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One of the defining features is its native long-context capability, with a maximum length of 512,000 tokens, 2X OpenAI’s GPT-5 family.

China’s DeepSeek has released a 685-billion parameter open-source AI model, DeepSeek V3.1, challenging OpenAI and Anthropic with breakthrough performance, hybrid reasoning, and zero-cost access on Hugging Face.

Researchers from Inclusion AI and Ant Group proposed a new LLM leaderboard that takes its data from real, in-production apps.

Moving beyond the slow, costly trial-and-error of RL, GEPA teaches AI systems to learn and improve using natural language.

Ultimately, model makers and enterprises are focusing on the wrong issue: They should be computing smarter, not harder.

The future will arrive with or without our guardrails. We must design AI’s structures now for a future of abundance rather than disruption.

TensorZero raises $7.3 million to build an open-source AI infrastructure stack that helps enterprises scale and optimize large language model (LLM) applications with unified tools for observability, fine-tuning, and experimentation.

New research reveals open-source AI models use up to 10 times more computing resources than closed alternatives, potentially negating cost advantages for enterprise deployments.

Morris found it could also reproduce verbatim passages from copyrighted works, including three out of six book excerpts he tried.

For enterprise teams and commercial developers, this means the model can be embedded in products or fine-tuned.