LFM2.5-230M released
LiquidAI · Model release
Source confirmed
Data as of September 27, 2026 · How the score is built
CurrentOpen WeightNon-Reasoning
Released Jun 25, 202632K context
LFM2.5-230M
Decision readingLFM2.5-230M is tracked, but not publicly ranked yet. The profile exposes 6 sourced benchmark rows and leaves unsupported fields blank until a published record exists.
Released Jun 25, 2026 — see all recent releases
LFM2.5-230M will be repriced, updated or retired. Get each notice with its source and date. Follow model changes
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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
Unranked
field median 50.2Not eligible for a public rank
Price
Self-hosted; infrastructure cost varies
input median $0.97No comparable first-party hosted token rate
Speed
Not measured
field median 91 tok/sTime to first token not measured
Context
32Ktokens
field median 256,000Reported for this model; direct source link not stored
Strongest published evidence
Instruction Following ranks #123. A well-rounded choice across a range of tasks.
Validate before choosing
6 published rows leave some tracked benchmark slots empty. No comparable first-party API token rate is published.
Source-linked · 6 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%1 benchmarkVerified | Score pending | 1 benchmark | 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 | |||
| KnowledgeRank Not rankedWeight 12%3 benchmarksVerified | 20.7 | 3 benchmarks | Verified | |||
| MultilingualWeight 7%0 benchmarksNot measured | Not measured | 0 benchmarks | Not measured | |||
| Inst. FollowingRank #123 of 124Percentile 1stWeight 5%2 benchmarksVerified | 10.4 | 2 benchmarks | Verified | |||
| MathWeight 5%0 benchmarksNot measured | Not measured | 0 benchmarks | Not measured |
6 of 486 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.
- Agentic1/1 verified
- CodingNot measured
- ReasoningNot measured
- MultimodalNot measured
- Knowledge3/3 verified
- MultilingualNot measured
- Inst. Following2/2 verified
- MathNot measured
Verified sourceProvisionalNot measured
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.
Agentic1 row
| Benchmark | Score | Versus best verified row | Gap | Weight | Evidence |
|---|---|---|---|---|---|
| BFCL v4Berkeley Function Calling Leaderboard v4 | Score21.0% | Versus best verified row Best verified: BTL-3 · 88.5% | Gap67.5 behind | Weight3% ref. weight | Provider exact |
Knowledge3 rows
| Benchmark | Score | Versus best verified row | Gap | Weight | Evidence |
|---|---|---|---|---|---|
| MMLU-ProMassive Multitask Language Understanding Professional | Score20.3% | Versus best verified row Best verified: Qwen3.7 Max · 89.6% | Gap69.4 behind | Weight6% ref. weight | Provider exact |
| GPQAGraduate-Level Google-Proof Q&A | Score25.4% | Versus best verified row Best verified: GPT-6 Astra · 96% | Gap70.6 behind | Weight3% ref. weight | Provider exact |
| GPQA-DGPQA Diamond | Score25.4% | Versus best verified row Best verified: GPT-6 Astra · 96.0% | Gap70.6 behind | WeightDisplay only | Provider exact |
Inst. Following2 rows
| Benchmark | Score | Versus best verified row | Gap | Weight | Evidence |
|---|---|---|---|---|---|
| IFBenchInstruction Following Benchmark | Score38.4% | Versus best verified row Best verified: MAI-Thinking-1 · 85% | Gap46.6 behind | WeightWeighted 70% | Provider exact |
| IFEvalInstruction-Following Eval | Score71.7% | Versus best verified row Best verified: Qwen3.5-27B · 95% | Gap23.3 behind | WeightDisplay only | Provider exact |
Bars run 0–100; the dark tick marks the best source-verified value
All 6 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.
Instruct
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Full release historyLFM2.5-230M release history
Radar confirmed these at the source. Use LFM2.5-230M 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
- 32K
- 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.
We track LFM2.5-230M, but the public leaderboard excludes this profile until enough non-generated benchmark coverage is available. Only published rows appear above.
LFM2.5-230M is a open weight model with a 32K context window. No explicit reasoning mode is documented in this profile.
Official exact-value snapshot from Liquid AI's June 25, 2026 LFM2.5-230M launch post and matching Hugging Face model card. BenchLM maps GPQA Diamond, MMLU-Pro, IFEval, IFBench, BFCLv4, and Tau2 Telecom. Multi-IF, CaseReportBench, BFCLv3, Tau2 Retail, and runtime tables remain out of schema.
6 of 486 tracked benchmark slots currently have displayable evidence. Missing categories stay blank.
Its strongest eligible category is Instruction Following at #123. a well-rounded choice across a range of tasks.
Last updated September 27, 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 LFM2.5-230M perform overall in AI benchmarks?
LFM2.5-230M has 6 source-displayable benchmark rows, but it does not qualify for a public overall rank. The available rows remain visible by category without being converted into a site-wide score. Missing evidence stays blank instead of being estimated from an earlier model.
Is LFM2.5-230M good for knowledge and understanding?
LFM2.5-230M has source-displayable benchmark coverage for knowledge and understanding, 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 LFM2.5-230M good for agentic tool use and computer tasks?
LFM2.5-230M 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 LFM2.5-230M good for instruction following?
LFM2.5-230M ranks #123 out of 124 eligible models for instruction following, with a public category score of 10.4/100. 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 LFM2.5-230M open source?
LFM2.5-230M 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 LFM2.5-230M have full benchmark coverage on BenchLM?
No. LFM2.5-230M currently has 6 source-displayable rows across 486 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 LFM2.5-230M?
LFM2.5-230M has a reported context window of 32K 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.