DeepSeek
DeepSeek is the price disruptor at the frontier: a free, capable chat assistant, an API priced far below the western frontier labs, and open-weight model releases that anyone can download and run. Successive model generations have kept it genuinely competitive on reasoning and coding, not merely cheap.
The open weights are the strategic fact. Organisations that cannot send data to a Chinese-hosted service can still use the models by self-hosting them on their own infrastructure, which splits the privacy question from the capability question.
For cost-sensitive builders and self-hosters it is a serious default; for regulated data on the hosted service, the jurisdiction question is the one to answer first.
- Cost
- Freemium (Free tier + paid plans)
- Ease
- Model
- Runs privately (self-hostable)
- Checked
- August 2026
Prices, plans and model versions change fast: this is a mid-2026 snapshot; check the tool's official site for the latest.
Best for
- A free, genuinely capable chat assistant
- The lowest-cost frontier-grade API for builders
- Open weights you can download and self-host
- Reasoning and coding strength per pound spent
- Privacy-critical use via self-hosted deployment
Less suited to
The hosted service processes data under Chinese jurisdiction, which rules it out for regulated or sensitive material in many organisations; the self-hosted weights are the answer to that, at the cost of running them yourself.
The polish layer is also thinner than the western flagships: fewer integrations, a sparser app ecosystem, and product surfaces that move fast and change names.
Costs & data, in short
Free hosted chat; API pricing among the lowest at frontier-adjacent quality; open weights free to self-host.
The hosted service processes data under Chinese jurisdiction, which rules it out for sensitive work; self-hosting the open weights removes that concern entirely.
Plans
| DeepSeek Chat | Free |
|---|---|
| Pay as you go | Price on applicationusage billed at API rates |
Prices as of August 2026. Prices and plans change regularly. Check with the provider before you buy.
In practice
How DeepSeek is used, area by area.
Jobs
Getting started
DeepSeek is a capable free starting point with one caveat that matters
DeepSeek is a capable free starting point with one caveat that matters. It handles reasoning, writing and coding at a level that costs money elsewhere, and successive model generations have kept it competitive rather than merely cheap, so you are not learning on a toy. The caveat is jurisdiction: the hosted service processes data under Chinese jurisdiction, so keep anything sensitive out while you learn, and treat it as a free classroom for the prompting habit rather than a place for confidential material. It is for beginners where budget is zero, capability matters and nothing sensitive is involved. Those wanting a rich ecosystem of integrations will find the western flagships smoother, and the models are released open-weight, so a self-hosted route exists later, which splits the privacy question from the capability question.
Example tasks
- Ask questions and draft text on a capable free assistant
- Work through reasoning problems step by step
- Get coding help without a subscription
- Summarise and rewrite documents
- Compare its answers against your usual assistant
Limits
Beginners with workplace data restrictions should check policy before using the hosted app, and those wanting a rich ecosystem of integrations will find the western flagships smoother. Start here for capability per pound, not for hand-holding.
Compares
| vs | Pick DeepSeek when | Pick the other when |
|---|---|---|
| ChatGPTFull comparison → | DeepSeek gives beginners frontier-adjacent capability for free, holding up on reasoning, writing and coding at a level that costs money elsewhere | you want the smoother ecosystem and no data-residency caveat while you learn |
| ClaudeFull comparison → | DeepSeek gives a beginner frontier-adjacent capability for nothing, holding up on reasoning, writing and coding at a level that costs money elsewhere, so nobody is learning on a toy | the material is not public and low-stakes, because DeepSeek's own caveat is jurisdiction and workplace policy should be checked before the hosted app touches anything sensitive |
Software development
DeepSeek changes what a developer weighs when choosing a model
DeepSeek changes what a developer weighs when choosing a model. It sets cost and control against polish: rather than paying western-frontier API rates or accepting a hosted-only model, a developer runs the open weights on their own infrastructure for privacy-critical work, meters high-volume workloads at a fraction of the incumbents' price, and gets coding performance that holds up against far costlier options. It is for developers who want strong models cheap at volume, or fully under their control. Jurisdiction is the fork in the decision: the hosted API processes data under Chinese jurisdiction and may fail client or employer policy, while self-hosting answers that at the cost of an infrastructure project. Tooling integrations trail the incumbents either way, so licence terms are verified per model release before the model enters a product.
Example tasks
- Generate and review code on a low-cost API
- Run reasoning-heavy tasks where model cost dominates
- Prototype products against frontier-grade completions cheaply
- Self-host the weights for policy-bound workloads
- Benchmark it against costlier models before scaling
Limits
The hosted API's jurisdiction may fail client or employer policy, and tooling integrations trail the incumbents; self-hosting restores control but becomes an infrastructure project. Verify licence terms per model release before it enters a product.
Compares
| vs | Pick DeepSeek when | Pick the other when |
|---|---|---|
| ClaudeFull comparison → | DeepSeek delivers frontier-adjacent coding on your own terms, open weights on your infrastructure and an API that undercuts far costlier rivals at volume | you want the frontier ceiling itself and the mature tooling built around it |
| ChatGPTFull comparison → | DeepSeek sets cost and control against polish, letting a developer run the open weights on their own infrastructure for privacy-critical work rather than paying western-frontier API rates or accepting a hosted-only model | the task is explanation, implementation and planning across several parts of a system |
| CursorFull comparison → | DeepSeek changes what a developer weighs when choosing a model, metering high-volume work against cost and restoring control through open weights on their own infrastructure | the editor is where you live and you want the strongest AI woven through it |
| GitHub CopilotFull comparison → | DeepSeek is the cost-and-control choice, run on your own infrastructure for privacy-critical work or metered cheaply at volume, though its tooling integrations trail the incumbents | development runs through GitHub and you want AI in the editor, the issues and the pull requests |
| GLM (Z.ai)Full comparison → | DeepSeek runs the reasoning-heavy tasks where model cost dominates, and can be benchmarked against costlier models before anything scales | Claude Code should simply be pointed at it for routine implementation work |
| TabnineFull comparison → | DeepSeek lets a product be prototyped against frontier-grade completions cheaply, so the cost of finding out stays small | zero code retention is what the deployment itself has to guarantee |
| Devin DesktopFull comparison → | DeepSeek gets coding performance that holds up against far costlier options, which is the whole reason to weigh it against a polished product at all | the inner loop should stay free of metering and the editor should carry the multi-file work |
| Sourcegraph CodyFull comparison → | DeepSeek makes jurisdiction the fork in the decision and answers it by self-hosting the weights, at the cost of an infrastructure project | AI assistance has to be standardised under enterprise controls |
| Claude CodeFull comparison → | DeepSeek generates and reviews code on a low-cost API, so what is being optimised is the meter rather than the workflow | the backlog holds well-defined tasks and your judgement is worth more than your typing |
| Devin CloudFull comparison → | DeepSeek is a model release to evaluate rather than a service to engage, so its licence terms are checked per release before it goes anywhere near a product | delegation with review fits the workflow better than another pair of hands |
| Kimi CodeFull comparison → | DeepSeek's own client is a developer-preview harness rather than a settled product, so what drives it day to day is usually something else | you want a capable coding agent without changing the tool you already drive |
| QwenFull comparison → | the same weights are also reachable on an API cheap enough that no infrastructure has to exist before anybody finds out whether the model is good enough | your team wants a capable agentic coding model it controls end to end |
Tasks
Coding & software development
DeepSeek delivers serious coding capability at the lowest cost in its class
DeepSeek delivers serious coding capability at the lowest cost in its class. The chat handles real programming work free, the API undercuts every frontier rival for high-volume use, and open weights allow fully private deployment, so regulated teams route around the hosted service and self-host instead. It suits teams for whom coding-AI spend matters, or who want strong open weights they control. Editor integrations centre on the incumbents, and the first-party agent harness is a developer preview that warns of compatibility-breaking changes, so the draw is still cost and weights rather than turnkey developer experience.
Example tasks
- Power coding assistants and pipelines at low token cost
- Run code generation on self-hosted infrastructure
- Handle long-context codebase tasks affordably
- Prototype agentic coding flows without burning budget
- Evaluate open-weight coding quality against closed rivals
Limits
Editor integrations centre on the incumbents, and the vendor's own agent harness is a developer preview that warns of compatibility-breaking changes. The draw is cost and weights, not turnkey developer experience.
Compares
| vs | Pick DeepSeek when | Pick the other when |
|---|---|---|
| GitHub CopilotFull comparison → | DeepSeek delivers serious coding capability at the lowest cost in its class, with a free chat, a volume-undercutting API and open weights for fully private deployment | you want the turnkey editor-and-GitHub-native experience rather than a first-party harness still in developer preview |
| ChatGPTFull comparison → | DeepSeek delivers serious coding capability at the lowest cost in its class, with the chat handling real programming work free, the API undercutting every frontier rival for high-volume use and open weights allowing fully private deployment | turnkey breadth across the workflow matters more than cost and control |
| ClaudeFull comparison → | DeepSeek is the cost-and-weights answer, handling real programming work free in chat, undercutting frontier rivals on the API at volume and shipping open weights a regulated team can self-host | the task needs reasoning across several files and an existing codebase understood before anything is changed |
| CursorFull comparison → | DeepSeek is chosen for cost and control rather than developer experience, handling real programming work free in chat and offering open weights for fully private deployment | you live in an editor all day and want the strongest AI woven into it, since the editor is the commitment |
| GLM (Z.ai)Full comparison → | DeepSeek handles long-context codebase tasks affordably and lets agentic coding flows be prototyped without burning budget | a flat Coding Plan suits the work better than metering it |
| TabnineFull comparison → | DeepSeek asks to be evaluated on open-weight coding quality against the closed rivals, which is a benchmarking decision rather than a deployment one | suggestions have to be grounded in private repositories |
| Devin DesktopFull comparison → | DeepSeek powers coding assistants and pipelines at low token cost, sitting underneath the tools rather than being one of them | a team needs to standardise on predictable AI economics |
| Claude CodeFull comparison → | the weights are yours to run, so a team that cannot let its source leave the building keeps the model, the code and the whole exercise inside it | the backlog holds whole tasks worth handing over and the agent should do the reading, the editing and the test runs itself |
Private, local & self-hosted
DeepSeek matters here for its open weights
DeepSeek matters here for its open weights. Frontier-grade models released openly mean you can run serious capability on your own infrastructure, sidestepping the data-residency concerns of its hosted service entirely, which is precisely how privacy-sensitive teams should consume it. It suits wanting top-tier open weights on your own hardware rather than the hosted app. Frontier-scale weights need serious hardware, so laptops run only the distilled variants and production self-hosting is real infrastructure work, and if self-hosting is the goal but the scale is personal, the smaller open-model ecosystems fit better. The weights are the reason it belongs in this category; the hardware bill is the cost of using them.
Example tasks
- Self-host open weights for data that cannot leave your infrastructure
- Run distilled variants on modest hardware
- Fine-tune open checkpoints for domain tasks
- Serve an internal assistant with no external API calls
- Keep model choice independent of any vendor's hosting
Limits
Frontier-scale weights need serious hardware: laptops run only the distilled variants, and production self-hosting is real infrastructure work. If self-hosting is the goal but the scale is personal, the smaller open-model ecosystems fit better.
Compares
| vs | Pick DeepSeek when | Pick the other when |
|---|---|---|
| Ollama | DeepSeek brings frontier-grade open weights you can run on your own infrastructure, sidestepping the data-residency question of any hosted service | the scale is personal and you want the one-command runtime for the smaller models a laptop can hold |
| GLM (Z.ai)Full comparison → | DeepSeek runs distilled variants on modest hardware and lets open checkpoints be fine-tuned for domain tasks | your rules about where source code may travel exclude hosted APIs and you want its coding capability anyway |
| TabnineFull comparison → | DeepSeek keeps the model choice independent of any vendor's hosting and serves an internal assistant with no external API calls at all | the requirement is to deploy AI coding fully air-gapped inside your own walls |
| QwenFull comparison → | DeepSeek is here for the weights, and the hardware bill is simply the cost of using them | self-hosting is a requirement rather than a preference and you want the open-weight line most likely to stay current |
Where to start
Not sure what to adopt first?
Five quick questions about your job, task and constraints. We'll suggest your top three tools, plus the one to try first.
Alternatives
Same category, different strengths.
Common questions
Is DeepSeek free?
There's a free tier to start; paid plans add capacity and features.
Where does DeepSeek fit best?
DeepSeek fits best in Getting started and Software development; see its practice notes for how.
Before sharing confidential or personal data, check this tool's data-governance and training policies. They differ between providers and can change.
Last checked: August 2026