DeepSeek V3.2
According to BenchLM.ai, DeepSeek V3.2 ranks #52 out of 119 models on the provisional leaderboard with an overall score of 57/100. It does not yet have enough sourced coverage for BenchLM's verified leaderboard. While not a frontier model, it offers specific advantages depending on the use case.
DeepSeek V3.2 is a open weight model with a 128K token context window. It processes queries without explicit chain-of-thought reasoning, offering faster response times and lower token usage.
DeepSeek V3.2 sits inside the DeepSeek V3.2 family alongside DeepSeek V3.2 (Thinking). This profile currently has 21 of 225 tracked benchmarks. BenchLM only exposes non-generated benchmark rows publicly, so missing categories stay blank until a sourced evaluation is available.
Its strongest category is Mathematics (#34), while its weakest is Multimodal & Grounded (#67). This performance profile makes it particularly strong for mathematical reasoning, scientific computing, and quantitative analysis.
Ranking Distribution
Category rank across 8 benchmark categories — sorted by best rank
Category Performance
Scores across all benchmark categories (0-100 scale)
Category Breakdown
Agentic
#45Coding
#47Reasoning
#56Knowledge
#48Math
#34Multilingual
#35Multimodal
#67Inst. Following
#64Chatbot Arena Performance
Benchmark Details
Only benchmark rows with an attached exact-source record are shown here. Source-unverified manual rows and generated rows are hidden from model pages.
Compare This Model
See how DeepSeek V3.2 stacks up against similar models
Frequently Asked Questions
How does DeepSeek V3.2 perform overall in AI benchmarks?
DeepSeek V3.2 currently ranks #52 out of 119 models on BenchLM's provisional leaderboard with an overall score of 57 (estimated). It is created by DeepSeek and features a 128K context window.
Is DeepSeek V3.2 good for knowledge and understanding?
DeepSeek V3.2 ranks #48 out of 119 models in knowledge and understanding benchmarks with an average score of 60.1. There are stronger options in this category.
Is DeepSeek V3.2 good for coding and programming?
DeepSeek V3.2 ranks #47 out of 119 models in coding and programming benchmarks with an average score of 58.2. There are stronger options in this category.
Is DeepSeek V3.2 good for reasoning and logic?
DeepSeek V3.2 ranks #56 out of 119 models in reasoning and logic benchmarks with an average score of 47.7. There are stronger options in this category.
Is DeepSeek V3.2 good for agentic tool use and computer tasks?
DeepSeek V3.2 ranks #45 out of 119 models in agentic tool use and computer tasks benchmarks with an average score of 51.1. There are stronger options in this category.
Is DeepSeek V3.2 good for instruction following?
DeepSeek V3.2 ranks #64 out of 119 models in instruction following benchmarks with an average score of 62. There are stronger options in this category.
Is DeepSeek V3.2 open source?
Yes, DeepSeek V3.2 is an open weight model created by DeepSeek, meaning it can be downloaded and run locally or fine-tuned for specific use cases.
Which sibling models are related to DeepSeek V3.2?
DeepSeek V3.2 belongs to the DeepSeek V3.2 family. Related variants on BenchLM include DeepSeek V3.2 (Thinking).
Does DeepSeek V3.2 have full benchmark coverage on BenchLM?
Not yet. DeepSeek V3.2 currently has 21 published benchmark scores out of the 225 benchmarks BenchLM tracks. BenchLM only exposes non-generated public benchmark rows, so missing categories stay blank until a sourced evaluation is available.
What is the context window size of DeepSeek V3.2?
DeepSeek V3.2 has a context window of 128K, which determines how much text it can process in a single interaction.
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