DeepSeek V3.2 is available on Novita AI as the Serverless LLM model ID deepseek/deepseek-v3.2, with OpenAI-compatible, completions, and Anthropic-compatible endpoints; as checked on June 23, 2026, the model page lists a 163,840-token context window, 65,536 max output tokens, $0.269 per 1M input tokens, $0.40 per 1M output tokens, and $0.1345 per 1M cache-read tokens. That makes this page the canonical Novita AI reference for DeepSeek V3.2 API availability, pricing, current limits, and the developer workloads where V3.2 is a practical fit.
DeepSeek V3.2 API Facts On Novita AI
The Novita AI model page lists DeepSeek V3.2 as a Chat model in the DeepSeek series with LLM and Serverless tags. For developers, the most important detail is that the Novita AI model ID is deepseek/deepseek-v3.2, not the upstream Hugging Face repository name. Use that exact ID in API requests to Novita AI.
| Item | Current value on Novita AI | Why it matters |
|---|---|---|
| Model display name | DeepSeek V3.2 | Use this in product copy, docs, and user-facing model selectors. |
| API model ID | deepseek/deepseek-v3.2 | Use this exact value in Novita AI API calls. |
| Availability | Serverless LLM | You can call the hosted model without provisioning a dedicated GPU instance. |
| Endpoints | chat/completions, completions, anthropic | Existing OpenAI-style and Anthropic-style integrations can be adapted with minimal routing changes. |
| Input price | $0.269 per 1M tokens | Current listed price for prompt/input tokens. |
| Output price | $0.40 per 1M tokens | Current listed price for generated tokens. |
| Cache-read price | $0.1345 per 1M tokens | Useful for repeated prompts and long shared context patterns. |
| Context window | 163,840 tokens | Supports long documents, larger code contexts, and multi-step agent traces. |
| Max output tokens | 65,536 tokens | Allows long generated answers, reports, and tool-plan outputs when your app needs them. |
| Features listed | Function calling, structured outputs, reasoning, serverless | Good fit for agent and structured application workflows. |
| Modalities | Text input, text output | Treat this as a text LLM, not a multimodal model. |
| Quantization listed | FP8 | Relevant for understanding serving characteristics, but not a field you need to pass in normal API calls. |
| Date checked | June 23, 2026 | Pricing and availability can change, so recheck the model page before publishing pricing-sensitive pages or quotes. |
For the latest live values, open the DeepSeek V3.2 model page on Novita AI. For request parameters and response shape, use the Novita AI chat completion API reference.
What DeepSeek V3.2 Changes For Developers
DeepSeek describes V3.2 as an efficient reasoning and agentic AI model. The upstream release highlights three core technical directions: DeepSeek Sparse Attention, a scaled reinforcement learning framework, and a large-scale agentic task synthesis pipeline. For application developers, those details translate into a model that is positioned for long-context reasoning, tool-use workflows, coding agents, and structured problem solving.
DeepSeek Sparse Attention, or DSA, is the headline architecture change. Instead of presenting it as a guaranteed latency or cost win for every workload, it is safer to describe the developer impact this way: DSA is designed to reduce attention overhead in long-context scenarios while preserving model quality. That matters when your prompt includes a long document, a repository-scale code sample, a retrieval bundle, or a multi-turn agent trace.
The model also has a large Mixture-of-Experts architecture. The upstream configuration shows 256 routed experts and 8 experts selected per token, with a maximum position embedding value of 163,840. Those details line up with the long-context value shown on the Novita AI model page. They also explain why hosted serverless access is attractive: most teams want to call this class of model through an API instead of operating the serving stack themselves.
DeepSeek V3.2 should be treated as a text model for application design. Novita AI lists text input and text output. If your application needs image understanding, speech, video, or embedding output, choose a separate model from the Novita AI model library rather than stretching this page beyond what V3.2 is listed to support.
How To Call DeepSeek V3.2 With The Novita AI API
DeepSeek V3.2 can be called through Novita AI’s OpenAI-compatible endpoint. The exact API reference endpoint is https://api.novita.ai/openai/v1/chat/completions; with the official OpenAI Python SDK, set the SDK base URL to https://api.novita.ai/openai and pass deepseek/deepseek-v3.2 as the model.
from openai import OpenAI
client = OpenAI(
api_key="<NOVITA_API_KEY>",
base_url="https://api.novita.ai/openai",
)
response = client.chat.completions.create(
model="deepseek/deepseek-v3.2",
messages=[
{
"role": "system",
"content": "You are a precise engineering assistant.",
},
{
"role": "user",
"content": "Summarize the main API migration risks in this release note.",
},
],
max_tokens=2048,
temperature=0.3,
)
print(response.choices[0].message.content)Keep the initial integration simple. Start with a small max_tokens value while testing, then raise it only for workloads that genuinely need long outputs. For deterministic reasoning, extraction, and code review tasks, use lower temperatures. For brainstorming and less constrained writing tasks, a higher value can be reasonable, but the exact setting should be validated against your own acceptance tests.
If you are migrating from an existing OpenAI-compatible integration, the two fields to check first are the base URL and the model ID. If you are migrating from an Anthropic-style client, the Novita AI model page also lists an anthropic endpoint option, so keep the application-level message format and tool-calling behavior under test instead of assuming every SDK abstraction maps identically.
When DeepSeek V3.2 Is The Right Fit
DeepSeek V3.2 is a good candidate when the workload needs reasoning depth, long context, and API-accessible deployment. It is especially relevant for teams building coding assistants, document analysis systems, agent workflows, and structured-output services where the prompt may include more than a short chat exchange.
Choose DeepSeek V3.2 when:
- You need the hosted
deepseek/deepseek-v3.2model on Novita AI rather than local deployment. - Your prompt may include long documents, large code snippets, or multi-step agent context.
- You want function calling or structured outputs in a text-only LLM workflow.
- Your application benefits from lower listed output pricing than many heavyweight reasoning models, while still requiring a frontier-scale model family.
- You want to compare a DeepSeek reasoning model against other Novita AI models through a unified API surface.
Consider another model when:
- Your use case is a short, high-volume classification task where a smaller model may be easier to tune and cheaper to operate.
- You need multimodal input or output.
- You require a published latency guarantee for a specific region or SLA. This article does not claim fastest latency, benchmark leadership, or a particular uptime guarantee for DeepSeek V3.2.
- You need the research-focused behavior of DeepSeek V3.2-Speciale. DeepSeek’s upstream notes describe Speciale as a high-compute variant for deep reasoning and state that it does not support tool calling.
The practical recommendation is to test DeepSeek V3.2 with your real prompts, not only generic benchmark tasks. Long-context models can look strong on paper but still need application-specific evaluation for retrieval quality, function-call consistency, refusal behavior, and output length control.
DeepSeek V3.2 Vs V3.1, V4, And V4 Pro Content
This page is intentionally focused on DeepSeek V3.2 availability on Novita AI: model ID, pricing, limits, endpoints, DSA, and developer fit. It is not a general DeepSeek history article and not a broader comparison page.
Use these related pages when your intent is different:
- For older DeepSeek API integration patterns, read How to Use Function Calling of DeepSeek V3.
- For DeepSeek R1 comparison and reasoning background, read DeepSeek R1 vs Llama 3.3 70B.
- For DeepSeek V3.2 production-cost framing and Speciale discussion, read How to Access DeepSeek V3.2 for Cutting Inference Costs in Production.
If you are comparing V3.2 with future DeepSeek V4 or V4 Pro coverage, keep the model IDs and release pages separate. A newer model name does not automatically replace deepseek/deepseek-v3.2 in production code. Check the exact Novita AI model page before changing model IDs, pricing assumptions, context limits, or feature flags.
Frequently Asked Questions
What is the DeepSeek V3.2 model ID on Novita AI?
Use deepseek/deepseek-v3.2 for Novita AI API calls. That differs from the upstream Hugging Face repository name, which is formatted as deepseek-ai/DeepSeek-V3.2.
Is DeepSeek V3.2 available as a Serverless LLM on Novita AI?
Yes. The Novita AI model page lists DeepSeek V3.2 with LLM and Serverless tags as of June 23, 2026.
What is the current DeepSeek V3.2 pricing on Novita AI?
As checked on June 23, 2026, the model page lists $0.269 per 1M input tokens, $0.40 per 1M output tokens, and $0.1345 per 1M cache-read tokens. Recheck the model page before making pricing-sensitive decisions.
What context window does DeepSeek V3.2 support on Novita AI?
Novita AI lists a 163,840-token context window and 65,536 max output tokens for DeepSeek V3.2.
Does DeepSeek V3.2 support OpenAI-compatible API calls?
Yes. The Novita AI model page lists chat/completions and completions endpoint support, and the API reference documents the OpenAI-compatible chat completion path.
What is DeepSeek Sparse Attention?
DeepSeek Sparse Attention, or DSA, is the efficient attention mechanism highlighted in the upstream DeepSeek V3.2 release. It is designed for long-context efficiency while preserving model quality.
Is DeepSeek V3.2-Speciale the same model as DeepSeek V3.2 on Novita AI?
No. DeepSeek’s upstream notes describe DeepSeek V3.2-Speciale as a high-compute variant for deep reasoning and note that it does not support tool calling. This Novita AI canonical page is about deepseek/deepseek-v3.2.
Recommended Articles
- How to Access DeepSeek V3.2 for Cutting Inference Costs in Production
- How to Use Function Calling of DeepSeek V3
- DeepSeek R1 vs Llama 3.3 70B
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