DeepSeek LLM 2.0
BenchLM is tracking DeepSeek LLM 2.0, 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.
DeepSeek LLM 2.0 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.
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 (#36), while its weakest is Multimodal & Grounded (#71). 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
#50Reasoning
#41Knowledge
#48Math
#36Multilingual
#52Multimodal
#71Inst. Following
#49Chatbot 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 LLM 2.0 stacks up against similar models
Frequently Asked Questions
How does DeepSeek LLM 2.0 perform overall in AI benchmarks?
BenchLM is tracking DeepSeek LLM 2.0, 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 DeepSeek LLM 2.0 open source?
Yes, DeepSeek LLM 2.0 is an open weight model created by DeepSeek, meaning it can be downloaded and run locally or fine-tuned for specific use cases.
Does DeepSeek LLM 2.0 have full benchmark coverage on BenchLM?
Not yet. DeepSeek LLM 2.0 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 DeepSeek LLM 2.0?
DeepSeek LLM 2.0 has a context window of 128K, which determines how much text it can process in a single interaction.
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