DeepSeek-Reasoner

Overview

Freshness note: Model capabilities, limits, and pricing can change quickly. This profile is a point-in-time snapshot last verified on February 15, 2026.

DeepSeek-Reasoner is DeepSeek’s API model optimized for reasoning-intensive tasks and positioned as a high-value alternative to premium Western frontier models. It is commonly used when teams want strong analytical behavior with lower per-token costs.

In the DeepSeek lineup, Reasoner is the “hard problem” model, while chat-oriented variants are usually better for lighter conversational workloads.

Capabilities

DeepSeek-Reasoner performs well on:

• Multi-step analytical tasks and logic-heavy prompt chains.
• Mathematical and algorithmic reasoning workloads.
• Coding support where explanation quality and careful breakdowns are useful.
• Structured long-form answers with explicit thought progression.
• Cost-sensitive production systems that still need strong reasoning output.

It is frequently selected for research copilots, technical tutoring, and decision-support systems that need reasoning depth without top-tier frontier pricing.

Technical Details

DeepSeek API docs list:

• 128K context window.
• 64K max output tokens.
• Separate pricing for cache hit vs cache miss input paths.
• Distinct model positioning as a reasoning-optimized endpoint.

Operationally, this profile benefits from retrieval and prompt discipline. Even with strong reasoning behavior, grounding quality still depends on context quality and evaluation design. It also benefits from explicit answer-format constraints in production, since reasoning models can otherwise over-generate verbose responses that increase cost without improving outcome quality.

Pricing & Access

Published API pricing includes cache-aware tiers. Typical non-cached pricing (per 1M tokens):

• Input (cache miss): $0.28
• Output: $0.42

Cached input pricing is lower, which can materially improve economics for repeated-context workloads.

Access:

• DeepSeek API platform
• Third-party gateways and cloud wrappers in some regions

For high-throughput deployments, cache strategy is a major optimization lever.

Best Use Cases

Choose DeepSeek-Reasoner for:

• Reasoning-heavy applications with tight budget constraints.
• Math and logic workflows that need transparent step organization.
• Coding and technical QA tasks where cost efficiency is critical.
• Evaluation pipelines where many long-form responses are generated.

It is less ideal if you need the broadest multimodal stack in one endpoint. It is also less ideal for pure real-time chat UX where very low-latency responses matter more than deep step-by-step reasoning quality.

Comparisons

• GPT-5 (OpenAI): GPT-5 offers broader ecosystem integration and high-end consistency; DeepSeek-Reasoner is dramatically cheaper for many reasoning workloads.
• Claude Opus 4.6 (Anthropic): Opus is premium and strong for enterprise-grade instruction-heavy tasks; DeepSeek provides compelling cost-performance for analytic throughput.
• Qwen3-Max (Alibaba): Both are strong Chinese frontier options; Qwen3-Max tends to be broader in general capability, while DeepSeek-Reasoner is especially attractive on pure reasoning economics.