DeepSeek-R1
Self-host vs API cost
Estimates at 50,000 req/day · 1000 tokens/req average.
According to BenchLM.ai, DeepSeek-R1 ranks #90 out of 119 models on the provisional leaderboard with an overall score of 33/100. 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-R1 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 16 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 (#67), while its weakest is Multimodal & Grounded (#92). 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
#72Coding
#70Reasoning
#80Knowledge
#73Math
#67Multilingual
#88Multimodal
#92Inst. Following
#76Chatbot 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-R1 stacks up against similar models
Frequently Asked Questions
How does DeepSeek-R1 perform overall in AI benchmarks?
DeepSeek-R1 currently ranks #90 out of 119 models on BenchLM's provisional leaderboard with an overall score of 33 (estimated). It is created by DeepSeek and features a 128K context window.
Is DeepSeek-R1 good for knowledge and understanding?
DeepSeek-R1 ranks #73 out of 119 models in knowledge and understanding benchmarks with an average score of 39. There are stronger options in this category.
Is DeepSeek-R1 good for coding and programming?
DeepSeek-R1 ranks #70 out of 119 models in coding and programming benchmarks with an average score of 27.1. There are stronger options in this category.
Is DeepSeek-R1 good for reasoning and logic?
DeepSeek-R1 ranks #80 out of 119 models in reasoning and logic benchmarks with an average score of 23.5. There are stronger options in this category.
Is DeepSeek-R1 good for agentic tool use and computer tasks?
DeepSeek-R1 ranks #72 out of 119 models in agentic tool use and computer tasks benchmarks with an average score of 31.6. There are stronger options in this category.
Is DeepSeek-R1 good for instruction following?
DeepSeek-R1 ranks #76 out of 119 models in instruction following benchmarks with an average score of 54.8. There are stronger options in this category.
Is DeepSeek-R1 open source?
Yes, DeepSeek-R1 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-R1 have full benchmark coverage on BenchLM?
Not yet. DeepSeek-R1 currently has 16 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-R1?
DeepSeek-R1 has a context window of 128K, which determines how much text it can process in a single interaction.
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