Cohere: Command R (08-2024)
command-r-08-2024 is an update of the [Command R](/models/cohere/command-r) with improved performance for multilingual retrieval-augmented generation (RAG) and tool use. More broadly, it is better at math, code and reasoning and...
Anyone in the Space can @-mention Cohere: Command R (08-2024) with the team's shared context - pooled credits, one chat, one memory.
Starter is free forever - 1 Space, 100 credits/month, 1 MCP. No card.
Verdict
Best for
- Retrieval-augmented generation workflows
- Multi-document summarization with citations
- Structured data extraction from long texts
- Cost-sensitive production deployments
- Business intelligence report generation
Strengths
Command R excels at grounding responses in provided context and generating inline citations, making it reliable for RAG applications where attribution matters. The 128K window handles lengthy documents without chunking overhead. Pricing sits 5-10x below GPT-4o and Claude Sonnet, enabling high-volume use cases like customer support automation or batch document processing. Structured output formatting is consistent, reducing post-processing needs in production pipelines.
Trade-offs
Creative writing and open-ended reasoning trail GPT-4 class models noticeably — expect more generic phrasing and less nuanced argumentation. Complex multi-hop reasoning tasks that require synthesizing information across disparate sources show higher error rates than Claude or GPT-4o. Lacks vision capabilities entirely, limiting use in multimodal workflows. Fine-tuning options are more restricted compared to open-weight alternatives like Llama 3.
Specifications
- Provider
- cohere
- Category
- llm
- Context length
- 128,000 tokens
- Max output
- 4,000 tokens
- Modalities
- text
- License
- proprietary
- Released
- 2024-08-30
Pricing
- Input
- $0.15/Mtok
- Output
- $0.60/Mtok
- Model ID
cohere/command-r-08-2024
Per-token prices show what the model costs upstream. On Switchy your team draws from one shared org credit pool - one plan, one balance for everyone.
Team cost calculator
5 seats · 80 msgs/day
Switchy meters this against your org's shared credit pool - one plan, one balance for everyone.
Providers
| Provider | Context | Input | Output | P50 latency | Throughput | 30d uptime |
|---|---|---|---|---|---|---|
| cohere | 128k | $0.15/Mtok | $0.60/Mtok | — | — | — |
Performance
Benchmarks
Works well with
Top MCPs
Compatibility data comes from first-party telemetry; once we have enough co-usage signal, top MCPs for this model will appear here.
How Switchy teams use it
Starter prompts
Cited Document Summary
Summarize the following document in 200 words, including inline citations [1], [2] for each claim. Focus on financial projections and risk factors. [paste document text]Open in a Space →
Structured Data Extraction
Extract company name, founding date, headquarters location, CEO name, and employee count from this press release. Return as JSON with null for missing fields. [paste text]Open in a Space →
Multi-Source Comparison
Compare the pricing models described in these three vendor proposals. Create a table with feature, Vendor A price, Vendor B price, Vendor C price. Cite which document each price comes from. [paste proposals]Open in a Space →
Customer Query Response
Answer this customer question using only information from our FAQ below. If the answer isn't in the FAQ, say so explicitly. Question: [paste question] FAQ: [paste FAQ content]Open in a Space →
Batch Report Generation
Generate a 150-word executive summary for this quarterly sales data. Include total revenue, top-performing region, and year-over-year growth percentage. Format as markdown. [paste data]Open in a Space →
Example outputs
Illustrative - representative of the model's voice and quality, not literal recordings.
Summarise this 40-page legal contract into a 3-paragraph executive brief covering key obligations, termination clauses, and liability caps.
This Services Agreement establishes a 24-month engagement between Provider and Client for cloud infrastructure management. Provider commits to 99.9% uptime SLA with monthly credits for breaches, while Client agrees to 30-day payment terms and quarterly usage reviews. Either party may terminate with 90 days' written notice after the initial 12-month period. Immediate termination is permitted for material breach (uncured after 30 days), insolvency, or regulatory prohibition. Upon termination, Client retains data access for 60 days before deletion. Provider's liability is capped at 12 months of fees paid, excluding gross negligence or willful misconduct. Client indemnifies Provider against third-party claims arising from Client's data or usage. Both parties maintain confidentiality obligations surviving termination by 3 years.
Command R's 128K context window handles long documents without truncation, making it suitable for contract analysis where full text matters. The model produces structured summaries that preserve legal precision. However, at $0.60/Mtok output, processing multiple lengthy documents daily becomes expensive compared to smaller-context alternatives for simpler summarisation tasks.
I have customer feedback in English, Spanish, and French. Extract sentiment, categorise by product feature, and flag urgent issues requiring immediate response.
**Sentiment Analysis:** - Positive: 127 comments (42%) - Neutral: 98 comments (33%) - Negative: 75 comments (25%) **Feature Categories:** - Checkout Process: 89 mentions (34% negative) — friction with payment gateway timeouts - Mobile App: 76 mentions (58% positive) — users praise new navigation - Customer Support: 68 mentions (mixed) — response times improved but knowledge gaps remain - Shipping: 67 mentions (19% negative) — delays in EU region **Urgent Flags (3):** 1. Payment failures affecting 12+ users in last 48hrs (Spanish feedback cluster) 2. Data sync bug causing cart losses (5 detailed reports, French) 3. Accessibility issue with screen readers (2 reports, English, regulatory risk)
Multilingual analysis without translation overhead showcases Command R's native language handling. The model structures unstructured feedback into actionable categories. With 128K context, it can process a full month's feedback in one pass. The $0.15 input pricing makes this economical for batch processing, though real-time streaming analysis would accumulate costs quickly.
Generate 5 variations of this email subject line for A/B testing: 'New features now available in your dashboard'. Target SaaS product managers, optimise for open rates.
**Variation A (Curiosity-driven):** "3 dashboard updates you didn't know you needed" **Variation B (Urgency + specificity):** "Your team's new workflow shortcuts (live today)" **Variation C (Benefit-forward):** "Cut reporting time by 40% with these dashboard tools" **Variation D (Question format):** "Ready to automate your weekly status reports?" **Variation E (Social proof angle):** "Why 200+ product teams switched to these dashboard features" **Testing recommendation:** Run A vs. C first (curiosity vs. concrete benefit), then test the winner against B. Variation D works best for engaged segments; E requires proof points in email body to avoid credibility gap.
Command R generates marketing copy with strategic variety rather than minor word swaps. Each variation targets a different psychological trigger, useful for actual testing. The model includes tactical advice about sequencing tests. For high-volume content generation, the output pricing becomes a consideration — five variations cost 3x more tokens than a single draft, so batch requests make economic sense.
Use-case deep-dives
Command R handles 128k-token support ticket context at $0.15/Mtok input
A 12-person SaaS support team routes 200+ tickets daily, each requiring context from prior conversations, knowledge base articles, and product docs. Command R's 128k context window lets you feed entire ticket histories plus relevant documentation in a single prompt without chunking or retrieval overhead. At $0.15/Mtok input, processing a 40k-token context costs under a penny—sustainable at volume. The $0.60/Mtok output rate matters less here since triage responses are short (routing decisions, priority tags, suggested replies). Without public benchmarks we can't verify accuracy against GPT-4 or Claude, so start with a 2-week pilot on non-critical tickets and measure routing precision before full rollout. If your ticket context regularly exceeds 100k tokens and you need sub-cent per-query economics, Command R is the call.
When Command R's input pricing beats real-time moderation models
A 4-person community platform reviews 5,000 user-generated posts overnight for policy violations. Command R processes these in batch: feed the post plus community guidelines (typically 8k tokens total) and get a violation flag plus reasoning. At $0.15/Mtok input, 5,000 posts cost $6 in input tokens; output is minimal (binary decision plus 50-word explanation). Compare that to real-time moderation APIs charging per-request minimums or specialized models at $2-3/Mtok. The 128k window also lets you include 20-30 example violations in-context for few-shot accuracy without fine-tuning. The trade-off: without published benchmarks on content moderation tasks, you'll need to validate precision/recall against your labeled dataset before trusting it in production. If you're processing thousands of items daily and can tolerate 10-minute latency, Command R's pricing structure wins.
Command R consolidates 100-page reports into executive briefs at scale
A 3-person policy research shop summarizes 40 government reports monthly, each 80-120 pages. Command R ingests the full PDF text (60-90k tokens) in one prompt and returns a 1,200-word executive summary. At $0.15/Mtok input and $0.60/Mtok output, a 75k-input/1.5k-output job costs $0.012 input plus $0.0009 output—under 2 cents per report. The 128k window eliminates the chunking-and-stitching workflow that plagues smaller-context models. The risk: without MMLU or summarization benchmarks, you can't pre-validate factual accuracy or citation fidelity. Run the first 10 reports through human review to catch hallucinations or omissions. If your reports are dense, technical, and exceed 50k tokens, and you need monthly throughput above 30 documents, Command R's context-to-cost ratio is unmatched.
Frequently asked
Is Command R (08-2024) good for general text generation?
Yes, Command R handles general text generation well with its 128K token context window. It's designed for business use cases like summarization, Q&A, and content generation. The model balances capability with cost at $0.15/$0.60 per Mtok, making it practical for production workloads where you need reliable output without GPT-4 pricing.
Is Command R cheaper than GPT-4o or Claude Sonnet?
Command R is significantly cheaper than both. At $0.15 input and $0.60 output per Mtok, you're paying roughly 1/10th of GPT-4o's cost and about half of Claude Sonnet 3.5's rates. If your use case doesn't require frontier-model reasoning and you're processing high volumes, Command R delivers better economics.
Can Command R handle 128K tokens effectively in practice?
Command R supports the full 128K context window, which equals about 96,000 words or 300 pages. This works well for document analysis, long-form summarization, and multi-turn conversations. However, without published benchmarks on retrieval accuracy across the full window, test your specific use case before committing to context-heavy workflows.
How does Command R compare to the previous Command model?
The August 2024 refresh improved instruction-following and reduced hallucination rates compared to earlier Command R versions. Cohere hasn't published detailed benchmark comparisons, but user reports indicate better consistency on business tasks like email drafting and report generation. The context window and pricing remain unchanged from the previous iteration.
Should I use Command R for customer-facing chatbots?
Command R works for customer support chatbots where you need cost efficiency and decent response quality. It handles FAQ answering, basic troubleshooting, and information retrieval reliably. For complex reasoning or nuanced conversations, you'll want Claude or GPT-4o instead. The latency is acceptable for chat, typically under 2 seconds for 500-token responses.