DeepSeekMath V2
BenchLM is tracking DeepSeekMath V2, but sourced benchmark results are not published on the site yet. This page currently shows the model metadata we can verify now, and score-level benchmark coverage will appear once public evaluations land.
DeepSeekMath V2 is a open weight model with a 128K token context window. It uses explicit chain-of-thought reasoning, which typically improves performance on math and complex reasoning tasks at the cost of higher latency and token usage.
This profile currently has 0 sourced benchmarks on BenchLM, so the benchmark sections below are intentionally marked as coming soon.
Its strongest category is Mathematics (#30), while its weakest is Instruction Following (#64). 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
#42Coding
#46Reasoning
#35Knowledge
#43Math
#30Multilingual
#32Multimodal
#64Inst. 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 DeepSeekMath V2 stacks up against similar models
Frequently Asked Questions
How does DeepSeekMath V2 perform overall in AI benchmarks?
BenchLM is tracking DeepSeekMath V2, but sourced benchmark coverage is still coming soon. We currently list its creator, model type, and context window while we wait for public benchmark results.
Is DeepSeekMath V2 open source?
Yes, DeepSeekMath V2 is an open weight model created by DeepSeek, meaning it can be downloaded and run locally or fine-tuned for specific use cases.
Does DeepSeekMath V2 have full benchmark coverage on BenchLM?
Not yet. DeepSeekMath V2 currently has 0 published benchmark scores out of the 152 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 DeepSeekMath V2?
DeepSeekMath V2 has a context window of 128K, which determines how much text it can process in a single interaction.
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