Governed Profile

Cohere: Command R (08-2024)

Cohere: Command R (08-2024) is a cost-efficient model with standard context support, suited to code retrieval and repository search for enterprise teams.

Try Cohere: Command R (08-2024) with your team

Last reviewed: 2026-06-09

Cohere

Stable

Context Window

128,000

Input / 1M

$0.23

Output / 1M

$0.90

What can you do with Cohere: Command R (08-2024)?

Practical ways teams can use Cohere: Command R (08-2024) inside governed AI workflows.

Search codebases with Cohere: Command R (08-2024)

Embed repositories, snippets, and technical docs so developers can find relevant implementation context with Cohere: Command R (08-2024).

Power coding assistants with Cohere: Command R (08-2024)

Retrieve related files, APIs, examples, and dependency context for governed developer workflows with Cohere: Command R (08-2024).

Index repositories with Cohere: Command R (08-2024)

Create searchable vectors for source files, documentation, issues, and engineering knowledge bases with Cohere: Command R (08-2024).

Deduplicate code knowledge with Cohere: Command R (08-2024)

Cluster similar snippets, docs, tickets, and examples for cleaner engineering support systems with Cohere: Command R (08-2024).

Rank technical evidence with Cohere: Command R (08-2024)

Surface relevant code, logs, docs, and tickets during incident and compliance reviews with Cohere: Command R (08-2024).

Measure code similarity with Cohere: Command R (08-2024)

Compare snippets, repositories, and technical records for recommendations or migration planning with Cohere: Command R (08-2024).

Why this model

Cohere: Command R (08-2024) is available in Remova as a standard context option with $0.23 per 1M tokens input pricing, $0.90 per 1M tokens output pricing, and text->text modality support for enterprise AI operations.

Cohere: Command R (08-2024) offers standard context capacity for enterprise prompts and documents.
Current Remova pricing band is cost-efficient: $0.23 per 1M tokens input and $0.90 per 1M tokens output.
Best-fit workloads include: Code retrieval, Repository search, Coding assistant retrieval.
Keep audit logs enabled for high-impact use cases.

At a glance

Model ID: cohere/command-r-08-2024
Context Window: 128,000 tokens
Modality: text->text
Input Modalities: text
Output Modalities: text
Input Price: $0.23 per 1M tokens
Output Price: $0.90 per 1M tokens
Provider: Cohere
Listing Date: 2024-08-30

Strengths

Cohere: Command R (08-2024) is suited for code 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

Operational drift can appear over time without recurring quality evaluations.
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.
Code retrieval systems need repository access controls, freshness checks, and relevance benchmarks.

Best for

Cohere: Command R (08-2024) for codebase retrieval across repositories, docs, issues, and technical records.
Cohere: Command R (08-2024) for repository search with access controls and relevance benchmarks.
Cohere: Command R (08-2024) for grounding coding assistants in approved repository context.
Cohere: Command R (08-2024) for surfacing relevant code, logs, and tickets during engineering reviews.

Rollout checklist

Define where Cohere: Command R (08-2024) 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.
Start with approved teams, then expand in controlled waves.
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.

presence_penalty

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

response_format

Prefer structured output where responses feed internal systems.

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

Cohere: Command R (08-2024) FAQs

Choose Cohere: Command R (08-2024) when the workload aligns with code retrieval, repository search, coding assistant retrieval 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 Cohere: Command R (08-2024) with your team