Text-generation models for scalable, production-ready language tasks
Cohere is a developer-focused text generation platform offering large language models (including Embed and Command-family models) aimed at engineers and product teams building search, summarization, and classification features. Its API-first offering suits businesses needing hosted or private model deployments with pay-as-you-go and enterprise options. Pricing includes a free tier with limited monthly usage, predictable metered paid plans, and enterprise contracts for higher volume and privacy needs.
Cohere is a text-generation platform providing API access to large language models for generation, embedding, and classification tasks. Its primary capability is delivering production-ready LLMs (generation and embedding endpoints) tailored for retrieval-augmented generation, semantic search, and text classification. Cohere differentiates with separate embed and 'Command' model families and a developer-centric API plus fine-tuning and evaluation tooling. It serves engineers, ML teams, and product managers building search, summarization, chat, and intent-classification features. Pricing is accessible with a free tier for exploration and metered paid tiers for production scale.
Cohere is a Toronto-founded AI company (launched 2020) that provides API access to large language models focused on text generation, embeddings, and classification. Positioning itself as an enterprise-grade LLM provider, Cohere emphasizes a developer-first API, model versioning, and privacy controls suitable for integrating language models into applications, search, and analytics. Its core value proposition is decoupling embedding and generation workloads—letting teams use dedicated embed models for retrieval and separate generative models for fluent text—while offering usage-based pricing and enterprise contracts for data residency and SLAs.
Cohere’s key features include the Generate endpoint (generation models like Command and Rerank-capable variants) for instruction-following text output, the Embed endpoint (e.g., embeddings-v2) producing dense vectors for semantic search and clustering, and the Classify endpoint for supervised intent or sentiment classification with few-shot examples. The platform also provides a Rerank API to reorder candidate documents using semantic relevance scores, enabling tighter retrieval-augmented generation. Developers get model parameters, tokenization details, and context-window guidance; Cohere supports prompt templates, batch embedding requests, and evaluation tooling to compare model variants in production workflows.
On pricing, Cohere offers a Free tier with limited monthly usage intended for exploration—commonly including free API credits and capped embed/generate calls (check current account dashboard for exact monthly credits). Paid usage is metered: generation and embedding consumption are billed per unit (tokens for generate, vector requests for embed); Cohere lists documented per-unit prices on its pricing page and provides a Pro or Team plan for higher quotas and priority support. For large enterprises, Cohere offers custom contracts that include dedicated throughput, private networking, and data residency guarantees—those are quoted individually and require sales engagement.
Cohere is used by product teams building semantic search, customer support automation, and analytics. For example, a search engineer uses embeddings to reduce time-to-relevance by improving semantic ranking in a knowledge base, and a customer-success manager uses generate and classify endpoints to auto-draft responses and tag sentiments at scale. The platform competes with other model providers like OpenAI and Anthropic; compared to them, Cohere centers on separate embedding models and enterprise deployment options, making it attractive where control of embedding-generation workflows matters most.
Three capabilities that set Cohere apart from its nearest competitors.
Current tiers and what you get at each price point. Verified against the vendor's pricing page.
| Plan | Price | What you get | Best for |
|---|---|---|---|
| Free | Free | Small monthly API credits for generate and embed, limited rate limits | Developers exploring APIs and prototypes |
| Pay-as-you-go | Metered (per-request billing) | Billed per generation token and per embedding request, no monthly commitment | Startups and apps with variable usage |
| Team / Pro | Starts around $/mo on request | Higher quotas, priority support, shared billing and team management | SMB product teams scaling usage |
| Enterprise | Custom | Dedicated throughput, SLAs, private networking, data residency | Enterprises needing contracts and compliance |
Copy these into Cohere as-is. Each targets a different high-value workflow.
Role: You are a customer-success AI that drafts concise, professional support replies for a B2B SaaS product. Constraints: produce 5 distinct reply templates, each 40–60 words, friendly but concise, include one-sentence apology/acknowledgement when appropriate, and a clear next step. Output format: return a JSON array of objects: {"subject":"...","body":"...","tags":[...]} with 2–3 tags each. Example output item: {"subject":"Issue with login","body":"Thanks for reporting this — we’re investigating your login failure and will respond within 2 hours. Meanwhile, try clearing your cache or resetting your password at /reset. If it persists, reply with error ID 12345.","tags":["login","urgent"]}.
Role: You are an SEO copywriter. Constraints: for each blog title provided (one per line), produce a concise SEO title (50–60 characters) and a meta description (110–160 characters) that includes the primary topic keyword, a benefit, and a call-to-action. Output format: return a JSON array of objects: {"title_input":"...","seo_title":"...","meta_description":"...","keyword":"..."}. Example input line: "How to run successful user interviews" -> example object: {"title_input":"How to run successful user interviews","seo_title":"User Interview Guide: Run Better Interviews","meta_description":"Master user interviews with practical scripts and templates to discover real user needs. Download the checklist.","keyword":"user interviews"}.
Role: You are a search relevance engine that ranks documents by semantic relevance to a user query. Constraints: accept input where the user supplies QUERY: <text> and DOCUMENTS: a JSON array of {"id":"","text":""}; return a JSON array sorted highest-to-lowest with items: {"id":"","score":0.000-1.000,"explanation":"<=20 words"}. Scores must be normalized 0–1, and explanations must be concrete (mention matching concepts). No extra commentary. Example input -> output mapping: QUERY: "refund policy" with docs about billing and returns should show billing doc score 0.92 and explanation "mentions refund timeframe and process".
Role: You are an automated triage assistant for incoming support tickets. Constraints: given a single ticket text, output a single-line JSON object with keys: {"intent":"one of [bug, billing, feature_request, account_help]","priority":"low|medium|high","assignee":"team or role name","escalate":true|false,"confidence":0.00-1.00}. Use conservative priority (only 'high' for revenue-impacting or security issues). Output only the JSON. Example: "Customer can't access paid features after billing" -> {"intent":"billing","priority":"high","assignee":"Billing Team","escalate":true,"confidence":0.94}.
Role: You are a senior legal-assistant AI synthesizing answers from provided retrieved passages. Multi-step constraints: 1) Read QUERY: <text> and pass a JSON array PASSAGES: [{"id":"DOC1","text":"..."},...]. 2) Produce a concise answer (150–300 words) that directly addresses the query, integrates multiple passages, and avoids hallucination. 3) Inline-cite sources using [DOC_ID:char-start-char-end] for every factual claim tied to a passage; include a final 'sources' list with doc ids and one-line summaries. 4) Append 'confidence' (low/medium/high) and 3 suggested follow-up questions. Few-shot example: QUERY: "How long does trademark registration take?" PASSAGES -> example answer with citations. Return only JSON: {"answer":"...","sources":[...],"confidence":"...","follow_ups":[...]}.
Role: You are a dataset engineer producing high-quality training data for an intent classifier. Constraints: given a list of labels provided as LABELS: ["labelA","labelB",...], produce exactly N examples per label (N specified by a variable), with diverse phrasing, lengths 5–30 words, and avoid overlapping intents. Output format: JSONL where each line is {"text":"...","label":"..."}. Include 3 few-shot examples for two labels: {"text":"I need to change my payment method","label":"billing"}, {"text":"App crashes on startup","label":"bug"}, {"text":"Can you add dark mode?","label":"feature_request"}. After examples, generate the requested dataset for all labels. Do not include extra commentary.
Choose Cohere over OpenAI if you need separate, production-grade embedding models and enterprise deployment features with private networking.
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