Observed Signal · Feb 21, 2025 · Technical Release · Source: OnlineMarketing.de · Impact: 3/5 · Sentiment: Neutral
Perplexity Uncensors DeepSeek R1 1776 AI Model
Perplexity introduces R1 1776, an uncensored variant of its DeepSeek R1-based model. The R1 1776 retraining used a multilingual dataset of 40,000 inputs and a censorship classifier built from around 300 known censored topics in China, guided by human annotators and LLM juries to assess censorship behavior. Benchmark tests show R1 1776 performs on par with the base R1 on core reasoning, while delivering stronger performance on complex mathematical and logical tasks. In direct comparisons with DeepSeek-R1, R1 1776 edges ahead in the AIME 2024 benchmark. Perplexity notes that uncensoring does not degrade core reasoning. The models R1 and R1 1776 are available for download on HuggingFace and via the Sonar API. Separately, DeepSeek recently topped the US free App Store charts in January 2025, highlighting market interest in AI apps.
Uncensored LLM model release with benchmark context; notable to AI/LLM industry
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Key Takeaways & Evidence Grounding
- Perplexity released R1 1776, an uncensored variant of the DeepSeek R1 model.
- R1 1776 was post-trained with a multilingual dataset of 40,000 inputs and a censorship classifier built from about 300 censored topics in China.
- Human annotators and LLM juries evaluated censorship behavior.
- Benchmark results show R1 1776 matches base R1 in core reasoning and outperforms DeepSeek-R1 on the AIME 2024 benchmark.
- R1 and R1 1776 are available on HuggingFace; access is provided via the Sonar API.
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DeepSeek previews V4 open-source LLM
Deepseek on April 24, 2026 published its long‑anticipated Deepseek V4 (variants Pro and Flash), an open‑source large language model built on a new architecture with 1.6 trillion parameters. The company highlights significant gains in reasoning and autonomous code generation, claims benchmark-leading performance in mathematics, STEM and programming among open models, and says V4 supports context windows up to one million tokens while reducing compute and memory costs. Deepseek positions V4 Pro as materially cheaper on coding tasks versus OpenAI’s GPT‑5.5. The rollout also involves a partnership with Huawei, which supplies "Supernode" clusters of Ascend‑950 chips; Deepseek and analysts note a strategic focus on Huawei and Cambricon domestic chips to relieve reliance on Nvidia/AMD. Market reaction is expected to be more muted than Deepseek’s earlier 2025 breakthrough R1 shock.
DeepSeek V4 (MODEL1) Expected with Engram, mHC
DeepSeek, a Chinese open-source AI startup, is expected to release DeepSeek V4 (rumored codename MODEL1) around the Lunar New Year (week of Feb 17, 2026). Reporting and code commits indicate V4 will be a major architectural overhaul focused on extreme long-context coding and software-engineering tasks. Key innovations described include Engram (a conditional memory lookup to separate factual recall from reasoning and enable multi-million-token knowledge stores), Manifold-Constrained Hyper-Connections (mHC) to stabilize rich cross-layer connectivity, and DeepSeek Sparse Attention (DSA) for 1M+ token contexts. DeepSeek reportedly delayed its R2 training after hardware instability with Huawei Ascend chips and reverted to Nvidia GPUs for final training. The article places DeepSeek within a broader surge of Chinese open-weight model activity (names cited include Qwen/Alibaba Cloud, Zhipu AI, Moonshot AI, and Minimax).
DeepSeek R1 vs OpenAI o1: 27x LLM Price Gap
An analysis from Tokonomics compares DeepSeek's R1 model to OpenAI's o1, finding R1 priced at $0.55 per million input tokens versus o1 at $15.00 — a 27x difference. Benchmark results cited (from a DeepSeek technical report) show R1 and o1 scoring within a few points on several reasoning and coding benchmarks, with R1 stronger on math and o1 ahead on one graduate-level science reasoning test. The author highlights three drivers for R1's low price (lower operating costs in China, a mixture-of-experts activation pattern, and aggressive market-share pricing), warns about internal 'thinking tokens' that inflate effective costs, and notes caching and discounted cached-input pricing further widen the gap. The piece recommends validating R1 on production data and escalating to o1 only for cases needing structured outputs, function calling, or enterprise SLAs.
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