Deployment Brief

LFM2.5-Embedding-350M (free)

LFM2.5-Embedding-350M (free) is a cost-efficient model with standard context support, suited to semantic retrieval and enterprise search for enterprise teams.

Try LFM2.5-Embedding-350M (free) with your team

Last reviewed: 2026-09-02

Liquid AI

Stable

Context Window

512

Input / 1M

$0.00

Output / 1M

$0.00

What can you do with LFM2.5-Embedding-350M (free)?

Practical ways teams can use LFM2.5-Embedding-350M (free) inside governed AI workflows.

Improve enterprise search with LFM2.5-Embedding-350M (free)

Rank documents, answers, and knowledge-base results so teams find the right information faster with LFM2.5-Embedding-350M (free).

Power semantic retrieval with LFM2.5-Embedding-350M (free)

Match user questions to relevant policies, product docs, tickets, and internal references with LFM2.5-Embedding-350M (free).

Deduplicate knowledge assets with LFM2.5-Embedding-350M (free)

Cluster related content, similar records, and overlapping documents for cleaner operations with LFM2.5-Embedding-350M (free).

Route support requests with LFM2.5-Embedding-350M (free)

Classify incoming questions and connect them with the most relevant internal resources with LFM2.5-Embedding-350M (free).

Rank compliance evidence with LFM2.5-Embedding-350M (free)

Surface the most relevant policies, logs, and documents during audits and reviews with LFM2.5-Embedding-350M (free).

Measure content similarity with LFM2.5-Embedding-350M (free)

Compare records, tickets, snippets, and documents for matching or recommendation workflows with LFM2.5-Embedding-350M (free).

Why this model

LFM2.5-Embedding-350M (free) is available in Remova as a standard context option with $0.00 per 1M tokens input pricing, $0.00 per 1M tokens output pricing, and text input to embeddings output support.

LFM2.5-Embedding-350M (free) offers standard context capacity for enterprise prompts and documents.
Current Remova pricing band is cost-efficient: $0.00 per 1M tokens input and $0.00 per 1M tokens output.
Best-fit workloads include: Semantic retrieval, Enterprise search, Knowledge indexing.
Route requests by policy tier so teams do not overuse capability.

At a glance

Model ID: liquid/lfm-2.5-embedding-350m:free
Context Window: 512 tokens
Modality: Text input to embeddings output
Input Modalities: Text
Output Modalities: Embeddings
Input Price: $0.00 per 1M tokens
Output Price: $0.00 per 1M tokens
Provider: Liquid AI
Listing Date: 2026-08-18

Strengths

LFM2.5-Embedding-350M (free) is suited for semantic retrieval.
Supports standard context for multi-step prompts and larger working sets.
Pricing profile is cost-efficient, enabling predictable workload routing decisions.
Can be paired with policy guardrails for safer deployment at scale.

Tradeoffs

Without workload routing, teams may overuse this model for requests that fit lower-cost tiers.
Standard context limits may require chunking or retrieval strategies for large documents.
Low-cost tiers can still underperform on high-consequence decisions without escalation paths.
Embedding and retrieval systems need benchmark sets to catch ranking drift and stale indexes.

Best for

LFM2.5-Embedding-350M (free) for semantic retrieval, ranking, and enterprise search workflows.
LFM2.5-Embedding-350M (free) for enterprise search across policies, product docs, and support knowledge bases.
LFM2.5-Embedding-350M (free) for indexing internal knowledge assets into searchable vector workflows.
LFM2.5-Embedding-350M (free) for surfacing compliance evidence and related records during audits.

Rollout checklist

Define where LFM2.5-Embedding-350M (free) is default vs. fallback in your routing policy.
Enable role-based access and policy checks before opening access broadly.
Set spend guardrails by team and monitor weekly token consumption.
Define escalation rules to premium models before launch.
Re-run quality and cost benchmarks monthly as newer releases appear.

Related models

Explore adjacent model profiles for routing and benchmarking decisions.

Free Resource

Where Should Your Team Start with AI?

Tell us your industry and team size. We'll tell you which AI use cases will save the most time with the least setup.

You get

A shortlist of AI use cases ranked by impact and effort for your situation.

Tuning notes

frequency_penalty

Tune repetition control for long responses in multi-step workflows.

max_tokens

Set completion limits to avoid unpredictable long-output spend.

min_p

Use this parameter only with tested defaults in production workflows.

presence_penalty

Use carefully when expanding idea diversity in exploration-heavy prompts.

Free Assessment

What Could Go Wrong?

5 questions about how your company uses AI today. We'll show you the risks most companies miss until it's too late.

You get

A risk breakdown with the 3 things you should fix first.

Knowledge Hub

LFM2.5-Embedding-350M (free) FAQs

Choose LFM2.5-Embedding-350M (free) when the workload aligns with semantic retrieval, enterprise search, knowledge indexing and quality targets justify its pricing profile.

It depends on workload mix. Most organizations use routing policies so routine traffic stays on lower-cost tiers.

Validate quality on real internal prompts, token efficiency, latency, and policy compliance behavior.

Deploy This Model With Governance

Use policy controls, role-based access, and budget guardrails before enabling advanced model tiers at scale.

Try LFM2.5-Embedding-350M (free) with your team