models/deepseek/deepseek-v3-2
D
DeepSeek·active

DeepSeek V3.2

DeepSeek's mid-tier model. Context window: 131K tokens.

Overall score
4.31
/5.00 · ranked #31
Input
$0.252
per 1M tokens
Output
$0.378
per 1M tokens
Context
131K
tokens
Blended
$0.346
3:1 out:in ratio

Price drops, new benchmarks, model updates. Stay current on DeepSeek V3.2.

One email per change. Unsubscribe anytime.

modelpicker.aipowered by live benchmark data

Scores by test

Methodology →
Structured Output
5.0
Strategic Analysis
5.0
Constrained Rewriting
4.0
Creative Problem Solving
4.0
Tool Calling
3.0
Faithfulness
5.0
Classification
3.0
Long Context
5.0
Safety Calibration
2.0
Persona Consistency
5.0
Agentic Planning
5.0
Multilingual
5.0
Tabular Data
5.0

What you need to know

DeepSeek V3.2 is a high-utility model optimized for complex structural and strategic tasks. It demonstrates peak performance in structured output, agentic planning, and strategic analysis, making it highly reliable for generating precise data formats and executing multi-step reasoning. Its 131K context window is fully leveraged, scoring a 5/5 for long-context retrieval and faithfulness.

At a blended cost of $0.346/MTok, this model provides a significant value proposition for developers requiring high-reasoning capabilities without the premium pricing of top-tier frontier models. It maintains a strong balance between cost and quality, ranking in the top third of 71 evaluated models with an average internal score of 4.31/5.0.

The model has notable deficiencies in safety calibration and basic classification. It also underperforms in tool calling compared to its reasoning capabilities, suggesting it is better suited as a core logic engine than a standalone autonomous agent relying heavily on external API integrations.

Use this model if your workflow requires strict adherence to structured formats, long-document analysis, or complex strategic planning at a low cost. Skip this model if your application requires rigorous safety guardrails or high-precision classification and tool-calling reliability.

Strengths — Top 3

Structured Output5.0/5.0
Strategic Analysis5.0/5.0
Faithfulness5.0/5.0

Relative weaknesses — Bottom 3

Safety Calibration2.0/5.0
Tool Calling3.0/5.0
Classification3.0/5.0

Similar models

QQwen: Qwen3.6 Flash$0.8914.23Oo4 Mini$3.584.46OOpenAI: gpt-oss-120b$0.1454.08QQwen: Qwen3.6 Plus$1.544.54