DeepSeek V3.2 is the latest major release in DeepSeek’s V3 series: an “inference-first” large language model family optimized for agentic tool use, long-context reasoning, and cost-efficient deployment.
What is DeepSeek v3.2?
DeepSeek v3.2 is the latest production release in the DeepSeek V3 family: a large, reasoning-first open-weight language model family designed for long-context understanding, robust agent/tool use, advanced reasoning, coding and math. The release bundles multiple variants (production V3.2 and a high-performance V3.2-Speciale). The project emphasizes cost-efficient long-context inference through a new sparse attention mechanism called DeepSeek Sparse Attention (DSA) and agents / “thinking” workflows (“Thinking in Tool-Use”).
Main features (high level)
- DeepSeek Sparse Attention (DSA): a sparse-attention mechanism intended to dramatically reduce compute in long-context scenarios while preserving long-range reasoning. (Core research claim; used in
V3.2-Exp.) - Agentic thinking + tool-use integration: V3.2 emphasizes embedding “thinking” into tool-use: the model can operate in reasoning-thinking modes and in non-thinking (normal) modes when calling tools, improving decision-making in multi-step tasks and tool orchestration.
- Large-scale agent data synthesis pipeline: DeepSeek reports a training corpus and agent-synthesis pipeline spanning thousands of environments and tens of thousands of complex instructions to improve robustness for interactive tasks.
- DeepSeek Sparse Attention (DSA): DSA is a fine-grained sparse attention method introduced in the V3.2 line (first in V3.2-Exp) that reduces attention complexity (from naive O(L²) to an O(L·k) style with k ≪ L), selecting a smaller set of key/value tokens per query token. The result is substantially lower memory/compute for very long contexts (128K), making long-context inference materially cheaper.
- Mixture-of-Experts (MoE) backbone and Multi-head Latent Attention (MLA): The V3 family uses MoE to increase capacity efficiently (large nominal parameter counts with limited per-token activation) along with MLA methods to maintain quality and control compute.
Technical specifications (concise table)
- Nominal parameter range: ~671B – 685B (variant dependent).
- Context window (documented reference): 128,000 tokens (128K) in vLLM/reference configs.
- Attention: DeepSeek Sparse Attention (DSA) + MLA; reduced attention complexity for long contexts.
- Numeric & training precision: BF16 / F32 and compressed quantized formats (F8_E4M3 etc.) available for distribution.
- Architectural family: MoE (mixture-of-experts) backbone with per-token activation economy.
- Input / output: standard tokenized text input (chat/message formats supported); supports tool-calls (tool-use API primitives) and both interactive chat-style calls and programmatic completions via API.
- Offered variants:
v3.2,v3.2-Exp(experimental, DSA debut),v3.2-Speciale(reasoning-first, API-only short term).
Benchmark performance
High-compute V3.2-Speciale reaches parity or exceeds contemporary high-end models on several reasoning/math/coding benchmarks, and achieves top-level marks on selected elite math problem sets. The preprint highlights parity with models such as GPT-5 / Kimi K2 on selected reasoning benchmarks, specific improvements versus earlier DeepSeek R1/V3 baselines:
- AIME: improved from 70.0 to 87.5 (Δ +17.5).
- GPQA: 71.5 → 81.0 (Δ +9.5).
- LCB_v6: 63.5 → 73.3 (Δ +9.8).
- Aider: 57.0 → 71.6 (Δ +14.6).
Comparison with other models (high level)
- Vs GPT-5 / Gemini 3 Pro (public claims): DeepSeek authors and several press outlets claim parity or superiority on selected reasoning and coding tasks for the Speciale variant, while emphasizing cost efficiency and open licensing as differentiators.
- Vs open models (Olmo, Nemotron, Moonshot, etc.): DeepSeek highlights agentic training and DSA as key differentiators for long-context efficiency.
Representative use cases
- Agentic systems / orchestration: multi-tool agents (APIs, web scrapers, code-execution connectors) that benefit from model-level “thinking” + explicit tool-call primitives.
- Long-document reasoning / analysis: legal documents, large research corpora, meeting transcripts — long-context variants (128k tokens) let you keep very large contexts in a single call.
- Complex math & coding assistance:
V3.2-Specialeis promoted for advanced math reasoning and extensive code debugging tasks per vendor benchmarks. - Cost-sensitive production deployments: DSA + pricing changes aim to lower inference costs for high-context workloads.
How to get started to use DeepSeek v3.2 API
DeepSeek v3.2 API Pricing in CometAPI，20% off the official price:
| Input Tokens | $0.22 |
|---|---|
| Output Tokens | $0.35 |
Required Steps
- Log in to cometapi.com. If you are not our user yet, please register first
- Get the access credential API key of the interface. Click “Add Token” at the API token in the personal center, get the token key: sk-xxxxx and submit.
- Get the url of this site: https://api.cometapi.com/
Use Method
- Select the “
deepseek-v3.2 - Replace <YOUR_API_KEY> with your actual CometAPI key from your account.
- Select Chat format: Insert your question or request into the content field—this is what the model will respond to.
- .Process the API response to get the generated answer.
