LLM Reference
DeepSeek Platform

DeepSeek Platform

Researched todayAI LabTier 1

DeepSeek

CodingRAGAgentsLong contextClassificationJSON / Tool useAIHighlightChina

DeepSeek Platform exposes 8 tracked models (8 with output token pricing in seed data). Task coverage across this catalog includes coding, rag, and agents; open any model detail page for benchmarks, batch tiers, and migration prompts.

Portfolio context: 6 decision-task tags, 8 catalog rows, latest research stamp 2026-05-22.

Use this portfolio page for

  • Teams comparing token and batch economics on this surface
  • Operators routing coding, rag, and agents workloads through this API

Do not stop here for

  • Final benchmark picks without opening the relevant model detail page

Catalog rows

8

Models linked to this provider in seed data

Priced output routes

8

Rows with token_out in seed data

Cheapest output

$0.280

DeepSeek V2 on this route

Batch-ready SKUs

0

No batch pricing tracked

Latest catalog ship

2026-04-24

28d since dated release field

Freshness

2026-05-22

Researched today

fresh

Catalog release signal

Latest ISO-dated model.release in this catalog is 2026-04-24 (28d ago).

Where this host wins

  • Coding: 7 tracked models with SWE-bench / HumanEval-style scores.
  • RAG: 6 tracked models with ruler / needle retrieval benchmarks.
  • Agentic: 5 tracked models with BFCL, tau-bench, and SWE-bench tool-use coverage.
  • Long-context: 7 tracked models with context-token or InfiniteBench-class signal.

Getting started

Official entry points from seed metadata — confirm quotas and regions in vendor docs.

Compliance notes (verbatim seed excerpts)

Not yet verified from seed copy — no SOC/ISO/HIPAA-class sentences detected to quote verbatim.

Platform Overview

DeepSeek's AI platform offers cutting-edge models like DeepSeek-V2 and DeepSeek-Coder-V2, designed for complex tasks such as coding, mathematics, and advanced reasoning. Built on a Mixture-of-Experts (MoE) architecture, the platform activates only a subset of parameters during inference, enhancing computational efficiency and reducing training costs and inference time. With 236 billion parameters and a context length of up to 128,000 tokens, DeepSeek models deliver exceptional performance, ranking among the top in various AI benchmarks, including AlignBench and MT-Bench. The platform's API is user-friendly and cost-effective, offering competitive pricing for input and output tokens. DeepSeek supports 338 programming languages, providing robust coding assistance and generating high-quality solutions for complex mathematical problems. The open-source nature of the models promotes transparency and community collaboration, allowing users to seamlessly integrate DeepSeek's powerful AI tools into their existing workflows. This combination of high performance, affordability, and versatility makes the platform an attractive choice for developers and businesses looking to leverage advanced AI technologies.

Available Models(8)

View all →

All models available as Serverless

ModelInput (per 1M)Output (per 1M)
DeepSeek V4 Flash$0.14$0.28
DeepSeek V4 Pro$0.435$0.87
DeepSeek V3.2 Exp$0.28$0.42
DeepSeek V3.2 Speciale$0.28$0.42
DeepSeek R1$0.55$2.19
DeepSeek V3$0.14$0.28
DeepSeek Coder V2$0.14$0.28
DeepSeek V2$0.14$0.28

Platform Details

TypeAI Lab
TierTier 1
Models8

Organization

DeepSeek
Founded2023
Herzliya, Tel Aviv District, Israel

DeepSeek is an advanced AI platform specializing in powerful models for coding, mathematics, and reasoning tasks. Its two primary models, DeepSeek-V2 and DeepSeek-Coder-V2, offer high performance and extensive capabilities. DeepSeek-V2 boasts 236 billion parameters and supports up to 128K tokens, outperforming models like GPT-4 and LLaMA3-70B in specific tasks. DeepSeek-Coder-V2 utilizes a Mixture-of-Experts design, supports over 338 programming languages, and achieves 90.2% accuracy in code generation. The platform provides a user-friendly API compatible with OpenAI, offers competitive pricing, and is open-source under the MIT License. DeepSeek's unique training methodology combines code and natural language datasets, enhancing its performance in coding tasks. The platform's focus on high context length, cost efficiency, and specialization in coding and mathematics sets it apart in the AI landscape.