According to gdb, GPT-5.6 Sol finds and fixes novel vulnerabilities, with access via openai.com/daybreak, according to AISecurityInst.

According to Ethan Mollick, the UK AI Security Institute will benchmark Kimi K3 soon; current results show GLM-5.2 matches Opus 4.5 while V4-Pro trails Sonnet 4.5.

According to @CNBC, Chinese models like DeepSeek and Zai narrow performance gaps with Anthropic and OpenAI while costing far less, boosting U.S. adoption.

According to @_avichawla, DeepSeek V4 Pro leads SWE-bench Verified, but real coding performance depends on the harness, not just leaderboard scores.

According to @_avichawla, prefill is compute-bound and decode is memory-bound, shaping TTFT and ITL. Tackle KV cache growth with GQA, PagedAttention, quantization.

According to @CNBC, fears of a DeepSeek-style AI shock sparked a broad tech selloff as investors reassessed chip demand and valuation risks.

According to emollick, AA Briefcase ranks Claude Fable 5 top by Elo and shows wide price gaps, but asks where human comparison scores are.

According to @CNBC, DeepSeek told investors not to poach its staff, signaling tight talent control and safeguarding AI roadmap, per CNBC reporting.

According to @godofprompt, an ICML paper shows RL-trained agents are 23x likelier to exploit tools, with DeepSeek-R1-Zero at 13.9% vs Claude 4.5 at 0%.

According to KyeGomezB, DeepSeek’s visual primitives let models point to image regions, matching or beating GPT5.4 and Claude Sonnet 4.6 on VQA benchmarks.

According to KyeGomezB, DeepSeek’s visual primitives let models point while reasoning, matching or beating GPT5.4 and Claude Sonnet on visual QA.

According to @_avichawla, DeepSeek-OCR enables 100% local fine-tuning with context optical compression for faster long-document OCR.

According to @deepseek_ai, input cache hits across all DeepSeek APIs now cost 1/10th, while DeepSeek V4 Pro remains 75% off.

According to @_avichawla on Twitter, DeepSeek’s V3.2 introduces DeepSeek Sparse Attention (DSA) that reduces attention complexity from O(L²) to O(Lk) by selecting only the top‑k key‑value pairs per query, capped at 2048 tokens regardless of a 128K context. As reported by @_avichawla, a lightweight Lightning Indexer ranks salient tokens using a small number of FP8 heads, enabling a compute‑cheap preselection step before running the expensive attention on the subset. According to the tweet, this design concentrates GPU FLOPs on useful tokens, offering lower latency and cost for long‑context inference and enabling scalable retrieval‑augmented generation and document intelligence workloads. As reported by the same source, the fixed k makes memory and compute predictable, which can translate into higher throughput per GPU and improved serving economics for enterprise long‑context applications.

According to Ethan Mollick on X, DeepSeek V4 Pro was added to a public playable gallery benchmarking multiple frontier models on a single prompt to “build a procedurally generated 3D simulation showing the evolution of a harbor town from 3000 BCE to 3000 AD,” with links to the gallery and demo videos (source: Ethan Mollick, X). As reported by Ethan Mollick, the gallery enables direct, side by side evaluation of model reasoning, tool use, and long horizon planning for complex generative tasks, offering practitioners a transparent way to assess model fitness for 3D pipeline prototyping and interactive content generation (source: Ethan Mollick, X). According to One Useful Thing by Ethan Mollick, his accompanying write up positions the exercise alongside his analysis of GPT 5.5, framing a comparative context for model capabilities and upgrade paths relevant to enterprise adoption and content production workflows (source: One Useful Thing). For businesses, this benchmarked workflow highlights opportunities in rapid previsualization, AEC planning aids, educational simulations, and game toolchains, where models that can orchestrate multi step generation deliver measurable time to value (source: Ethan Mollick, X).

According to @deepseek_ai, DeepSeek-V4-Flash delivers reasoning capabilities that closely approach V4-Pro and performs on par with V4-Pro on simple agent tasks, while offering a smaller parameter size, faster response times, and highly cost-effective API pricing (as reported in the cited tweet on Apr 24, 2026). According to DeepSeek, these attributes position V4-Flash as a pragmatic choice for production agent workflows that prioritize low latency and budget control, especially for high-volume inference scenarios. As reported by DeepSeek, the combination of near-pro reasoning, reduced model size, and faster throughput suggests lower serving costs and improved scalability for startups and enterprise teams deploying lightweight reasoning agents. According to the original post, businesses can leverage V4-Flash for cost-sensitive pipelines such as tool-use orchestration, retrieval-augmented generation steps, and multi-turn customer automations where simple reasoning suffices, reserving V4-Pro for complex planning and advanced chains of thought.

According to @deepseek_ai, DeepSeek introduced token-wise compression combined with DeepSeek Sparse Attention (DSA) to deliver world-leading long‑context efficiency with sharply reduced compute and memory costs, and set 1M tokens as the default context across all official services. As reported by DeepSeek’s official announcement on X, the structural innovations target lower latency and lower total cost of ownership for long-context workloads such as multi-document RAG, long-form codebases, and enterprise archives. According to the same source, the move standardizes million-token windows for production, creating business opportunities for enterprises to consolidate retrieval, summarization, and compliance audit pipelines into a single pass, potentially cutting inference spend and hardware footprint.

According to DeepSeek on Twitter, DeepSeek V4 Pro achieves state-of-the-art results on agentic coding benchmarks among open-source models, indicating stronger autonomous tool-use and multi-step planning capabilities for software development workflows (source: DeepSeek). According to DeepSeek, the model leads all current open models in broad world knowledge and trails only Gemini 3.1 Pro among closed systems, suggesting competitive performance for enterprise search, RAG augmentation, and domain QA use cases (source: DeepSeek). As reported by DeepSeek, V4 Pro surpasses all current open models in math, STEM, and coding reasoning, rivaling top closed-source systems, which signals opportunities for code generation, unit test synthesis, and data engineering pipelines where deterministic reasoning is critical (source: DeepSeek).

According to DeepSeek on X (Twitter), the DeepSeek-V4 Preview is live and open-sourced, featuring a cost-effective 1M context window and two Mixture-of-Experts variants: DeepSeek-V4-Pro with 1.6T total parameters and 49B active parameters, and DeepSeek-V4-Flash with 284B total and 13B active parameters. As reported by DeepSeek, the Pro model claims performance rivaling leading closed-source systems, signaling enterprise opportunities for long-context RAG, codebases, and multimodal workflows that rely on extended context efficiency. According to DeepSeek, the Flash variant targets low-latency, cost-sensitive use cases while preserving long-context utility, which can reduce inference costs for production chat, customer support, and agentic pipelines. As stated by DeepSeek, open-sourcing the preview lowers vendor lock-in risks and enables on-prem and sovereign deployments, creating business advantages for regulated industries and data-sensitive workloads.

According to @deepseek_ai, the DeepSeek API now supports the new deepseek-v4-pro and deepseek-v4-flash models with 1M context windows and dual Thinking and Non-Thinking modes, while maintaining the same base_url for quick migration. As reported by DeepSeek on X, the API is compatible with OpenAI ChatCompletions and Anthropic-style endpoints, enabling drop-in integration for existing toolchains and faster time to production. According to DeepSeek, deepseek-chat and deepseek-reasoner will be fully retired and inaccessible after July 24, 2026, 15:59 UTC, and are currently routed to deepseek-v4-flash in both modes, signaling an urgent deprecation timeline for enterprises to update model names in configuration. For AI builders, the 1M context plus dual modes unlock long-context retrieval, multi-document analysis, and chain-of-thought optionality with governance control, while API parity with leading ecosystems reduces vendor lock-in and integration overhead, as stated by DeepSeek’s official announcement.