Cohere: Command A
Command A is an open-weights 111B parameter model with a 256k context window focused on delivering great performance across agentic, multilingual, and coding use cases. Compared to other leading proprietary...
Anyone in the Space can @-mention Cohere: Command A with the team's shared context - pooled credits, one chat, one memory.
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Verdict
Best for
- High-volume document summarization
- Cost-sensitive customer support automation
- Batch processing of long transcripts
- Retrieval-augmented generation at scale
- Internal knowledge base Q&A
Strengths
Command A's 256K context window handles full-length reports, legal documents, and multi-hour transcripts in a single pass. Input pricing at $2.50 per Mtok undercuts most frontier models by 40-60%, which compounds savings when processing thousands of requests daily. Cohere's enterprise focus shows in consistent latency and straightforward API design — no complex prompt engineering required for standard extraction and summarization tasks.
Trade-offs
Without public benchmark data, Command A's reasoning capabilities remain unproven against peers like GPT-4o or Claude Sonnet 4.5. Early adopters report weaker performance on multi-step logic, nuanced instruction-following, and creative writing compared to frontier models. Output pricing at $10 per Mtok narrows the cost advantage when generating long responses. Cohere's smaller ecosystem means fewer community resources and third-party integrations than OpenAI or Anthropic alternatives.
Specifications
- Provider
- cohere
- Category
- llm
- Context length
- 256,000 tokens
- Max output
- 8,192 tokens
- Modalities
- text
- License
- proprietary
- Released
- 2025-03-13
Pricing
- Input
- $2.50/Mtok
- Output
- $10.00/Mtok
- Model ID
cohere/command-a
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 | 256k | $2.50/Mtok | $10.00/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
Summarize Legal Contract
Read this contract and provide a structured summary covering: parties involved, key obligations, payment terms, termination clauses, and critical deadlines. Format as bullet points.Open in a Space →
Batch Process Support Tickets
Analyze this support ticket. Categorize the issue (billing, technical, account), identify urgency (low/medium/high), and draft a professional response addressing the customer's concern.Open in a Space →
Extract Meeting Action Items
Review this meeting transcript and list all action items. For each, specify: the task, assigned person (if mentioned), and deadline. Ignore off-topic discussion.Open in a Space →
Generate FAQ from Documentation
Read this product documentation and generate 10 frequently asked questions with clear answers. Focus on features, setup steps, and common troubleshooting scenarios.Open in a Space →
Analyze Customer Feedback Trends
Review these customer feedback entries and identify the top 5 recurring themes. For each theme, provide a brief explanation and estimate how many responses mention it.Open in a Space →
Example outputs
Illustrative - representative of the model's voice and quality, not literal recordings.
Review this product requirements doc and flag any ambiguous acceptance criteria or missing edge cases before we start the sprint.
The model would scan the PRD and return a structured analysis highlighting three areas: (1) the user authentication flow lacks clarity on session timeout behavior, (2) the payment integration section doesn't specify retry logic for failed transactions, and (3) acceptance criteria for the search feature don't address empty-state handling or special character inputs. It would also suggest specific questions to ask stakeholders for each gap, formatted as actionable next steps rather than abstract concerns.
Command A's 256k token context window makes it practical for ingesting entire PRDs or multi-file specifications in one pass. This example shows its strength in structured analysis and actionable feedback. The trade-off: without public benchmarks, teams can't predict how its reasoning compares to alternatives on complex logic tasks.
Summarize the last 40 messages in this Slack thread and extract the three decisions we actually made, ignoring the tangents.
The model would parse the conversation thread and produce a concise summary identifying: (1) the team agreed to ship the beta feature behind a flag by Friday, (2) Sarah will own the database migration script, and (3) the A/B test will run for two weeks before evaluation. It would separate these concrete decisions from the exploratory discussion about future roadmap items, presenting them as a bulleted action list with owner names and deadlines intact.
This showcases Command A's ability to distill signal from noise in long conversational contexts—a common workplace pain point. The massive context window supports entire Slack exports or meeting transcripts. The limitation: at $10/Mtok output, summarizing verbose threads frequently can add up faster than with cheaper alternatives.
Draft a professional but warm email declining this consulting proposal, emphasizing budget constraints and leaving the door open for future collaboration.
The model would generate a 150-word email that opens with appreciation for the detailed proposal, clearly states that current budget allocations don't allow for the engagement, and closes by suggesting a check-in next quarter when planning cycles refresh. The tone would balance professionalism with genuine warmth—avoiding corporate stiffness while maintaining clarity about the decision. It would include a specific compliment about an aspect of the proposal to reinforce the relationship.
Command A handles nuanced tone control well, making it useful for sensitive business communication where both clarity and relationship maintenance matter. This example highlights its practical application in daily knowledge work. The gap: without benchmark data on instruction-following or tone accuracy, teams must validate output quality through their own testing before trusting it for external communications.
Use-case deep-dives
Command A handles 200-page compliance reviews without choking
A 4-person legal ops team at a Series B SaaS company needs to cross-reference vendor contracts against internal security policies before every procurement approval. Command A's 256k context window fits 8-12 full contracts plus the company's 40-page security framework in a single prompt, letting the team ask "which vendors fail our data residency rules" without manual chunking. At $2.50/Mtok input, a typical 180k-token review costs $0.45—cheap enough to run on every deal over $5k. The $10/Mtok output rate stings if you're generating long summaries, so keep responses under 2k tokens (use structured extraction, not prose). If you're reviewing fewer than 20 documents per week, the context window is overkill and you'll save money with a smaller model. For teams running 50+ policy checks monthly, Command A turns a 90-minute manual task into a 4-minute API call.
Command A routes 300 daily tickets when speed matters more than perfection
A 12-person e-commerce support team gets 300 inbound tickets daily across email, chat, and social—each needing a priority tag and a routing decision to billing, shipping, or product specialists. Command A classifies and routes at $2.50/Mtok input, so a 400-token ticket costs $0.001 to process. Over 300 tickets/day that's $0.30, or $9/month for the entire triage layer. The model handles straightforward categorization reliably, but without public benchmarks you're flying blind on accuracy—expect to tune prompts and spot-check outputs for the first two weeks. The 256k context lets you include the last 50 tickets as few-shot examples, which tightens routing consistency. If your ticket volume drops below 100/day, the setup overhead isn't worth it; just use rules-based triage. Above 200/day, Command A pays for itself by keeping your senior agents out of the inbox.
Command A summarizes 40 Slack threads into a board deck on Friday mornings
A 20-person growth team runs weekly all-hands where the VP presents a 6-slide deck summarizing the week's experiments, blockers, and wins. Command A ingests 35-40 Slack threads (roughly 120k tokens of conversation history) and outputs a structured brief with top 3 wins, top 2 blockers, and 4 experiment results. At $2.50 input + $10 output per Mtok, a 120k-input / 1.5k-output run costs $0.30 + $0.015 = $0.32 per week—$1.28/month to automate a task that previously took the VP 90 minutes every Friday. The lack of public benchmarks means you can't predict summarization quality upfront, so budget two weeks to dial in the prompt and verify it's not dropping critical updates. If your team is under 10 people or posts fewer than 20 threads/week, the context window is wasted and a smaller model will do. For teams generating 100+ messages daily, Command A turns Slack chaos into a readable brief without hiring a chief of staff.
Frequently asked
Is Command A good for general text generation tasks?
Command A handles standard text generation competently with a 256k context window that accommodates large documents. At $2.50 input and $10 output per million tokens, it sits in the mid-range pricing tier. Without public benchmark data, you're betting on Cohere's track record rather than proven performance metrics. For mission-critical work, test thoroughly or choose models with published scores.
Is Command A cheaper than GPT-4o or Claude Sonnet?
Command A costs $2.50 input and $10 output per Mtok, making it cheaper than GPT-4o ($2.50/$10) on par for input but identical on output, and more expensive than Claude 3.5 Sonnet ($3/$15 but with better benchmarks). The pricing is competitive but not a standout bargain. You're paying mid-tier rates without the benchmark transparency of competitors.
Can Command A handle 200k token documents effectively?
The 256k context window technically supports documents up to 200k tokens. However, without published needle-in-haystack or long-context retrieval benchmarks, actual performance at that scale is unverified. If your workflow depends on accurate retrieval from massive contexts, validate with your specific use case or choose models with proven long-context scores like Gemini 1.5 Pro.
How does Command A compare to previous Cohere models?
Cohere hasn't published comparative benchmarks between Command A and earlier Command models, making direct performance comparison impossible from public data. The 256k context window represents a significant expansion if you're upgrading from older Cohere models. Pricing appears consistent with Cohere's recent tier structure. Request internal benchmarks from Cohere if you need migration justification.
Should I use Command A for production chatbots?
Command A works for production chat with standard latency expectations for API-based LLMs. The 256k window handles conversation history well. The lack of public benchmarks means you can't compare response quality, safety, or instruction-following against alternatives. Run A/B tests against GPT-4o-mini or Claude Haiku before committing production traffic if quality metrics matter to your users.