deepseek

DeepSeek-V3.2-Speciale

DeepSeek-V3.2-Speciale is a reasoning-first LLM featuring gold-medal math performance, DeepSeek Sparse Attention, and a 131K context window. Rivaling GPT-5...

DeepSeekReasoningAIOpenSourceMathOlympiadSparseAttention
deepseek logodeepseekDeepSeek-V3December 1, 2025
Context
131Ktokens
Max Output
47Ktokens
Input Price
$0.28/ 1M
Output Price
$0.42/ 1M
Modality:Text
Capabilities:StreamingReasoning
Benchmarks
GPQA
59%
GPQA: Graduate-Level Science Q&A. A rigorous benchmark with 448 multiple-choice questions in biology, physics, and chemistry created by domain experts. PhD experts only achieve 65-74% accuracy, while non-experts score just 34% even with unlimited web access (hence 'Google-proof'). DeepSeek-V3.2-Speciale scored 59% on this benchmark.
HLE
25%
HLE: High-Level Expertise Reasoning. Tests a model's ability to demonstrate expert-level reasoning across specialized domains. Evaluates deep understanding of complex topics that require professional-level knowledge. DeepSeek-V3.2-Speciale scored 25% on this benchmark.
MMLU
89%
MMLU: Massive Multitask Language Understanding. A comprehensive benchmark with 16,000 multiple-choice questions across 57 academic subjects including math, philosophy, law, and medicine. Tests broad knowledge and reasoning capabilities. DeepSeek-V3.2-Speciale scored 89% on this benchmark.
MMLU Pro
76%
MMLU Pro: MMLU Professional Edition. An enhanced version of MMLU with 12,032 questions using a harder 10-option multiple choice format. Covers Math, Physics, Chemistry, Law, Engineering, Economics, Health, Psychology, Business, Biology, Philosophy, and Computer Science. DeepSeek-V3.2-Speciale scored 76% on this benchmark.
SimpleQA
21%
SimpleQA: Factual Accuracy Benchmark. Tests a model's ability to provide accurate, factual responses to straightforward questions. Measures reliability and reduces hallucinations in knowledge retrieval tasks. DeepSeek-V3.2-Speciale scored 21% on this benchmark.
IFEval
86%
IFEval: Instruction Following Evaluation. Measures how well a model follows specific instructions and constraints. Tests the ability to adhere to formatting rules, length limits, and other explicit requirements. DeepSeek-V3.2-Speciale scored 86% on this benchmark.
AIME 2025
96%
AIME 2025: American Invitational Math Exam. Competition-level mathematics problems from the prestigious AIME exam designed for talented high school students. Tests advanced mathematical problem-solving requiring abstract reasoning, not just pattern matching. DeepSeek-V3.2-Speciale scored 96% on this benchmark.
MATH
90%
MATH: Mathematical Problem Solving. A comprehensive math benchmark testing problem-solving across algebra, geometry, calculus, and other mathematical domains. Requires multi-step reasoning and formal mathematical knowledge. DeepSeek-V3.2-Speciale scored 90% on this benchmark.
GSM8k
96%
GSM8k: Grade School Math 8K. 8,500 grade school-level math word problems requiring multi-step reasoning. Tests basic arithmetic and logical thinking through real-world scenarios like shopping or time calculations. DeepSeek-V3.2-Speciale scored 96% on this benchmark.
MGSM
92%
MGSM: Multilingual Grade School Math. The GSM8k benchmark translated into 10 languages including Spanish, French, German, Russian, Chinese, and Japanese. Tests mathematical reasoning across different languages. DeepSeek-V3.2-Speciale scored 92% on this benchmark.
MathVista
71%
MathVista: Mathematical Visual Reasoning. Tests the ability to solve math problems that involve visual elements like charts, graphs, geometry diagrams, and scientific figures. Combines visual understanding with mathematical reasoning. DeepSeek-V3.2-Speciale scored 71% on this benchmark.
SWE-Bench
73%
SWE-Bench: Software Engineering Benchmark. AI models attempt to resolve real GitHub issues in open-source Python projects with human verification. Tests practical software engineering skills on production codebases. Top models went from 4.4% in 2023 to over 70% in 2024. DeepSeek-V3.2-Speciale scored 73% on this benchmark.
HumanEval
83%
HumanEval: Python Programming Problems. 164 hand-written programming problems where models must generate correct Python function implementations. Each solution is verified against unit tests. Top models now achieve 90%+ accuracy. DeepSeek-V3.2-Speciale scored 83% on this benchmark.
LiveCodeBench
38%
LiveCodeBench: Live Coding Benchmark. Tests coding abilities on continuously updated, real-world programming challenges. Unlike static benchmarks, uses fresh problems to prevent data contamination and measure true coding skills. DeepSeek-V3.2-Speciale scored 38% on this benchmark.
MMMU
65%
MMMU: Multimodal Understanding. Massive Multi-discipline Multimodal Understanding benchmark testing vision-language models on college-level problems across 30 subjects requiring both image understanding and expert knowledge. DeepSeek-V3.2-Speciale scored 65% on this benchmark.
MMMU Pro
58%
MMMU Pro: MMMU Professional Edition. Enhanced version of MMMU with more challenging questions and stricter evaluation. Tests advanced multimodal reasoning at professional and expert levels. DeepSeek-V3.2-Speciale scored 58% on this benchmark.
ChartQA
89%
ChartQA: Chart Question Answering. Tests the ability to understand and reason about information presented in charts and graphs. Requires extracting data, comparing values, and performing calculations from visual data representations. DeepSeek-V3.2-Speciale scored 89% on this benchmark.
DocVQA
93%
DocVQA: Document Visual Q&A. Document Visual Question Answering benchmark testing the ability to extract and reason about information from document images including forms, reports, and scanned text. DeepSeek-V3.2-Speciale scored 93% on this benchmark.
Terminal-Bench
48%
Terminal-Bench: Terminal/CLI Tasks. Tests the ability to perform command-line operations, write shell scripts, and navigate terminal environments. Measures practical system administration and development workflow skills. DeepSeek-V3.2-Speciale scored 48% on this benchmark.
ARC-AGI
14%
ARC-AGI: Abstraction & Reasoning. Abstraction and Reasoning Corpus for AGI - tests fluid intelligence through novel pattern recognition puzzles. Each task requires discovering the underlying rule from examples, measuring general reasoning ability rather than memorization. DeepSeek-V3.2-Speciale scored 14% on this benchmark.

About DeepSeek-V3.2-Speciale

Learn about DeepSeek-V3.2-Speciale's capabilities, features, and how it can help you achieve better results.

High-Compute Reasoning Focus

DeepSeek-V3.2-Speciale is a Mixture-of-Experts language model featuring 685 billion parameters. It activates 37 billion parameters per token to balance performance and efficiency. This variant is engineered to solve multi-step problems that require significant internal reasoning. By scaling post-training compute to over 10% of the pre-training budget, the model produces long chain-of-thought trajectories that can exceed 47,000 tokens per response. This makes it suitable for navigating complex logical proofs and technical research.

Sparse Attention Architecture

The model uses DeepSeek Sparse Attention (DSA) to manage its 131,072-token context window. This mechanism employs a lightning indexer to isolate the most relevant tokens, reducing the computational burden typically found in dense long-context systems. It maintains logic while operating with lower overhead. The architecture specifically targets high-compute environments where reasoning depth is prioritized over broad multimodal flexibility.

Technical and Academic Performance

Speciale is the first open-weights model to achieve gold-medal level results in the 2025 International Mathematical Olympiad (IMO). It excels in technical benchmarks like AIME 2025 and HumanEval, often matching proprietary systems in pure logic tasks. Developers can use it for generating complex codebases or synthesizing dense technical documentation. The model is released under the MIT license, facilitating broad utility in the open-source community.

DeepSeek-V3.2-Speciale

Use Cases

Discover the different ways you can use DeepSeek-V3.2-Speciale to achieve great results.

Mathematical Proof Generation

Solving olympiad-level mathematical proofs and symbolic logic problems requiring high cognitive depth.

Architectural Software Design

Generating complex, multi-file software architectures by reasoning through structural dependencies without tool-use.

Technical Document Synthesis

Analyzing and cross-referencing insights across massive technical papers within its 131K context window.

Synthetic Data Production

Creating high-quality reasoning-rich training datasets to distill logic into smaller, specialized AI models.

Scientific Deep-Dive Research

Reviewing and synthesizing dense academic literature to extract nuanced logical progression in STEM fields.

Autonomous Agent Planning

Navigating complex multi-step planning and strategy development for AI agents in simulated environments.

Strengths

Limitations

Elite Math Performance: Achieves 96% on AIME 2025 and gold-medal results in the IMO international competition.
No Native Multimodality: Lacks the ability to process images or audio, restricting its use to text-based data.
Massive Thinking Budget: Generates thinking trajectories exceeding 47,000 tokens for deep logical exploration.
Disabled Tool Calling: Does not support function calling, limiting its utility for autonomous API interaction.
DSA Efficiency: The DeepSeek Sparse Attention mechanism handles 128K context with lower compute costs than dense models.
Inference Latency: Thinking modes can cause wait times of several minutes for complex logical proofs.
Cost Advantage: Provides frontier reasoning at $0.28 per million tokens, which is significantly cheaper than proprietary peers.
Hosting Requirements: Requires enterprise-grade GPU clusters due to its 685-billion parameter MoE architecture.

API Quick Start

deepseek/deepseek-v3.2-speciale

View Documentation
deepseek SDK
import OpenAI from "openai";

const openai = new OpenAI({
  baseURL: "https://api.deepseek.com",
  apiKey: process.env.DEEPSEEK_API_KEY,
});

async function main() {
  const completion = await openai.chat.completions.create({
    messages: [{ role: "user", content: "Solve for x in the complex plane: e^z = -1." }],
    model: "deepseek-v3.2-speciale",
  });

  console.log(completion.choices[0].message.content);
}

main();

Install the SDK and start making API calls in minutes.

Community Feedback

See what the community thinks about DeepSeek-V3.2-Speciale

DeepSeek-V3.2-Speciale is a beast. Maxed out reasoning that rivals Gemini 3 Pro.
OpenRouter
twitter
The HumanEval scores are real. It writes cleaner code than many proprietary models I've tested this month.
dev_guru_99
reddit
The pricing is just insane. Frontier-level reasoning at a fraction of the cost of OpenAI or Anthropic.
AI_Builder_X
twitter
It's refreshing to see an open-weight model actually challenge the top 3 labs. The architecture choices here are brilliant.
binary_explorer
hackernews

Related Videos

Watch tutorials, reviews, and discussions about DeepSeek-V3.2-Speciale

It does reason for an insane amount of time... it's a deep deep reasoner.

DeepSeek model gets gold [in IMO] while Deepthink only got bronze.

Watching what it does could be extremely educationally valuable.

The logic here is on another level compared to standard models.

You can actually see the model iterating through failures.

All special really means is deep think. It's got a deep think mode.

It thinks for 63 seconds... that's pretty amazing.

It's very smart there... coming up with some really advanced code.

Scaling this locally is going to be the biggest hurdle for users.

The reasoning tokens are billed differently on most platforms.

Special is the beast. Maxed out reasoning, deep chain of thought.

Special is designed for deep reasoning, multi-step proofs, complex research.

DeepSeek's transparency is a massive advantage... seeing the work.

It manages to stay coherent over much longer responses than Gemini.

The Sparse Attention tech is how they keep the pricing this low.

More than just prompts

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AI Agents
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Smart Workflows

Pro Tips

Expert tips to help you get the most out of DeepSeek-V3.2-Speciale and achieve better results.

Optimize Sampling Parameters

Set temperature to 1.0 and top_p to 0.95 for high-logic tasks to ensure deep reasoning paths remain diverse.

Provide Technical Detail

Structure instructions in markdown to help the model better organize its internal chain-of-thought processing.

Allow for Thinking Time

Expect higher latency during complex proofs because the model generates massive internal thinking chains.

Use Dedicated Endpoints

Specify the 'speciale' API path in your configuration to access the high-compute reasoning variant specifically.

Testimonials

What Our Users Say

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Mohammed Ibrahim

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Ben Bressington

Ben Bressington

CTO, AiChatSolutions

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Sarah Chen

Sarah Chen

Head of Growth, ScaleUp Labs

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Founder, DataDriven.io

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Frequently Asked Questions

Find answers to common questions about DeepSeek-V3.2-Speciale