DeepSeek V3
Self-host vs API cost
Estimates at 50,000 req/day · 1000 tokens/req average.
According to BenchLM.ai, DeepSeek V3 ranks #77 out of 110 models on the provisional leaderboard with an overall score of 37/100. 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 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 5 of 152 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 Knowledge (#57), while its weakest is Instruction Following (#57). This performance profile makes it particularly effective for knowledge-intensive tasks like research, analysis, and factual Q&A.
Ranking Distribution
Category rank across 5 benchmark categories — sorted by best rank
Category Performance
Scores across all benchmark categories (0-100 scale)
Category Breakdown
Agentic
Coding
Reasoning
Knowledge
#57Math
Multilingual
Multimodal
Inst. Following
#57Chatbot 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 stacks up against similar models
Frequently Asked Questions
How does DeepSeek V3 perform overall in AI benchmarks?
DeepSeek V3 currently ranks #77 out of 110 models on BenchLM's provisional leaderboard with an overall score of 37 (estimated). It is created by DeepSeek and features a 128K context window.
Is DeepSeek V3 good for knowledge and understanding?
DeepSeek V3 ranks #57 out of 110 models in knowledge and understanding benchmarks with an average score of 47.2. There are stronger options in this category.
Is DeepSeek V3 good for coding and programming?
DeepSeek V3 has visible benchmark coverage in coding and programming, but BenchLM does not currently assign it a global category rank there.
Is DeepSeek V3 good for instruction following?
DeepSeek V3 ranks #57 out of 110 models in instruction following benchmarks with an average score of 62.5. There are stronger options in this category.
Is DeepSeek V3 open source?
Yes, DeepSeek V3 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 V3 have full benchmark coverage on BenchLM?
Not yet. DeepSeek V3 currently has 5 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 V3?
DeepSeek V3 has a context window of 128K, which determines how much text it can process in a single interaction.
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