58 summarised stories about DeepSeek, each linking back to the original source. Browse all topics →
Fortune 16 ● 4 sources
DeepSeek increased the prices for its flagship V4 AI models via a new peak/off-peak schedule. Peak-hour output-token pricing for the DeepSeek-V4-Flash model rises to $1.32 per 1 million tokens (from $0.28), effective Aug. 16. The change shifts token costs toward higher-demand periods to push usage into off-peak hours and reduce congestion.
The New Stack 2 months ago 53 ● 2 sources
DeepSeek open sourced DeepSeek Harness, a Node.js agent runtime that treats the model adapter, tool registry, session log, and agent loop as interchangeable plugins. Within a few hours of going live on GitHub, the repository received more than 33,000 stars. Developers can now build and extend agents by mounting plugins rather than patching a privileged core, with features driven by an append-only session event log.
Trending Topics 2 months ago 5 ● 4 sources
DeepSeek released the generally available version of its top model, DeepSeek-V4-Pro-0813, with agent improvements and API format support. It costs about 6 US cents per completed task, based on Artificial Analysis’s Intelligence Index. DeepSeek also revised its API pricing to use peak/off-peak rates starting 16 August 2026 16:00 UTC, with off-peak charged at half the regular rate, while the model’s performance still trails Kimi K3 on the index.
ChinaTalk 2 months ago 4
DeepSeek CEO Liang Wenfeng’s leaked late-July meeting minutes laid out his thesis that progress toward generalized intelligence depends on automated learning, while positioning DeepSeek’s open model releases as a business built on enterprise API demand. DeepSeek’s team estimated that on an average day in 2025, R1’s API generated US$562,027. The article argues this leads to a strategy focused on compute-supported “main quest” research and a management approach that limits mandatory work and relies on domestic hardware ties instead of consumer-first product expansion.
The Batch 43 ● 4 sources
DeepSeek released DeepSeek-V4-Flash-0731, a fine-tuned small model that overtook its own DeepSeek-V4-Pro on independent tests. It scored 50 points on Artificial Analysis’ Intelligence Index (max reasoning), up from 44 for DeepSeek-V4-Pro. The update improves agentic performance and lowers per-task costs through the unchanged V4-Flash architecture with new fine-tuning, with weights available under the MIT license and API prices starting at $0.14 per 1 million input tokens.
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15 October 2026
Meta used today’s open-weight model drop to make a point: “frontier” doesn’t have to stay in the datacenter. Muse Spark Open ships as an 816B multimodal model with a 1 million–token context window, paired with Muse Code for coding and agent-style workflows—and it comes with strings. The license is free for use and fine-tuning, but it adds revenue-linked conditions (including advance notice above $20 million) and asks developers to display “Built with Muse Spark Open.” Running locally is a hardware project in itself: Meta says you need about 1TB of RAM. Still, the rollout is broad—Meta is making it available via multiple API platforms for desktop-focused tasks, and now through Amazon SageMaker JumpStart for deploy-to-your-own-AWS-account use, including BF16 and a 4-bit NVFP4 variant.
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