Which DeepSeek Model Should You Choose?

Choose DeepSeek by deployment route first, then task. For the current first-party API, start with V4 Flash and move to V4 Pro only when representative tests justify the added cost. For local reasoning, consider an R1 distill that fits. Use Coder V2 or VL2 only when their specialised open checkpoints match the workload.

Quick decision table

Every option needs a task-specific evaluation.

Step 1: hosted API or local model?

Choose the API when you need simple scaling, current hosted models and no model-server maintenance. You pay per token and send prompts to the service. Choose local deployment when data control, offline operation, fixed checkpoints or research reproducibility matter enough to justify hardware and administration.

Third-party hosting sits between them. It can expose open checkpoints without local hardware, but introduces another provider’s pricing and privacy policy. Name that provider in evaluation records.

Step 2: Flash or Pro?

V4 Flash and Pro share the documented one-million-token context, 384,000 maximum output, JSON and tool support. Flash had lower token rates and a higher concurrency limit on 2026-07-30. Pro was positioned by DeepSeek for more demanding tasks.

Start with Flash for extraction, classification, short drafting, simple code help and high-volume workflows. Create an evaluation set of failure-prone cases. Escalate only those cases to Pro if it improves accepted-result rate. This routing approach can control cost without assuming that one model is always best.

Step 3: do you need reasoning specifically?

Current V4 models have thinking controls, so a hosted application does not need an old deepseek-reasoner alias. Use high or max effort selectively and include reasoning output in token budgets.

For local experiments, R1 and distilled variants remain relevant. An 8B distill is not equivalent to full 671B R1. Select by memory, runtime and measured task success. See the R1 guide.

Step 4: is the input visual?

V4 is listed as text-only in current API materials. If the task requires native image understanding in an open DeepSeek model, examine VL2. A hosted app’s file extraction and a third-party vision proxy are different architectures.

For precise OCR, compare VL2 with a conventional OCR baseline. For screenshots and charts, evaluate hallucination and grounding. The VL guide details those checks.

Step 5: is a specialised coding checkpoint justified?

Current V4 supports coding, tool calls and FIM in documented modes. It is the logical starting point for a new first-party API integration. Coder V2 is useful when you need a downloadable code-focused checkpoint, fixed local version or research comparison.

Use repository-level tasks with tests, security traps and dependency constraints. Choose the model that produces safe accepted patches with the least review work, not the one with the most impressive provider benchmark.

Cost comparison

As verified, V4 Flash rates were $0.0028/M cached input, $0.14/M uncached input and $0.28/M output. V4 Pro was $0.003625/M, $0.435/M and $0.87/M. The pricing guide is the central source.

Local cost includes hardware, storage, electricity and operations. Compare a monthly total across expected volume. A workstation that already exists differs from buying one solely for sporadic use.

Context length is not the only capacity measure

A one-million-token context does not guarantee perfect retrieval, and sending the maximum is expensive. Evaluate performance as context grows. Use retrieval, summarisation and stable caching rather than dumping every document into every call.

For local models, runtime defaults may expose less context than the model card. More context consumes memory. Record both the theoretical limit and actual configured value.

Privacy and security decision

Hosted chat and API usage follow DeepSeek’s service policies. Local inference can keep content on your machine, but the surrounding client, logs and network binding still matter. Third-party providers follow their own policies.

Classify data before choosing a model. If content is highly confidential or regulated, use an approved environment and minimise prompts. No model choice creates a legal or security guarantee.

Run a fair evaluation

Fix prompts, tools, temperature or thinking settings, context and success criteria. Use exact model IDs or checkpoint digests. Score correctness, unsupported claims, security, latency, total tokens and human review time. Include abstention and failure cases.

Do not compare a web-search-enabled product with an offline checkpoint on current-events questions without noting the tool difference. Product features often matter more than base-model labels.

Recommendation patterns

For a startup API: Flash first, Pro fallback on hard cases. For a student laptop: a small R1 distill, with modest expectations. For a private coding lab: Coder V2 or R1 distill plus deterministic tests. For scanned documents: VL2 only after OCR comparison. For high-stakes business work: approved hosted or local infrastructure plus human review.

Conclusion

The right DeepSeek model is the cheapest and simplest option that passes your real acceptance tests under the required privacy and operational constraints. Start current, record exact versions and resist transferring claims between API models, full models and small distills.