AI model
Open-weight reasoning large language model from Chinese AI lab DeepSeek, trained with reinforcement learning for chain-of-thought reasoning.
New Constructs puts Anthropic's equity at as little as $144bn, 93% below the $2tn it is reportedly targeting for its IPO. That is its lowest scenario, built from reports of leaked financials because the prospectus was filed confidentially.
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Anthropic and OpenAI said in September they shut down campaigns to copy their flagship models by training smaller models on their answers. Researchers expect more attempts, since a copier needs only ordinary access to prompt the model and record its replies.
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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.
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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.
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Grok 4.20 followed an injected wrong step about five times as often with reasoning mode on, in a 15-question Kaggle benchmark entry. On the Anthropic test it uses, that makes the trace a better record of how the model answered and a weaker guard against a bad step.
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Blog vs Bytecode, a 28-item Kaggle benchmark, graded empty proxy responses as wrong and scored DeepSeek-R1 at 17% until a second gateway showed 100%. Once capture was fixed, frontier models lost points by flagging sound code, while a small Gemma model missed most of the planted flaws.
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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.
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Reading a 600 GB checkpoint from local NVMe at 7 GB/s takes about 86 seconds, so the network stops gating scale-out on warm nodes. The first fill still costs the full download. The slowest node sets it.
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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.
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A dev.to walkthrough of DeepSeek's GRPO puts a 70B PPO training loop over 600GB of VRAM before any sharding. The critic-free version deletes the most expensive network and pays for it in sampled completions.
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METR also had the agent match the median human expert on five AI R&D tasks, using 32 hours of wall clock against the humans' eight, assembled from attempts of two hours or less. The confidence intervals still overlap the other public models it has tested.
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MLPerf Inference v6.1 preview submissions put Vera Rubin NVL72 at up to 3.7x GB300 on Qwen3-VL and up to 2.5x on DeepSeek-R1, on two different inference frameworks. The four-rack 99% scaling result is an offline number.
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Anthropic tested whether a model's visible reasoning names what actually drove its answer, and for two reasoning models it usually did not. Teams treating traces as an audit record are relying on that property.
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DeepSeek's first CFO arrives with CITIC Securities engaged and a 2027 listing as the goal. The near-term cash is a 50 billion yuan private round, and the two published accounts of June's valuation differ by nearly seven times.
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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.
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American agencies say six Chinese labs bought bulk subscriptions to US models and trained on the outputs since 2024. Enterprise buyers are meanwhile paying a fifth as much for models that clear most of their engineering work.
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A preprint holds 1,520 benchmark responses constant and varies one sentence about what a low score will do to the model being scored. The judges get more lenient, and their reasoning traces never mention the sentence.
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CISA, the NSA and the FBI want US model providers to catch industrial-scale distillation hiding inside traffic that looks like a busy enterprise customer, then quietly degrade the answers, which makes false positives a product decision.
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Sarah Friar told Goldman Sachs' technology conference that a pricier model can be cheaper when it needs fewer tries, a claim Artificial Analysis puts at an eleven-to-one price spread against seven index points running the other way.
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The joint NSA, CISA and FBI advisory names six China-based companies and describes billions of tokens taken through paid APIs, cloud resellers and proxy transfer stations, which turns billing telemetry into a detection surface.
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Invest · August 30, 2026 · Confirmed2 publishers