DeepSeek R1: Reasoning, Pricing, and Coding Use

What Is DeepSeek R1

The R1 project includes DeepSeek-R1-Zero, DeepSeek-R1, and six distilled models. R1-Zero used large-scale reinforcement learning without supervised fine-tuning as a preliminary step. DeepSeek reported repetition, readability, and language-mixing problems in R1-Zero, then added cold-start data before reinforcement learning for R1.

The full R1 code and weights use the MIT License and permit commercial use, modification, derivative works, and distillation. Distilled Qwen and Llama variants also carry obligations from their underlying models, so do not apply the full-model license summary to every checkpoint.

Reasoning Training and Chain-of-Thought Output

Calling R1 a “chain-of-thought architecture” is inaccurate. Its architecture is based on DeepSeek V3 Base; the differentiator is its reasoning-oriented post-training. The hosted reasoning API historically exposed a separate reasoning-content field before the final answer.

Visible reasoning text is not proof that a conclusion is correct. Check the final answer against tests, source material, and deterministic tools. For sensitive code changes, focus review on the patch and test evidence rather than the apparent fluency of the reasoning trace.

DeepSeek R1 vs OpenAI o1/o3

DeepSeek described R1 as comparable to OpenAI o1 across math, code, and reasoning tasks at release. That is a vendor claim from a specific evaluation set, not a universal equivalence and not a direct comparison with every later o3 configuration.

Historical Cost and Current API Access

At launch, DeepSeek priced deepseek-reasoner at $0.14 per million cached input tokens, $0.55 per million uncached input tokens, and $2.19 per million output tokens. Those are historical R1 API rates.

The claim that R1 costs exactly 10% of o1 is unsupported because the ratio changes with cache use, output length, provider prices, and reasoning-token consumption. More importantly, DeepSeek retired the deepseek-reasoner alias on July 24, 2026. Current requests use the V4 family and its thinking modes, not the original R1 endpoint.

DeepSeek R1 for Coding

R1 is most relevant when a coding task benefits from deliberate reasoning, such as algorithm design, root-cause analysis, migration planning, or verifying a complex invariant. It may be less efficient for simple transformations where a smaller non-reasoning model is sufficient.

Use a three-part pilot:

• Solve one reproducible bug and require the failing and passing test output.
• Review one algorithm for correctness, complexity, and edge cases.
• Plan one migration, then have a separate reviewer challenge every assumption.

R1 and V3.2

R1 is a reasoning-focused release built on DeepSeek V3 Base. DeepSeek V3.2 belongs to the later V3 general and agentic line; it is not simply “R1 version 2.” Compare the exact checkpoints and modes instead of treating the names as a linear upgrade. DeepSeek has since moved its hosted API to V4 models, while the original R1 weights remain available for self-hosting.

Using It in Verdent

Verdent's current built-in catalog lists DeepSeek V4 Pro, not DeepSeek R1. Check the current model catalog and choose a documented model for the workflow. Do not present an R1 guide as evidence of native R1 availability.

Related Guides

• DeepSeek V3.2
• GPT-OSS 120B
• GPT-5

Test a Reasoning-Heavy Task

Choose one task with a verifiable answer, measure the full token and review cost, and keep the original R1 weights separate from current hosted endpoints.

Select Verdent's current DeepSeek model Download Verdent for a reasoning pilot