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model

DeepSeek-R1

Open-weight reasoning large language model from Chinese AI lab DeepSeek, trained with reinforcement learning for chain-of-thought reasoning.

Known aliases

  • DeepSeek R1
  • deepseek-r1
  • DeepSeek R1 670B
  • DSR1

Relationships

No evidence-backed relationships are recorded.

Current stories

invest1 publisher

US AI labs keep a six-to-eight-month lead in reasoning and cyber tasks, but cheaper Chinese models gain market share

Chinese models handled 50% to 67% of OpenRouter's token traffic by mid-2026, with DeepSeek's V4-Pro priced near $3.96 per million output tokens. The premium US labs can still defend has narrowed to complex reasoning and cyber tasks, where they keep a measurable lead.

Reality

Evidence35
Adoption50
Hype gap+25
Incentives
Insufficient
Confidence35
build1 publisher

Idiomatic os.path.join trips Gemini 3.7 Flash in a 12-task LLM security benchmark

Six LLMs on a 12-task Kaggle security benchmark all caught SQL injection, hardcoded keys and pickle RCE, but Gemini 3.7 Flash missed a path traversal. With one scenario per flaw class, the run shows which textbook patterns the models know and says little about trusting one to review real code.

Publishers:dev.to

Reality

Evidence30
Adoption
Insufficient
Hype gap+40
Incentives40
Confidence35
invest7 publishers

OpenAI's first chip beat merchant silicon in 16 months. The moat was never the fab

Jalapeno's lead is measured against last generation and the volumes are tiny, but a first-pass ASIC clearing Nvidia, AMD and Google parts reprices the design barrier, not the supply chain.

Perspective Coverage

7 publishers
Builder
Builder 30%
Operator
Operator 24%
Investor
Investor 46%

Reality

Evidence50
Adoption8
Hype gap+40
Incentives65
Confidence55
build1 publisher

Confidential inference on Blackwell retains 96-98 percent of throughput with CC-aware adaptations, NVIDIA reports

NVIDIA measured TensorRT LLM holding 96.1 to 98.2 percent of its non-confidential output throughput on Blackwell, and it got there by unpinning host memory on the affected paths, moving decode readback off the scheduler thread, and timing kernel tactics with the GPU's global timer instead of CUDA events.

Reality

Evidence58
Adoption22
Hype gap+10
Incentives80
Confidence55
build9 publishers

OpenAI's first Jalapeno numbers buy it leverage, not a procurement input

The 1.7x to 3.6x latency range is set by the baseline systems, not the chip, and the report's own publication date is unsettled. Read it as direction, not evidence.

Perspective Coverage

9 publishers
Builder
Builder 41%
Operator
Operator 31%
Investor
Investor 28%

Reality

Evidence52
Adoption14
Hype gap+38
Incentives82
Confidence68

Earlier coverage

  1. DeepSeek V4 moves the coding-model decision into the finance column

    Build · September 1, 2026 · 1 publisher

  2. X discloses Chinese bot farm accounts posting on AI data centre and energy debate

    Invest · August 30, 2026 · 2 publishers

  3. Speculative decoding gets scaling laws you can size a draft model against

    Build · August 28, 2026 · 1 publisher

  4. SPEED-Bench re-tests speculative decoding at the batch size you actually serve

    Build · August 27, 2026 · 1 publisher

  5. Nvidia's power pitch: 40,000 Rubin GPUs in 100MW, or 2.5kW a GPU at the meter

    Build · August 26, 2026 · 1 publisher

  6. The judge went synthetic first, which tells you which part of your pipeline is next

    Build · August 22, 2026 · 1 publisher

  7. Cost per shipped feature, not the leaderboard: one CTO cut a $14k model bill by $9k

    Build · August 19, 2026 · 1 publisher

  8. Nature Perspective: patching one fact into a model leaves the reasoning around it broken

    Science · August 16, 2026 · 1 publisher