DeepSeek Write for Us: Guest Posts and Contributor Guidelines

DeepSeek develops language, reasoning, coding, mathematical, and
multimodal artificial intelligence models. Developers may access
supported models through DeepSeek’s services or work with publicly
released model weights and code where the applicable licence permits.

Computer Tech Reviews welcomes practical contributions about DeepSeek
models, APIs, local deployment, inference, evaluation, fine-tuning,
model architecture, coding applications, multilingual use, security,
privacy, and responsible implementation.

We invite articles from AI developers, researchers, machine-learning
engineers, software developers, MLOps professionals, security
specialists, product managers, and people with first-hand experience
testing or deploying DeepSeek models.

This contributor page belongs to our broader

Artificial Intelligence Write for Us

hub, which covers additional AI models, providers, platforms, and
applications.

What Is DeepSeek?

DeepSeek is an artificial intelligence company and model developer. Its
work includes general language models, reasoning models, coding models,
mathematical models, vision-language systems, multimodal research, and
tools for developers.

The term “DeepSeek” can refer to the company, its chat service, API
platform, model families, official code repositories, or individual
model checkpoints. Articles should state exactly which of these they
cover.

A model accessed through DeepSeek’s hosted API may not behave exactly
like a publicly released checkpoint, local quantization, distilled
model, or third-party hosted implementation. Deployment environment,
inference software, precision, prompt formatting, and model revision
can all affect results.

DeepSeek Topics We Welcome

We accept original tutorials, technical explainers, evaluations,
comparisons, case studies, implementation guides, and informed analysis
about:

• DeepSeek model families and architectures
• Building applications with the DeepSeek API
• Using DeepSeek through compatible SDKs
• Running DeepSeek models locally
• Self-hosting and private deployment
• Quantization and resource-efficient inference
• Serving DeepSeek models with inference frameworks
• DeepSeek for software development
• DeepSeek for mathematics and technical reasoning
• Multilingual and Chinese-language evaluation
• DeepSeek multimodal and vision-language models
• Prompt design and structured responses
• Tool use and agentic workflows
• Retrieval-augmented generation
• Fine-tuning and model adaptation
• DeepSeek distilled models
• DeepSeek licences and commercial use
• API cost, latency and token usage
• Security and privacy in DeepSeek applications
• Evaluating DeepSeek against other model families
• Common DeepSeek implementation mistakes

DeepSeek Models and Access Methods

A DeepSeek model may be accessed in several ways, depending on current
availability:

• Through DeepSeek’s chat service
• Through DeepSeek’s hosted API
• Through a supported cloud or inference provider
• By downloading an official model checkpoint
• Through a distilled or quantized model
• Through a third-party application or hosted endpoint

Articles must identify the exact access method. A third-party service
may apply its own prompt templates, moderation, context limits,
quantization, logging, pricing, or infrastructure.

Writers should also distinguish an official DeepSeek checkpoint from a
community conversion, quantization, fine-tune, or derivative model.

Building with the DeepSeek API

Developer articles may cover chat applications, document processing,
coding assistance, data extraction, retrieval, tool use, or other
workflows built through DeepSeek’s API.

A reproducible API tutorial should identify:

• The exact API model identifier
• The testing date and endpoint
• The SDK or HTTP client and version
• System instructions and prompt format
• Input and output structure
• Token and response limits
• Streaming or non-streaming behaviour
• Tools and external integrations
• Error handling, retries and rate limits
• Pricing context and measured usage
• Data handling and privacy requirements

Do not expose a real API key in screenshots, sample code, error logs, or
public repositories. Use environment variables or an appropriate secret
management system.

Running DeepSeek Models Locally

Publicly released DeepSeek models or derived checkpoints may be run on
local or private infrastructure when hardware, licences and supported
inference software permit.

Local deployment can provide greater control over infrastructure and
data flow, but it does not automatically guarantee privacy, security,
low cost, or strong performance.

A local-deployment article should document:

• The official repository and model revision
• Whether the model is original, distilled or quantized
• The model and code licences
• Hardware, memory and storage
• Operating system and driver versions
• Inference framework and configuration
• Precision and quantization format
• Context and batch settings
• Latency and throughput measurements
• Known limitations or quality changes

DeepSeek for Coding

DeepSeek models may assist with generating functions, explaining code,
preparing tests, reviewing changes, debugging errors, and working with
technical documentation.

Generated code still needs human review. It may contain vulnerabilities,
incorrect dependencies, broken APIs, inefficient logic, licensing
concerns, or changes that pass one test while causing failures
elsewhere.

Strong coding evaluations should use realistic repositories and report
whether the generated changes compile, pass tests, satisfy security
checks, preserve existing behaviour, and require human correction.

DeepSeek for Reasoning and Mathematics

DeepSeek has released models and research focused on reasoning,
mathematics, and code. These systems may produce lengthy intermediate
work or spend additional computation before returning an answer.

A plausible explanation is not proof that the final answer is correct.
Evaluation should verify results independently and test whether the model
remains reliable when wording, assumptions, or problem structure
changes.

Contributions concentrating specifically on DeepSeek R1, R1-derived
models, reasoning training, distillation, local R1 deployment, or R1
evaluation should use our

DeepSeek R1 Write for Us

page.

Evaluating DeepSeek

An evaluation should be designed around the intended application rather
than a few impressive outputs or vendor-reported benchmarks.

Depending on the use case, testing may consider:

• Factual accuracy and evidence support
• Reasoning and mathematical correctness
• Coding correctness and test results
• Instruction and format adherence
• Chinese, English and multilingual performance
• Consistency across repeated tests
• Handling of uncertainty and missing information
• Tool-selection and argument accuracy
• Safety and refusal behaviour
• Prompt-injection resistance
• Latency, throughput and resource use
• API or infrastructure cost
• Amount of human correction required

Authors should identify the exact model, checkpoint, revision,
quantization, provider, settings, prompt template, and date of testing.

DeepSeek Licences and Model Variants

DeepSeek repositories may contain code, original model weights,
distilled models, or checkpoints based on another model family. These
components may not all have identical licence conditions.

Before using a model commercially or redistributing it, review:

• The official repository licence
• The model or weight licence
• The licence of any base model
• Conditions attached to distilled models
• Required notices or attribution
• Restrictions imposed by a hosting provider
• Licences of datasets and application dependencies

Avoid describing a model as “fully open source” unless the article
explains which code, weights, data, training information, and licence
terms are actually available.

Privacy and Security

Hosted APIs and locally deployed models create different security and
privacy responsibilities. Teams should examine where data is processed,
what is logged, how long it is retained, who can access it, and which
regional or contractual requirements apply.

Local deployment still requires model-file verification, dependency
review, endpoint security, authentication, permissions, monitoring,
patching, and protection of prompts and retrieved information.

Tool-enabled DeepSeek applications should validate model-generated
arguments and require authorization before executing consequential
actions.

DeepSeek vs. Anthropic

Comparisons between DeepSeek and Anthropic may examine hosted APIs,
model access, local deployment options, reasoning, coding, multilingual
performance, pricing, data handling, safety controls, documentation,
and enterprise requirements.

Articles mainly focused on Anthropic, Claude, the Anthropic API, Claude
Code, Constitutional AI, or Anthropic’s safety research should use our

Anthropic Write for Us

page.

DeepSeek vs. Gemini

DeepSeek and Google’s Gemini ecosystem may be compared across language,
reasoning, coding, multimodal capabilities, API development, cloud
integration, deployment choices, model access, cost, and security.

Contributions primarily about Gemini applications, models, APIs,
developer tools, or Google Cloud integration should use our

Gemini Write for Us

page.

DeepSeek vs. Mistral AI

A comparison with Mistral AI may examine open-weight and hosted models,
architecture, local deployment, multilingual performance, coding,
inference requirements, licensing, latency, and enterprise control.

Articles focused mainly on Mistral models, APIs, self-hosting,
fine-tuning, licences, or deployment should use our

Mistral AI Write for Us

page.

DeepSeek vs. Qwen

DeepSeek and Qwen may be compared across Chinese and multilingual
performance, coding, mathematics, reasoning, multimodal tasks, model
sizes, open-weight availability, licences, local deployment, and
hardware requirements.

Contributions mainly covering the Qwen model family, Qwen APIs,
fine-tuning, local inference, multimodal models, or deployment should
use our

Qwen AI Write for Us

page.

How to Compare DeepSeek with Other Providers

A fair comparison should begin with a defined use case. Do not declare
one model “best” using unrelated benchmarks or different testing
conditions.

A comparison should document:

• Exact model identifiers and revisions
• Hosted, local or third-party deployment
• Quantization and inference framework
• Testing date, task and intended audience
• Prompts, system instructions and tools
• Sampling and context settings
• Evaluation rubric and human reviewers
• Latency, throughput and cost
• Failures, refusals and inconsistent results
• Licensing, privacy and deployment differences

What Makes a Strong DeepSeek Article?

A strong contribution is based on first-hand testing, reproducible
development, technical expertise, independent evaluation, or a
carefully documented implementation.

Your submission should:

• Identify the exact DeepSeek model or checkpoint.
• State the provider, repository, revision and testing date.
• Explain the problem and intended audience.
• Document prompts, tools, data and integrations.
• Describe hardware and inference settings for local tests.
• Explain the evaluation method.
• Include failures and meaningful limitations.
• Address licences, privacy, security and human oversight.
• Disclose any connection to a provider or product.

Content We Are Unlikely to Accept

We generally do not accept copied model descriptions, promotional
company profiles, unsupported “best AI” claims, outdated model
comparisons, or tests based on a few selected prompts.

We may also reject fabricated benchmarks, untested code, vague
“open-source” claims, collections of search terms, or articles that
ignore model licences, hardware requirements, privacy, security, and
human review.

Editorial Guidelines

• Submit original content that has not been published elsewhere.
• Write at least 800 words for a standard contribution.
• Use a focused title, clear headings and short paragraphs.
• Identify exact models, revisions and testing dates.
• Support technical claims with official documentation or research.
• Use first-hand testing for tutorials and comparisons.
• Do not expose API keys, private prompts or confidential data.
• Disclose relevant employer, client or vendor relationships.
• Avoid promotional, exaggerated or keyword-focused writing.
• Test code and instructions before submission.

AI tools may assist with research, coding or drafting, but authors
remain responsible for verifying every claim, source, benchmark, model
identifier, licence, code example and recommendation. Unedited or
unverified AI-generated submissions will not be accepted.

How to Submit a DeepSeek Article

Send your proposal or completed article to

contact@computertechreviews.com
.

Please include:

• Your proposed article title
• A short summary of the article
• The intended audience and use case
• A proposed outline
• The models, repositories or APIs involved
• Details of your testing or implementation experience
• Links to previous writing samples, if available
• Disclosure of any connected provider, company or client

Related AI Model Contributor Pages

Select the page that most accurately matches the primary model,
provider, or platform covered by your proposed article.

Frequently Asked Questions

Can I submit a DeepSeek API tutorial?

Yes. Identify the exact API model, endpoint, SDK, dependencies, date of
testing, expected output, pricing context, and security requirements.
Test the complete tutorial before submission.

Can I submit a local DeepSeek deployment guide?

Yes. Document the checkpoint, revision, licence, quantization, hardware,
drivers, inference framework, configuration, memory use, performance,
and limitations.

Is every DeepSeek model open source?

Do not assume that all DeepSeek services, code, model weights, distilled
checkpoints, or third-party deployments have identical openness or
licence conditions. Review the official repository and every applicable
licence.

Where should I submit a DeepSeek R1 article?

Articles specifically about R1 reasoning, reinforcement learning,
distillation, local deployment, benchmarks, or R1-derived checkpoints
should use the DeepSeek R1 contributor page.