DeepSeek

DeepSeek

MODEL DIRECTORY

MODEL DIRECTORY

DeepSeek

DeepSeek

DeepSeek’s current V4 family provides Pro and Flash API models with thinking and non-thinking modes, a documented one-million-token context window, and compatible API interfaces.

CURRENT MODEL SNAPSHOT

Provider: DeepSeek
Current anchor: DeepSeek-V4-Pro and DeepSeek-V4-Flash
Lifecycle: Current
Weights: Open-weight
Reviewed: 20 July 2026

DeepSeek V4 in context

DeepSeek’s current API generation is V4, not the older R1, V2, or V3 framing still found across many comparison pages. DeepSeek documents V4-Pro and V4-Flash with thinking and non-thinking operation and a one-million-token context window. It also offers OpenAI- and Anthropic-compatible interfaces, which can simplify evaluation but does not make migrations behaviourally identical.

The current family has two principal API choices:

  • DeepSeek-V4-Pro is the stronger tier for difficult reasoning, coding, and long-horizon tasks.

  • DeepSeek-V4-Flash is the faster option for production workloads that need lower latency or cost.

  • Both models support thinking and non-thinking modes, which should be treated as separate evaluation configurations.

  • Legacy deepseek-chat and deepseek-reasoner aliases have a documented discontinuation date of 24 July 2026 UTC.

Migrate off legacy aliases before their discontinuation and test the chosen V4 model and reasoning mode end to end. API compatibility reduces client-code friction, but output schemas, tool calls, token usage, safety behaviour, and latency still require regression testing.

Where DeepSeek is distinctive

DeepSeek is relevant when teams want strong reasoning and coding candidates, long context, and the option to work through compatible API patterns. Its open-weight ecosystem can also support alternative hosting strategies, but a self-managed deployment shifts reliability, security, and model-serving responsibility to the operator.

Strengths to test

  • Reasoning and coding evaluations where thinking mode can be enabled selectively.

  • Long-context workflows that need to test retrieval and instruction persistence across large inputs.

  • Provider-switch or gateway evaluations using familiar OpenAI- or Anthropic-compatible client patterns.

  • Architectures considering both hosted API access and open-weight deployment options.

Trade-offs and failure modes

  • Compatible request formats do not guarantee compatible outputs, tool semantics, or error behaviour.

  • Thinking mode can materially change latency, token consumption, and the shape of intermediate reasoning.

  • One-million-token capacity is not evidence that every fact will be used reliably in a long prompt.

  • Self-hosting creates an inference, patching, observability, abuse-prevention, and incident-response obligation.

Run the same regression suite across V4-Pro and V4-Flash, with thinking both enabled and disabled where supported. Include tool schemas, JSON validation, multilingual prompts, long-context distractors, adversarial instructions, and the real network path. Compare completed workflow cost, not only listed token price.

Deployment and enterprise decision notes

DeepSeek provides a hosted API and compatible interface patterns. Open-weight releases may support additional hosting routes through the broader ecosystem. Confirm the exact model artefact, licence, serving stack, region, data path, and provider terms for the selected architecture.

Best fit

A strong candidate for controlled reasoning, coding, long-context, and price-performance evaluations, especially when compatible API clients or open-weight deployment options matter. It is also useful as a second-provider candidate in a routed model strategy.

Not the best fit

Avoid using legacy aliases, assuming drop-in behavioural compatibility, or selecting a self-hosted path without model-serving expertise. It is not appropriate where data, regional, contractual, or operational requirements have not been validated for the chosen provider or host.

Data and governance

Document the V4 model, reasoning mode, interface, hosting party, region, retention settings, licence, safety layer, and migration fallback. For self-hosting, add artefact provenance, vulnerability management, access logs, rate controls, and a process for model or serving-stack updates.

Official sources

Beam AI support status

Under evaluation. This page is a model-selection reference, not confirmation of a Beam integration, benchmark result, data-residency promise, or production recommendation. Validate the exact provider surface and model version in the intended workflow before release.

Use case 1

Reasoning-intensive operations

Evaluate V4-Pro with thinking mode for multi-step planning, exception analysis, and evidence-backed recommendations. Compare against non-thinking operation and a faster model, because deeper reasoning is valuable only when it improves completed-task quality enough to justify latency.

Use case 2

Coding and repository assistance

Use a bounded code context to explain, modify, and test software changes. Validate tool calls, patch correctness, secret handling, dependency risks, and the difference between hosted and self-managed serving before granting write or execution permissions.

Use case 3

Long-context case analysis

Process a large case file or knowledge bundle while testing distractor resistance, source recall, and instruction persistence. Use retrieval baselines and traceable citations; a one-million-token limit should not replace information architecture or evidence checks.

Use case 4

Compatible-API migration test

Run an existing OpenAI- or Anthropic-style client against DeepSeek’s compatible interface in a controlled environment. Compare schemas, tool calls, streaming, errors, retries, safety behaviour, and observability before treating the route as a production substitute.

Related LLMs

Curated alternatives to compare before selecting a model family.

ChatGPT

ChatGPT is OpenAI’s conversational product; enterprise automations use the underlying GPT models and APIs. This guide separates the product from the model family and explains how to evaluate the current GPT-5.6 lineup.

ChatGPT

Combine sections from a range of categories to easily assemble pages that meet the needs of your growing business.

Perplexity

Perplexity’s Sonar API family combines language models with web search and citations. The core enterprise decision is whether provider-managed retrieval and source handling fit the workflow.

Perplexity

Combine sections from a range of categories to easily assemble pages that meet the needs of your growing business.

Gemini

Google’s Gemini family covers production and preview models for multimodal, reasoning, audio, media, and tool-oriented workloads. Model stage and endpoint stability are central to an enterprise selection.

Gemini

Combine sections from a range of categories to easily assemble pages that meet the needs of your growing business.

Claude

Anthropic’s Claude family spans frontier reasoning, coding, vision, and faster production models. The practical choice is a specific pinned model ID on the Claude API or a supported cloud platform.

Claude

Combine sections from a range of categories to easily assemble pages that meet the needs of your growing business.

Llama

Meta’s Llama 4 family provides open-weight, natively multimodal models. Scout emphasizes very long context and efficient deployment; Maverick targets stronger general multimodal capability.

Llama

Combine sections from a range of categories to easily assemble pages that meet the needs of your growing business.

PaLM

PaLM is Google’s superseded language-model family. Existing implementations should identify the old endpoint, select a current Gemini replacement, and run a full migration regression.

PaLM

Combine sections from a range of categories to easily assemble pages that meet the needs of your growing business.

Start Today

Build AI agents with the right model

See how Beam can orchestrate governed AI workflows across the model family that fits your requirements.

Start Today

Build AI agents with the right model

See how Beam can orchestrate governed AI workflows across the model family that fits your requirements.

Start Today

Build AI agents with the right model

See how Beam can orchestrate governed AI workflows across the model family that fits your requirements.

FAQs

Frequently Asked Questions

Model selection, deployment, governance, and Beam support questions answered.

What is the current DeepSeek model lineup?

DeepSeek currently documents V4-Pro and V4-Flash, each with thinking and non-thinking modes and a one-million-token context window. Model names and lifecycle labels can change quickly, so record the exact model ID or release used in testing and confirm it against the linked provider documentation before production.

How can an enterprise access or deploy DeepSeek?

The family is available through DeepSeek’s hosted API and compatible API interfaces, while open-weight releases can create additional hosting choices. Availability, regional controls, service terms, and feature parity can vary by route. Evaluate the exact provider surface that will carry production traffic, rather than assuming every hosted or self-managed option behaves identically.

What workloads are a strong fit for DeepSeek?

It is a strong candidate for reasoning, coding, long-context, provider-routing, and price-performance evaluations with explicit regression testing. Treat that as a shortlist hypothesis, not a universal ranking. Use representative prompts, tools, documents, languages, and failure cases to compare quality, latency, reliability, and total operating cost.

What should security and governance teams review for DeepSeek?

Review model and mode, hosting party, region, data terms, licence, serving controls, long-context behaviour, and migration from legacy aliases. Document the data path, retention settings, model version, region, subprocessors or hosting stack, human-review points, and incident fallback before the workflow is approved.

Does this page confirm Beam support for DeepSeek?

No. Beam support is marked Under evaluation because no approved integration or production-support evidence is attached to this CMS record. The page can guide discovery and evaluation, but the implementation owner must verify access, controls, tool behaviour, and operational fit before making a customer commitment.