DeepSeek-R1
Self-host vs API cost
Estimates at 50,000 req/day · 1000 tokens/req average.
BenchLM is tracking DeepSeek-R1, 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-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 0 sourced benchmarks on BenchLM, so the benchmark sections below are intentionally marked as coming soon.
Its strongest category is Coding (#60), while its weakest is Multimodal & Grounded (#93). This performance profile makes it particularly well-suited for software development and code generation tasks.
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
#68Coding
#60Reasoning
#81Knowledge
#65Math
#65Multilingual
#86Multimodal
#93Inst. Following
#69Chatbot 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?
BenchLM is tracking DeepSeek-R1, 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-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 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-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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