Model
deepseek-r1
DeepSeek-R1 was an open-weight reasoning model released in January 2025 by the Chinese lab DeepSeek, reported to be comparable to OpenAI's o1 on maths, coding and reasoning benchmarks but published under an MIT licence with a paper describing its training method. It mattered as much for economics as capability: DeepSeek said the underlying V3 model's final training run cost about $5.6 million, far below the budgets assumed for frontier systems, and the release helped trigger a sharp sell-off in US technology stocks — including a record one-day loss for Nvidia — that punctured the assumption that frontier AI required enormous, defensible capital.
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- Benchmarks & progress 4
- Open weights & ecosystem 3
- Security & misuse 2
- Models & capabilities 2
- Money & business 2
- Safety & alignment 1
- Ideas & essays 1
- Government & policy 1
- Culture & impact 1
- Compute & infrastructure 1
US CAISI finds DeepSeek models far more jailbreak-susceptible than US frontier models
The report also found DeepSeek's most secure model was twelve times more likely than US models to follow malicious instructions hidden inside an AI agent's task.
Benchmarks & progress · Safety & alignment · Security & misuse
ARC Prize compares reasoning models with no clear winner
ARC-AGI-2 remained unsolved by every system tested, and which model looked best depended entirely on whether accuracy or cost per task was prioritised.
Benchmarks & progress
ETH Zurich's 'Proof or Bluff?' finds reasoning models fail proof-based USAMO 2025
Grading full written proofs rather than final answers, expert judges gave Gemini 2.5 Pro 24% and every other tested model under 5%, out of a possible 100%.
Benchmarks & progress
Alibaba releases QwQ-32B (full release)
Alibaba's Qwen team said reinforcement learning let a 32-billion-parameter model reach performance comparable to DeepSeek-R1's 671-billion-parameter model, under an Apache 2.0 licence.
Open weights & ecosystem · Models & capabilities · Benchmarks & progress
Perplexity open-sources decensored DeepSeek R1 variant
Perplexity retrained R1 on 40,000 examples covering roughly 300 CCP-restricted topics, reporting near-identical math and knowledge benchmark scores to the original.
Open weights & ecosystem
Cisco researchers report DeepSeek R1 fails all HarmBench jailbreak tests
Researchers ran 50 automated HarmBench prompts against six models; DeepSeek R1 refused none of them, while OpenAI's o1-preview refused the most.
Security & misuse
Dario Amodei publishes 'On DeepSeek and Export Controls'
Amodei called DeepSeek's V3 training cost 'on-trend' rather than a discontinuity, and argued controls matter because millions of smuggled chips are harder to hide than thousands.
Ideas & essays · Government & policy
NVIDIA loses a record amount of market value in a day
The roughly $589bn one-day fall, the largest for any US company on record, followed DeepSeek's claim that a competitive model cost about $5.6m to train.
Culture & impact · Money & business · Compute & infrastructure
DeepSeek releases R1, and the market notices
A Chinese lab matched frontier reasoning performance with open weights and a published method, wiping hundreds of billions off US tech stocks a week later.
Open weights & ecosystem · Models & capabilities · Money & business