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DeepSeek V3.2

  • Text Generation

DeepSeek V3.2 is a large open-source Mixture-of-Experts language model from DeepSeek that emphasizes high reasoning performance and efficient long‑context inference. It is notable for its DeepSeek Sparse Attention and multi-latent attention mechanisms, which significantly cut compute and memory costs for long sequences.

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Providers Comparison

Speed

Output tokens per second · Higher is better

246
97
92
91
90
80
60
27
LLM API
SiliconFlow (FP8)
GMI
CoreWeave
Novita
DeepSeek
Parasail (FP8)
DeepInfra (FP4)

End-to-End Response Time

Seconds · Lower is better

65.1
8.1
19.8
20.4
20.9
23.1
24.6
30.6
LLM API
SiliconFlow (FP8)
GMI
CoreWeave
Novita
DeepSeek
Parasail (FP8)
DeepInfra (FP4)

Price

USD per 1M tokens (blended) · Lower is better

65.1
8.1
19.8
20.4
20.9
23.1
24.6
30.6
LLM API
SiliconFlow (FP8)
GMI
CoreWeave
Novita
DeepSeek
Parasail (FP8)
DeepInfra (FP4)

About the model

What is DeepSeek V3.2?

DeepSeek V3.2 is a cutting-edge open-source Mixture-of-Experts large language model by DeepSeek, with around 685B total parameters and ~37B active parameters per token that targets GPT-5-class reasoning and agent performance. It is primarily used for advanced reasoning and agentic tool-use workflows, such as long-horizon automation, complex planning, and multi-step decision-making in production environments. It is also widely used for long-context coding assistance, code generation and debugging, as well as large-document and RAG-style analysis thanks to context windows on the order of 128K–160K tokens. As its name suggests, DeepSeek V3.2 belongs to the DeepSeek V3 family and succeeds earlier DeepSeek V3.x experimental variants as a frontier open-weight model.

Model capabilities

5 Core Capabilities

  • Conversational Chat

    Engages in multi-turn dialogue, following instructions, answering questions, and maintaining context for general-purpose conversational assistance.

  • Document Reasoning

    Analyzes and summarizes long-form text, extracting key points, performing reasoning, and answering questions based on provided content.

  • Multilingual Translation

    Translates between multiple languages while attempting to preserve meaning, style, and domain-specific terminology across diverse text inputs.

  • Visual Understanding

    Interprets images to identify objects, scenes, relationships, and described content for downstream reasoning or question answering tasks.

  • Text OCR Extraction

    Reads and extracts textual content from images, including documents, screenshots, or signs, enabling downstream search, analysis, and transformation.

Use cases

6 Most Valuable Use Cases

  • Advanced Code Generation
  • Long-Context Document QA
  • Enterprise Workflow Automation
  • Agentic Tool Use
  • Structured JSON Outputs
  • Case Monitoring & Analysis

Transparent pricing

Cost Comparison

Up to ~70% cheaper and faster than comparable DeepSeek V3.2 deployments

Provider Region Latency Throughput Uptime Input ($/1M) Output ($/1M) Context
LLM API BEST Global 80ms 120 tps 99.99% $0.15 $0.30 256K
DeepSeek Global ~180ms ~45 tps ~99.9% ~$0.30 ~$0.60 ~128K
OpenRouter Global ~220ms ~35 tps ~99.9% ~$0.35 ~$0.70 ~128K
Hyperbolic API US East ~210ms ~40 tps ~99.9% ~$0.32 ~$0.65 ~128K
Together AI US West ~200ms ~50 tps ~99.9% ~$0.28 ~$0.58 ~128K

Performance benchmarks

Technical Specifications

Metric DeepSeek V3.2 GPT-4.1 Claude 3.5 Sonnet
Avg Latency ~180ms ~220ms ~250ms
Context Window 128K 128K 200K
Input Price ($/1M) $0.40 $5.00 $3.00
Output Price ($/1M) $0.80 $15.00 $15.00
Max Output Tokens 4K 4K 4K
Throughput 80 tps 50 tps 40 tps
Uptime 99.5% 99.9% 99.9%

30-day usage via LLM API

62B
Prompt tokens processed (last 30 days)
11.5B
Completion tokens generated (last 30 days)
3.1M
API requests served (last 30 days)
99.8%
Avg uptime over the last 30 days
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Architecture & Integration

Why Build on LLM.API?

One unified API. Every major model. Built-in reliability, cost control, and observability.

  • Unified AI Routing

    Dynamically route each request to the optimal model across providers based on latency, cost, and quality—without changing your app code or client integration.

    One endpoint, any model

  • Cost-Aware Orchestration

    Automatically balance cost and performance with configurable policies that pick cheaper models for routine calls and premium models only when they truly matter.

    Spend less per token

  • Intelligent Fallbacks

    Configure per-route failover to backup models and providers so outages, rate limits, or timeouts don’t take your AI features offline.

    Resilient by default

  • Deep Observability

    Get per-request traces, latency, cost, and model metrics across all providers in one place, with logs ready for debugging and optimization.

    See every token

  • Task-Level Abstractions

    Define high-level tasks like chat, retrieval, or tools once and let LLM.API handle model-specific prompts, parameters, and orchestration behind a stable contract.

    Code to tasks, not models

  • High-Throughput Batch

    Submit massive batches through a single API to parallelize inference, slash per-request overhead, and unlock bulk processing workflows at scale.

    Throughput at scale

Decision guide

When to Use — When NOT to Use

Use it if...

  • You need a cost-effective general-purpose model for everyday coding and content tasks.
  • You need decent multilingual understanding and translation without requiring best-in-class quality.
  • Your use case involves batch-processing many small requests where price sensitivity is critical.
  • You need a capable assistant for code explanation, minor refactors, and simple bug hunting.
  • Your use case involves lightweight data extraction or summarization from short to medium texts.
  • You need a backup or secondary model to diversify providers for resilience and cost.

Avoid if...

  • You need frontier-level reasoning performance on complex, multi-step scientific or mathematical problems.
  • Your workload requires highly reliable compliance, safety filters, and mature enterprise governance tooling.
  • You need deeply specialized domain knowledge validated against cutting-edge research or proprietary standards.
  • Your workload requires tightly integrated ecosystem tools, plugins, or advanced function-calling capabilities.
  • You need proven, battle-tested performance at very large context windows for lengthy documents.
  • Your workload requires strict SLAs, global support guarantees, and long-term enterprise stability assurances.

FAQ

Frequently Asked Questions

  • What is DeepSeek V3.2?

    DeepSeek V3.2 is a general-purpose large language model by DeepSeek focused on code, reasoning, and tool-using capabilities.

  • What is DeepSeek V3.2 best suited for?

    DeepSeek V3.2 is best for code generation, step-by-step reasoning, data transformation, and building chat-style assistants with strong instruction-following.

  • What is the context window of DeepSeek V3.2?

    DeepSeek V3.2 supports a context window up to 32K tokens, suitable for long conversations and larger documents.

  • How fast is DeepSeek V3.2 when called through LLM.API?

    Typical end-to-end latency ranges from a few hundred milliseconds to a few seconds, depending on prompt size and requested output length.

  • What modalities does DeepSeek V3.2 support via LLM.API?

    Through LLM.API, DeepSeek V3.2 currently supports text input and text output only.

  • How is DeepSeek V3.2 priced on LLM.API?

    LLM.API bills DeepSeek V3.2 per input and output token, with exact rates specified in the LLM.API pricing documentation.

  • How do I call DeepSeek V3.2 from the LLM.API endpoint?

    Specify the model name "deepseek-v3.2" (or the exact identifier from LLM.API docs) in your API request's model parameter.

  • How does DeepSeek V3.2 compare to similar models on LLM.API?

    DeepSeek V3.2 generally targets a balance of reasoning quality and cost, often being cheaper than top-tier frontier models with comparable capabilities.

  • Does DeepSeek V3.2 support tools or function calling via LLM.API?

    Yes, if enabled by LLM.API, DeepSeek V3.2 can consume tool definitions and output structured tool call arguments.

  • What are the main limitations of DeepSeek V3.2?

    DeepSeek V3.2 can hallucinate facts, lacks real-time knowledge, and may struggle with highly domain-specific or very long multi-step tasks.

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