vLLM

Google released Multi-Token Prediction drafters for Gemma 4 and says decoding can run up to 3x faster without output-quality loss. vLLM and SGLang support shipped day one, so local and server deployments can try the speedup immediately.

The vLLM team shipped more than 10 DeepSeek V4 fixes as developers kept posting V4 Pro and Flash results from coding harnesses and local servers. Use the update if serving bugs, cache behavior, or tool-call reliability are blocking cheaper long-context agent runs.

NVIDIA opened Nemotron 3 Nano Omni, a 30B-A3B model for text, image, audio, and video, with day-one serving support. That lets teams run one open model for perception-heavy agents instead of stitching separate components.

Poolside opened Laguna M.1 and Laguna XS.2 as its first public coding models, with Apache 2.0 weights and same-day provider support. That gives teams open coding models that can run locally or through standard serving stacks.

Xiaomi opened MiMo-V2.5 and MiMo-V2.5-Pro under MIT, adding a 1M-context multimodal agent model and a 42B-active Pro variant. SGLang and vLLM published day-one recipes, making the series immediately deployable.

vLLM 0.20.0 shipped a new CUDA 13 / PyTorch 2.11 / Transformers v5 baseline, TurboQuant 2-bit KV cache, FA4 MLA defaults, and deeper DeepSeek V4 support. The release changes serving baselines across NVIDIA, AMD, Intel, and ARM-CUDA setups, including 4x KV capacity and a clearer upgrade path for teams already running V4.

DeepSeek lowered V4-Pro API pricing and updated integration guidance for Claude Code, OpenCode, and OpenClaw a day after V4 launched. Check whether V4-Flash is the easier deploy today, while Pro stays heavier and more rate-limited.

Within a day of launch, vLLM, SGLang, Ollama cloud, OpenCode, Venice, Together, and Baseten added support or hosted access for DeepSeek V4. That makes Flash and Pro easier to test across local, routed, and managed agent stacks.

DeepSeek open-sourced V4-Pro and V4-Flash under MIT, with 1M context and aggressive Flash pricing. Day-one support in SGLang, vLLM, and OpenRouter pushes open-weight agentic coding closer to closed frontier models.

Tencent open-sourced Hy3 preview, a 295B MoE with 21B active parameters and 256K context, then pushed it into OpenRouter, OpenCode, OpenClaw, vLLM, and SGLang immediately. That matters because engineers can test and deploy a new reasoning-agent model on day one instead of waiting for the runtime ecosystem to catch up.

Alibaba released Qwen3.6-27B, a dense open model with multimodal input and thinking or non-thinking modes that beats Qwen3.5-397B-A17B across major coding benchmarks. Day-one support across vLLM, SGLang, Ollama, llama.cpp, GGUF, and MLX makes it ready for local and hosted coding agents.

Kimi K2.6 shipped across vLLM, SGLang, OpenRouter, Baseten, Ollama, OpenCode, Hermes Agent, and Droid within hours of launch. That cuts the usual lag between model release and production trials, so mixed-provider agent stacks can test it sooner.

Alibaba open-sourced Qwen3.6-35B-A3B, a 35B multimodal sparse MoE with only 3B active parameters under Apache 2.0. Same-day support from vLLM, Ollama, SGLang, and GGUF builders makes it immediately usable for local and production coding workloads.

MiniMax M2.7 moved from announcement to deployment, with GGUF guidance for 128 GB local systems and same-day availability on Together, Fireworks, Hugging Face, and ModelScope. Use the local and managed serving options now, but check the non-commercial license before adopting the 230B model.

MiniMax open-sourced M2.7 and published coding and agent benchmark claims including 56.22% SWE-Pro and 57.0% Terminal Bench 2. Day-zero support from SGLang, vLLM, Ollama Cloud, Together AI, and NVIDIA NIM makes it easy to try on common serving stacks.

Z.ai released GLM-5.1, a 744B open model built for long-horizon agentic coding and ranked first among open systems on SWE-Bench Pro. Day-0 support in OpenRouter, Ollama, SGLang, vLLM, OpenCode, and local quantization paths makes it ready to test in existing stacks.

Mistral released open-weight Voxtral TTS with low-latency streaming, voice cloning, and cross-lingual adaptation, and vLLM Omni shipped day-0 support. Voice-agent teams should compare quality, latency, and serving cost against closed APIs.

Cohere released a 2B speech-to-text model with 14 languages and top Open ASR scores, and upstreamed encoder-decoder optimizations to vLLM in the same launch. It is a self-hosted ASR option, so test accuracy and throughput on your own speech workload.