DeepSeek V3.2: Features, Pricing, and API Use
DeepSeek V3.2 is an AI language model released in December 2025 with both thinking and non-thinking modes. Its open weights, MIT license, sparse-attention design, and tool-use support make it relevant to teams testing reasoning-heavy coding workflows. It is no longer the model behind DeepSeek's hosted API aliases, so model identity matters when comparing old examples with a current deployment.
Use this guide to separate durable V3.2 characteristics from historical pricing and access details. A useful evaluation brief names the checkpoint, host, and billing method once, then keeps them fixed across tasks.
DeepSeek V3.2 vs V3: What's New
V3.2 extended the V3 line with DeepSeek Sparse Attention and tighter integration between reasoning and tool use. It can operate without visible reasoning for direct responses or use a thinking mode for harder tasks. The model card also describes tool-use support while thinking, which is useful when a coding task requires repeated inspection, execution, and correction.
| Evaluation point | V3.2 test design |
|---|---|
| Reasoning mode | Test both thinking and non-thinking behavior on the same task |
| Tool loop | Run a reasoning task that requires several tool calls |
| Context | Use the host's implemented limit for the test |
| Checkpoint | Keep one V3.2 build and quantization across every run |
Pricing Context: Historical V3.2 API Rates
The frequently quoted $0.07 per million input tokens was a historical cache-hit rate, not the general input price. It should not be used as a standalone estimate for an ordinary V3.2 request. DeepSeek's hosted API later moved to newer models, and the old deepseek-chat and deepseek-reasoner aliases no longer identify V3.2.
Build a cost test from actual uncached input, cache hits, generated output, and the host's serving fee. Self-hosting also adds compute, storage, observability, and operations costs that do not appear in token tables.
DeepSeek V3.2 vs GPT-5 vs Claude
The useful distinction is control, not a universal ranking. DeepSeek V3.2 provides MIT-licensed weights that can be deployed or adapted within the license terms. GPT-5 and Claude are provider-hosted proprietary model families, which shifts more serving responsibility to the provider.
Run the same repository task across candidates. Good test prompts include:
- “Trace this intermittent test failure, explain the root cause, and propose the smallest safe patch.”
- “Plan a database migration, identify rollback risks, and wait for approval before editing.”
- “Review this pull request for correctness, security boundaries, and missing tests.”
Score completion, tool reliability, review effort, latency, and total cost under the same acceptance criteria.
MIT License Advantages
The MIT license permits broad use, modification, and distribution while retaining the license notice. That flexibility can help teams that need local deployment, a controlled serving layer, or a model-specific adaptation. It does not remove the need for security review, infrastructure capacity, data governance, or compliance checks.
Treat the model license and the service terms as separate layers. A host may add pricing, usage, retention, or availability rules even when the underlying weights use MIT.
API Setup and Current Availability
First, decide whether you need the original V3.2 checkpoint or simply a current DeepSeek-hosted model. Then choose the endpoint and run a small tool-use test before connecting a production repository.
DeepSeek's hosted API has moved beyond V3.2, so old setup snippets may silently target a different model or fail. A V3.2 deployment needs the checkpoint's matching context, quantization, and chat template.
Checking DeepSeek V3.2 Access in Verdent
Verdent's built-in catalog lists DeepSeek V4 Pro rather than V3.2. For an external host that explicitly offers the older checkpoint, BYOA configuration explains the supported provider path.
Benchmark a Hosted V3.2 Checkpoint
Run one bounded repository task in thinking and non-thinking modes while keeping the host and serving configuration unchanged. Compare tool recovery and review effort.
Frequently Asked Questions
Is DeepSeek V3.2 the same as DeepSeek V3?
No. V3.2 is a later checkpoint in the V3 family with sparse attention, thinking and non-thinking modes, and updated tool-use behavior.
Can I still call DeepSeek V3.2 through the current DeepSeek API aliases?
No. The hosted API aliases moved to later models, so a V3.2 evaluation needs a deployment that explicitly identifies the V3.2 checkpoint.
Is DeepSeek V3.2 available under the MIT license?
Yes. Its published weights use the MIT license, subject to retaining the license notice and meeting any separate service terms for the host you choose.
How is DeepSeek V3.2 different from DeepSeek R1?
V3.2 is a general model with selectable thinking behavior and tool use. R1 belongs to a reasoning-focused line, so compare exact checkpoints rather than treating the names as interchangeable.