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Data

Data as of October 5, 2026 · How the score is built

EstablishedOpen WeightNon-Reasoning

Released Apr 8, 2026128K context

LFM2.5-VL-450M

Decision readingLFM2.5-VL-450M is tracked, but not publicly ranked yet. The profile exposes 7 sourced benchmark rows and leaves unsupported fields blank until a published record exists.

Released Apr 8, 2026 — see all recent releases

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 51Not eligible for a public rank

Price

Self-hosted; infrastructure cost varies

input median $0.95No comparable first-party hosted token rate

Speed

Not measured

field median 90 tok/sTime to first token not measured

Context

128Ktokens

field median 256,000Reported for this model; direct source link not stored

Strongest published evidence

Published rows are visible, but no category has enough eligible evidence for a comparative rank.

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 changes

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
AgenticWeight 22%1 benchmarkVerified
Score pending
CodingWeight 20%0 benchmarksNot measured
Not measured
ReasoningWeight 17%0 benchmarksNot measured
Not measured
MultimodalWeight 12%3 benchmarksVerified
Score pending
KnowledgeRank Not rankedWeight 12%2 benchmarksVerified
20.8
MultilingualWeight 7%0 benchmarksNot measured
Not measured
Inst. FollowingWeight 5%1 benchmarkVerified
Score pending
MathWeight 5%0 benchmarksNot measured
Not measured

7 of 649 tracked benchmark slots have displayable evidence · bars run 0–100

Coverage details

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. Agentic1/1 verified
  2. CodingNot measured
  3. ReasoningNot measured
  4. Multimodal3/3 verified
  5. Knowledge2/2 verified
  6. MultilingualNot measured
  7. Inst. Following1/1 verified
  8. 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
Agentic benchmark values, best verified comparison, weight, and source status
BFCL v4Berkeley Function Calling Leaderboard v4Score21.1%Versus best verified row

Best verified: BTL-3 · 88.5%

Gap67.4 behindWeight3% ref. weight
Multimodal3 rows
Multimodal benchmark values, best verified comparison, weight, and source status
MMMUMassive Multi-discipline Multimodal UnderstandingScore32.7%Versus best verified row

Best verified: Qwen3.6-27B · 82.9%

Gap50.2 behindWeightDisplay only
RealWorldQAScore58.4%Versus best verified row

Best verified: Qwen3.8-Flash-Next · 88.5%

Gap30.1 behindWeightDisplay only
CountBenchScore73.3%Versus best verified row

Best verified: Qwen3.6-27B · 97.8%

Gap24.5 behindWeightDisplay only
Knowledge2 rows
Knowledge benchmark values, best verified comparison, weight, and source status
MMLU-ProMassive Multitask Language Understanding ProfessionalScore19.3%Versus best verified row

Best verified: Qwen3.7 Max · 89.6%

Gap70.3 behindWeight6% ref. weight
GPQAGraduate-Level Google-Proof Q&AScore25.7%Versus best verified row

Best verified: GPT-6 Astra · 96%

Gap70.3 behindWeight3% ref. weight
Inst. Following1 row
Inst. Following benchmark values, best verified comparison, weight, and source status
IFEvalInstruction-Following EvalScore61.2%Versus best verified row

Best verified: Qwen3.5-27B · 95%

Gap33.8 behindWeightDisplay only

Bars run 0–100; the dark tick marks the best source-verified value

All 7 rows

Lineage

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.

  1. Apr 8, 2026 · you are here

    LFM2.5-VL-450M

    Not publicly ranked · Price not listed

Vl

Radar

LFM2.5-VL-450M release history

Full release history

Radar confirmed these at the source. Use LFM2.5-VL-450M 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
Established
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-VL-450M, but the public leaderboard excludes this profile until enough non-generated benchmark coverage is available. Only published rows appear above.

LFM2.5-VL-450M is a open weight model with a 128K context window. No explicit reasoning mode is documented in this profile.

Official exact-value snapshot from Liquid AI's April 8, 2026 LFM2.5-VL-450M launch post and matching Hugging Face model card. BenchLM maps only the directly comparable GPQA, MMLU-Pro, IFEval, BFCLv4, MMMU, RealWorldQA, and CountBench results. MMStar, MMBench, POPE, MMVet, BLINK, InfoVQA, OCRBench, MM-IFEval, MMMB, RefCOCO-M, and Multi-IF remain outside BenchLM's current exact benchmark schema.

7 of 649 tracked benchmark slots currently have displayable evidence. Missing categories stay blank.

Last updated October 5, 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 parameters

Questions

How does LFM2.5-VL-450M perform overall in AI benchmarks?

LFM2.5-VL-450M has 7 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-VL-450M good for knowledge and understanding?

LFM2.5-VL-450M 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-VL-450M good for agentic tool use and computer tasks?

LFM2.5-VL-450M 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-VL-450M good for multimodal and grounded tasks?

LFM2.5-VL-450M has source-displayable benchmark coverage for multimodal and grounded 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-VL-450M good for instruction following?

LFM2.5-VL-450M has source-displayable benchmark coverage for instruction following, 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-VL-450M open source?

LFM2.5-VL-450M 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-VL-450M have full benchmark coverage on BenchLM?

No. LFM2.5-VL-450M currently has 7 source-displayable rows across 649 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-VL-450M?

LFM2.5-VL-450M 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.

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