DeepSeek: R1
Analysis Summary
DeepSeek R1 is a full reasoning model with an intelligence index of 18.5 and coding index of 24.6, placing it among the stronger models outside the top-tier flagship bracket. GPQA at 0.708, MMLU-Pro at 0.844, and LiveCodeBench at 0.617 are all high-quality scores. The math index of 68 and AIME score of 0.68 reflect exceptional quantitative reasoning. Tool use and function calling are supported, and the 163K context window is generous.
For businesses, R1 suits complex coding tasks, mathematical analysis, long-document reasoning, and structured content generation. The long-context reliability score of 0.523 is a positive signal for document-heavy workflows. The main limitation is a low agentic index of 8.7, meaning it is less reliable for autonomous multi-step pipelines than its raw reasoning scores might suggest.
At $0.70 input and $2.50 output per million tokens, it is priced competitively for its capability tier. Teams needing deep reasoning and coding support without flagship costs will find strong value here, particularly for analytical and technical workloads.
Assessed July 10, 2026
Editorial notes
DeepSeek R1 delivers frontier-level math and coding performance with a 163K context window, tool use, and function calling at competitive pricing; agentic reliability is limited but reasoning depth is strong.
Rankings consider pricing, capabilities, benchmarks, and real-world applicability and are refreshed as new models launch. Feedback?
DFO Verdict
DeepSeek R1 delivers frontier-level math and coding performance with a 163K context window, tool use, and function calling at competitive pricing; agentic reliability is limited but reasoning depth is strong.
How DeepSeek: R1 compares
DeepSeek: R1 ranks #160 of 393 AI models we track for overall intelligence, #85 of 157 for coding, #282 of 300 for agentic tasks. Its 164K-token context window is larger than 59% of the models we list. At $0.70 per million input tokens it is cheaper than 31% of comparable models.
Dark bar = input Ā· light bar = output, scaled to the priciest peer.
1M tokens ā 8 full-length novels or ~2,500 pages of business documents in a single request.
Strongest on value. The pulled-in technical corner is the trade-off, and if the shape matters more than the price, this is your model.
Compare shapes side-by-side āPricing
| Token Type | Cost per 1M tokens | Cost per 1K tokens |
|---|---|---|
| Input | $0.70 | $0.000700 |
| Output | $2.50 | $0.002500 |
What would DeepSeek: R1 cost your business?
Pick the job that looks most like yours, then fine-tune with the sliders. Estimates update live.
A website chatbot handling around 100 customer conversations a day, a few short messages each.
Full calculator with 612 models ā Price Calculator
These numbers get smaller with the right architecture.
We route routine calls to cheap models and save DeepSeek: R1 for the hard ones. Most clients cut their estimate by 60-80%.
Talk to our teamAbout DeepSeek: R1
DeepSeek R1 is here: Performance on par with OpenAI o1, but open-sourced and with fully open reasoning tokens. It's 671B parameters in size, with 37B active in an inference pass..
Frequently asked questions about DeepSeek: R1
How much does DeepSeek: R1 cost?
DeepSeek: R1 costs $0.70 per million input tokens and $2.50 per million output tokens.
What is the context window of DeepSeek: R1?
DeepSeek: R1 has a context window of 163,840 tokens (164K).
Is DeepSeek: R1 good for coding?
On our coding benchmark index, DeepSeek: R1 ranks #85 of 157 models, placing it in the broader range of the field for code generation and debugging.
What can DeepSeek: R1 do?
DeepSeek: R1 supports tool use and function calling.
Who created DeepSeek: R1?
DeepSeek: R1 is developed by DeepSeek and was released on January 20, 2025.
