New models and versions, open weights, and changes in capability and pricing, with their evaluations.
Epoch AI estimates that open-weight cyber capabilities are about 6–7 months behind the closed frontier. Zai’s GLM-5.3 is the highest-scoring open-weight model on its Cyber ECI, scoring 160 and beating OpenAI’s GPT-5.4, released in March. https://t.co/F0D6nSnM69
Mistral AI released Timestral in four sizes, from 5M to 209M parameters, for probabilistic, multivariate forecasting with past-only and known-future covariates. Mistral reports that its 209M model ranks first among pretrained, zero-shot models across five metrics on GIFT-Eval, TIME and fev-bench, using a single checkpoint without ensembling. All sizes support CPU inference, and Mistral published a GitHub repository for running Timestral on users’ data; domain adaptation and fine-tuning are available through Mistral Forge.
Why it matters: The comparisons across model sizes and CPU latency provide a concrete basis for assessing forecasting accuracy against compute requirements in production deployments.
NVIDIA AI congratulates StepFun on Step 5 Preview, calling it StepFun’s largest open weights release. NVIDIA says the 600B MoE model supports agent tasks lasting up to 24 hours and runs at nearly 8K tokens/sec/GPU on NVIDIA GB300 using NVFP4. StepFun’s quoted announcement lists 27B active parameters per token, a 1M-token context window, native text and vision input, and BF16 and FP8 weights on Hugging Face and ModelScope. NVP4 checkpoint: https://t.co/lSECChAwlW Deploy with https://t.co/GO1wqTSBOk https://t.co/28TBzUo6ZU
Why it matters: The reported throughput ties Step 5 Preview to a specific GPU and precision format, giving readers concrete deployment context for its long running agent workloads.
Leiphone compared HiDream-O1-Video-1.0, Seedance 2.5, and MiniMax H3 on three original image-to-video tasks, evaluating intent planning, temporal causality, and audio-video alignment.
Xiaomi has released the MiMo-V2.6 family, including the multimodal MiMo-V2.6-Pro and MiMo-V2.6-Flash, and introduced MiMo-V2.6-Pro-UltraSpeed, which delivers up to 20× faster output.
Why it matters: Xiaomi's first entry in the frontier open-model lineup, with open RL environments and training recipes, provides a concrete example of post-training cost strategies.
DeepSeek has released DeepSeek-V4.1-Flash, describing it as smarter, faster, and more efficient. It is the smallest model in a new architecture family, with native visual understanding, and is designed for stronger capabilities, faster inference, and higher throughput, with the architecture able to scale to larger models.
Why it matters: As the smallest model in DeepSeek's new architecture family, its native visual understanding and higher inference throughput offer a view of its lightweight multimodal positioning.
DeepSeek has launched the experimental multimodal model DeepSeek-V4-Flash-Vision-Exp on its API platform. Its text capabilities match DeepSeek-V4-Flash across agents, reasoning, and world knowledge.
Why it matters: The official release gives the new model's text-capability alignment with V4-Flash and improvements on multimodal-agent benchmarks, helping readers assess its readiness for multimodal agents.
DeepSeek has released DeepSeek-V4-Pro, highlighting upgraded agent capabilities and notable production gains. V4-Pro and V4-Flash offer flexible reasoning levels: low for simple tasks, high for everyday agent workflows, and max for complex tasks. The model natively supports the OpenAI Responses API and is optimized for Codex with one-click configuration. It is now available in the app, on the web in Expert Mode, and through the API; its model name is unchanged.
Why it matters: The official announcement describes V4-Pro's agent upgrades and three reasoning levels, helping readers understand its position in production workflows.
DeepSeek says its official DeepSeek-V4-Flash API is now in public beta. The company reports a major upgrade in agent capabilities, with benchmark scores well above V4-Pro-Preview. It natively supports the Responses API format and is fully adapted for Codex; setup details are in the official API documentation.
Why it matters: DeepSeek has announced the V4-Flash API beta and improved agent capabilities, offering a basis for assessing integration with Codex.
