Deployment Brief

Intfloat: E5-Base-v2

Intfloat: E5-Base-v2 is a cost-efficient model with standard context support, suited to semantic retrieval and enterprise search for enterprise teams.

Try Intfloat: E5-Base-v2 with your team

Last reviewed: 2026-06-09

Intfloat

Stable

Context Window

8,192

Input / 1M

$0.01

Output / 1M

$0.00

What can you do with Intfloat: E5-Base-v2?

Practical ways teams can use Intfloat: E5-Base-v2 inside governed AI workflows.

Improve enterprise search with Intfloat: E5-Base-v2

Rank documents, answers, and knowledge-base results so teams find the right information faster with Intfloat: E5-Base-v2.

Power semantic retrieval with Intfloat: E5-Base-v2

Match user questions to relevant policies, product docs, tickets, and internal references with Intfloat: E5-Base-v2.

Deduplicate knowledge assets with Intfloat: E5-Base-v2

Cluster related content, similar records, and overlapping documents for cleaner operations with Intfloat: E5-Base-v2.

Route support requests with Intfloat: E5-Base-v2

Classify incoming questions and connect them with the most relevant internal resources with Intfloat: E5-Base-v2.

Rank compliance evidence with Intfloat: E5-Base-v2

Surface the most relevant policies, logs, and documents during audits and reviews with Intfloat: E5-Base-v2.

Measure content similarity with Intfloat: E5-Base-v2

Compare records, tickets, snippets, and documents for matching or recommendation workflows with Intfloat: E5-Base-v2.

Why this model

Intfloat: E5-Base-v2 is available in Remova as a standard context option with $0.01 per 1M tokens input pricing, $0.00 per 1M tokens output pricing, and text->embeddings modality support for enterprise AI operations.

Intfloat: E5-Base-v2 offers standard context capacity for enterprise prompts and documents.
Current Remova pricing band is cost-efficient: $0.01 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: intfloat/e5-base-v2
Context Window: 8,192 tokens
Modality: text->embeddings
Input Modalities: text
Output Modalities: embeddings
Input Price: $0.01 per 1M tokens
Output Price: $0.00 per 1M tokens
Provider: Intfloat
Listing Date: 2025-11-18

Strengths

Intfloat: E5-Base-v2 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

Policy exceptions should be monitored and reviewed on a fixed cadence.
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

Intfloat: E5-Base-v2 for semantic retrieval, ranking, and enterprise search workflows.
Intfloat: E5-Base-v2 for enterprise search across policies, product docs, and support knowledge bases.
Intfloat: E5-Base-v2 for indexing internal knowledge assets into searchable vector workflows.
Intfloat: E5-Base-v2 for surfacing compliance evidence and related records during audits.

Rollout checklist

Define where Intfloat: E5-Base-v2 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.

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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

Intfloat: E5-Base-v2 FAQs

Choose Intfloat: E5-Base-v2 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 Intfloat: E5-Base-v2 with your team