchmark | Score | Versus best verified row | Gap | Weight | Evidence |
|---|---|---|---|---|---|
| IFBenchInstruction Following Benchmark | Score56.5% | Versus best verified row Best verified: MAI-Thinking-1 · 85% | Gap28.5 behind | WeightWeighted 70% | Provider exact |
| IFEvalInstruction-Following Eval | Score91.8% | Versus best verified row Best verified: LFM2.5-24B-A2B · 96.8% | Gap4.9 behind | WeightDisplay only | Provider exact |
Math3 rows
| Benchmark | Score | Versus best verified row | Gap | Weight | Evidence |
|---|---|---|---|---|---|
| AIME26AIME 2026 | Score50.0% | Versus best verified row Best verified: GLM-5.2 · 99.2% | Gap49.2 behind | WeightWeighted 25% | Provider exact |
| MATH-500MATH-500 Problem Set | Score88.8% | Versus best verified row Best verified: Ternary Bonsai 2 27B · 98.8% | Gap10 behind | WeightDisplay only | Provider exact |
| AIME 2025American Invitational Mathematics Examination 2025 | Score42.5% | Versus best verified row Best verified: GPT-5.2 · 100% | Gap57.5 behind | WeightDisplay only | Provider exact |
Bars run 0–100; the dark tick marks the best source-verified value
All 7 rowsLineage
The sequence follows explicit supersedes links. Each score is estimated for that model; a relative can inform a sparse estimate but never sets a floor, so a newer release can score below an earlier one. Scores and prices remain blank when the corresponding public row or first-party rate is unavailable.
Reasoning
Radar
Full release historyLFM2.5-8B-A1B release history
Radar confirmed these at the source. Use LFM2.5-8B-A1B in your work? Explore Radar to follow supported changes and choose your alerts.
Radar
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
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 #177 of 221 on the public leaderboard with a score of 30.19/100. 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 671 tracked benchmark slots currently have displayable evidence. Missing categories stay blank.
Its strongest eligible category is Instruction Following at #119. a well-rounded choice across a range of tasks.
Last updated October 14, 2026. Runtime fields remain blank until a sourced snapshot exists.
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 parametersQuestions
How does LFM2.5-8B-A1B perform overall in AI benchmarks?
LFM2.5-8B-A1B ranks #177 out of 221 models on the public BenchAlign leaderboard, with a score of 30.19/100. 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 has source-displayable benchmark coverage for agentic tool use and computer tasks, 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 instruction following?
LFM2.5-8B-A1B ranks #119 out of 144 eligible models for instruction following, with a public category score of 36.5/100. 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 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 16 source-displayable rows across 671 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.