PricingDeepSeek1mo ago
DeepSeek
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....
Running this yourself: can likely run on your own machine.
OpenRouter
Price record: 2026-10-03. Source: openrouter.
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51.7
Quality Score
1373
Arena ELO
671B
Parameters
64K
Context
This measures the amount of verifiable public evidence we have, not how capable the model is. A missing field means it has not been verified yet, not that its value is zero.
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Jan 2025
Released
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Pricing
1
Benchmarks
19
Open Source
1
Research
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language: 0.7 | coding: 1.0 | instruction_following: 1.0 | Overall: 0.9
Arena-Hard-Auto official Gemini-2.5 judged score 58.0 with CI -2.2/2
View sourcelanguage: 0.7 | coding: 1.0 | instruction_following: 1.0 | Overall: 0.9
View sourcelanguage: 0.7 | coding: 1.0 | instruction_following: 1.0 | Overall: 0.9
View sourceWe introduce Nemotron-Cascade 2, an open 30B MoE model with 3B activated parameters that delivers best-in-class reasoning and strong agentic capabilities. Despite its compact size, its mathematical and coding reasoning performance approaches that of frontier open models. It is the second open-weight LLM, after DeepSeekV3.2-Speciale-671B-A37B, to achieve Gold Medal-level performance in the 2025 International Mathematical Olympiad (IMO), the International Olympiad in Informatics (IOI), and the ICPC World Finals, demonstrating remarkably high intelligence density with 20x fewer parameters. In contrast to Nemotron-Cascade 1, the key technical advancements are as follows. After SFT on a meticulously curated dataset, we substantially expand Cascade RL to cover a much broader spectrum of reasoning and agentic domains. Furthermore, we introduce multi-domain on-policy distillation from the strongest intermediate teacher models for each domain throughout the Cascade RL process, allowing us to efficiently recover benchmark regressions and sustain strong performance gains along the way. We release the collection of model checkpoint and training data.
We introduce Nemotron-Cascade 2, an open 30B MoE model with 3B activated parameters that delivers best-in-class reasoning and strong agentic capabilities. Despite its compact size, its mathematical and coding reasoning performance approaches that of frontier open models. It is the second open-weight LLM, after DeepSeekV3.2-Speciale-671B-A37B, to achieve Gold Medal-level performance in the 2025 International Mathematical Olympiad (IMO), the International Olympiad in Informatics (IOI), and the ICPC World Finals, demonstrating remarkably high intelligence density with 20x fewer parameters. In contrast to Nemotron-Cascade 1, the key technical advancements are as follows. After SFT on a meticulously curated dataset, we substantially expand Cascade RL to cover a much broader spectrum of reasoning and agentic domains. Furthermore, we introduce multi-domain on-policy distillation from the strongest intermediate teacher models for each domain throughout the Cascade RL process, allowing us to efficiently recover benchmark regressions and sustain strong performance gains along the way. We release the collection of model checkpoint and training data.
R1 is now available through local Ollama runtime. 128K context window listed. A version of the DeepSeek-R1 model that has been post trained to provide unbiased, accurate, and factual information by Perplexity.
language: 0.7 | coding: 1.0 | instruction_following: 1.0 | Overall: 0.9
Arena-Hard-Auto official Gemini-2.5 judged score 58.0 with CI -2.2/2
language: 0.7 | coding: 1.0 | instruction_following: 1.0 | Overall: 0.9
language: 0.7 | coding: 1.0 | instruction_following: 1.0 | Overall: 0.9
language: 0.7 | coding: 1.0 | instruction_following: 1.0 | Overall: 0.9
language: 0.7 | coding: 1.0 | instruction_following: 1.0 | Overall: 0.9
language: 0.7 | coding: 1.0 | instruction_following: 1.0 | Overall: 0.9
language: 0.7 | coding: 1.0 | instruction_following: 1.0 | Overall: 0.9
language: 0.7 | coding: 1.0 | instruction_following: 1.0 | Overall: 0.9
language: 0.7 | coding: 1.0 | instruction_following: 1.0 | Overall: 0.9
language: 0.7 | coding: 1.0 | instruction_following: 1.0 | Overall: 0.9
language: 0.7 | coding: 1.0 | instruction_following: 1.0 | Overall: 0.9
language: 0.7 | coding: 1.0 | instruction_following: 1.0 | Overall: 0.9
language: 0.7 | coding: 1.0 | instruction_following: 1.0 | Overall: 0.9
language: 0.7 | coding: 1.0 | instruction_following: 1.0 | Overall: 0.9
language: 0.7 | coding: 1.0 | instruction_following: 1.0 | Overall: 0.9
language: 0.7 | coding: 1.0 | instruction_following: 1.0 | Overall: 0.9
language: 0.7 | coding: 1.0 | instruction_following: 1.0 | Overall: 0.9
language: 0.7 | coding: 1.0 | instruction_following: 1.0 | Overall: 0.9