DeepSeek, All Models Available: families, capabilities, context windows, and access channels

DeepSeek has evolved into a multi-family lineup that covers general chat, reasoning, coding, and multimodal use cases. Rather than one monolithic system, DeepSeek is organized across distinct model series—each tuned for a different task profile and latency/cost envelope. Understanding these families, their context windows, and where they can be accessed (first-party API, cloud gateways, or local runtimes) helps teams choose the right engine for drafting, code, analysis, and agentic workflows.

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How DeepSeek organizes its model families.

DeepSeek’s catalog clusters into four practical buckets that map to real workloads:

• General language models (V-series) — balanced chat models for drafting, Q&A, analysis, and tool-use.

• Reasoning models (R-series) — enhanced stepwise reasoning for math, logic, planning, and complex multi-turn tasks.

• Coding models (Coder-series) — code completion, refactoring, test generation, multi-file context, and tool calling for repositories.

• Vision & multimodal variants — input understanding from images/PDFs/diagrams with text-first output, useful for charts and UI screenshots.

Each family is typically offered at multiple sizes (e.g., Small / Base, Large / Pro), trading off latency and cost versus accuracy and depth of reasoning.

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Model catalog overview you can actually select in practice.

Below is an operational view of the lineup you’ll encounter on common providers and APIs. Names may include “Chat,” “Instruct,” or explicit sizes; in daily work, think in terms of the family and tier first:

General chat (V-series)

• DeepSeek-V (Core / Large) — default assistant for broad tasks (summaries, edits, data extraction).

• DeepSeek-V-Instruct — instruction-tuned for consistent formatting, stronger adherence to task/format rules.

Reasoning (R-series)

• DeepSeek-R1 (Reasoner / Pro) — slower but more deliberate stepwise reasoning; excels at multi-step problems, decomposition, and tool orchestration.

• DeepSeek-R-Lite — trimmed latency footprint for production agents that still benefit from explicit reasoning paths.

Coding (Coder-series)

• DeepSeek-Coder-V2 (Chat / Base / Large) — repository-aware code generation, multi-file context, refactors, tests, and inline explanations.

• DeepSeek-Coder-Instruct — instruction-first outputs for CI/CD bots and code actions (patches, diffs, commit-style summaries).

Multimodal / Vision

• DeepSeek-V-Vision (Base / Pro) — image + text understanding; screenshots, charts, simple UI states, and figure-aware summaries.

• DeepSeek-Doc / PDF modes — text-first extraction from PDFs and image-heavy docs; suited to “what’s on page X?” style prompts.

In many integrations you’ll also see Mini or Flash style variants built for low-latency chat and high QPS workloads (routing, classification, short replies).

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Context windows, token behavior, and when “more” is actually useful.

DeepSeek families generally follow modern long-context conventions:

• General chat: medium-to-long context suitable for multi-document conversations, policy docs, and knowledge bases.

• Reasoning: equal or larger context, but the benefit comes from structured thinking rather than raw length; plan for higher token usage when enabling explicit tool calls or chain-of-thought-like behaviors (the model still returns succinct answers while using its internal reasoning).

• Coding: repo-scale context in chat flows; strongest when you pin an index (file map, dependencies, build config) and ask for targeted changes rather than “whole project” rewrites.

• Vision: treats images/PDFs as embeddings within the same token budget; quality improves when you add tight instructions about regions, tables, or figure captions.

Practical guidance:

• Use V-series for long policy docs, reports, product briefs.

• Switch to R-series when problems branch (multi-constraint planning, rigorous math, multi-hop retrieval).

• Prefer Coder-series anytime edits touch multiple files or require test scaffolding.

• Bring Vision models when screenshots, dashboards, or charts carry the key facts.

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Coding capabilities: what the Coder-series does better than general chat.

Coding variants are trained to respect syntax, dependency graphs, and diff-style outputs. In practice they:

• Produce minimal diffs (patches) instead of walls of code.

• Track module boundaries and project conventions (linters, formatters).

• Generate tests and migrations as first-class tasks, not afterthoughts.

• Handle multi-file prompts with references (“update service A and its interface in B; adjust tests in C”).

For large repos, seed the conversation with:

• A file map and key modules (names + roles).

• Constraints (language versions, frameworks, CI requirements).

• Acceptance criteria (build passes, specific test names succeed).

The Coder-series will align changes with those constraints and produce workable diffs faster than generic chat models.

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Multimodal and document reading: what to expect on PDFs, images, and tables.

DeepSeek’s vision/multimodal variants are text-first interpreters of visual inputs. They read:

• PDFs (prefer searchable text; for scans, provide page ranges and captions).

• Charts and tables (describe axes/series; ask for extracted values or trends).

• Screenshots/UI states (identify labels, error messages, configuration options).

Best results come from region-specific prompts:

“From page 7 table ‘Revenue by Region’, extract the 2024 totals and rank descending. Return JSON.”

This makes the model focus and reduces token waste on decorative content.

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Access channels: where you can actually run DeepSeek.

Teams typically access DeepSeek via three paths:

• First-party API / console — full catalog, latest checkpoints, and fine-grained parameters (temperature, max tokens, tool/function calling).

• Cloud gateways (aggregators) — quick model pickup alongside other vendors; good for A/B testing (DeepSeek vs Gemini/Claude/ChatGPT) with one SDK.

• Enterprise platforms — private routing, audit logging, and compliance features; common in data-sensitive deployments.

Latency and pricing vary by channel; low-latency Mini/Small tiers and off-peak batches often cut costs for high-volume classification or templated replies.

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Tool calling, structured output, and agent patterns that work.

Across families, DeepSeek supports function/tool calling and strict JSON schemas for reliable downstream use:

• Define clear tool signatures (names, params with types, enums for allowed values).

• Ask for “Return ONLY valid JSON” (no prose) when integrating with pipelines.

• For agents, let R-series handle planning/critique, and route execution calls to task-specific tools (search, DB, code runner, spreadsheet calc).

A robust pattern is a two-stage loop:

1. Plan (R-series): break the task, propose tool calls, verify inputs.

2. Execute (V/Coder): call tools with validated arguments; emit final JSON/report.

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Choosing the right DeepSeek model for the job.

• Knowledge work & drafting: DeepSeek-V (Large / Instruct) for long reports, polished summaries, and transformations.

• Complex reasoning & planning: DeepSeek-R1 / R-Pro for multi-constraint tasks, math/logic, retrieval-heavy answers.

• Software engineering: DeepSeek-Coder-V2 (Large) for multi-file changes, tests, and refactors; Coder-Base for fast inline suggestions.

• Vision & documents: DeepSeek-V-Vision (Pro) for chart/table extraction, UI screenshot analysis, and PDF reading with structured outputs.

• High-throughput ops: Mini/Small tiers for routing, classification, FAQ deflection, and templated decompositions ahead of a larger model.

If you’re building an internal platform, route:

• Short turns to V-Mini,

• Ambiguous turns to V-Large,

• Multi-step plans to R-series,

• Code edits to Coder-series,

• Images/PDFs to Vision,with a fallback to retry or escalate when confidence/validation fails.

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Enterprise considerations: privacy, governance, and reliability.

Production teams should standardize around:

• No-train flags and data retention controls per tenant/environment.

• Red-team prompts and guardrail policies (blocked patterns, PII handling).

• Observability (traces, token/cost meters, latency SLOs, and output validation).

• Version pinning (lock model IDs per environment; manage upgrades through canaries).

• Eval sets for regression testing across families (V vs R vs Coder).

Document a fallback matrix (what to do when a model is unavailable or returns low-confidence results) and a content validation layer (schema checks, citation checks, unit tests for code).

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Roadmap themes you can plan around.

DeepSeek development typically focuses on:

• Bigger long-context stability (less degradation near window limits).

• Cheaper high-throughput “Mini” tiers for agent scaffolding and pre-filters.

• Stronger code-aware reasoning (multi-repo, build system awareness, security scanning).

• R-series improvements in planning and tool-use reliability.

• Vision upgrades for precise table/chart extraction and page-anchored citations.

Plan your integrations to be model-agnostic: keep prompts, tools, and schemas portable so you can adopt new checkpoints with minimal refactoring.

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Practical prompts to get predictable results from each family.

V-series (reports/policy):

“Summarise sections 2–4 of the attached PDF. Return a table (Finding | Evidence | Page) and a 120-word narrative.”

R-series (planning/logic):

“Decompose the task into steps, list uncertainties, select tools with arguments, then propose a plan in JSON. No prose.”

Coder-series (diffs/tests):

“Given files X, Y, Z, add feature A. Return a unified diff and Jest tests. Respect ESLint config from .eslintrc.”

Vision (tables/charts):

“From page 5 chart, extract series ‘Gross Margin (%)’ by quarter. Return JSON array with quarter, value, and a one-sentence trend.”

These templates reduce drift and make outputs easy to validate in pipelines.

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Deployment tips that save time and budget.

• Right-size the model to the step—don’t use an R-series where V-Mini is enough.

• Cache deterministic transforms (classification, templated rewrites).

• Constrain outputs with strict schemas and enums; reject/repair invalid JSON automatically.

• Batch similar queries to amortize overhead; stream results for UX responsiveness.

• Instrument every call (tokens, latency, retries, failure types) and set alerts.

Treat DeepSeek families as composable components: you’ll ship faster, and your unit economics will be healthier than running everything through a single largest model.

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