openvinotoolkit/openvino 2026.4.0

on GitHub

Summary of major features and improvements  

• More GenAI coverage and framework integrations to minimize code changes

  • New models supported:
    • On CPU: Gemma-3n
    • On CPU, GPU: Kokoro-82M, Qwen3-VL-4B with EAGLE-3, Qwen3-ASR, Muse Glimmer 30B, Qwen3.8 27B, Gemma 4 12B, Hy-MT2-1.8B, DeepSeek OCR-2, and Granite 4.0 H Micro
    • On NPUs: FLUX.2-Klein 4B and Kokoro-82M
  • Additional CPU and GPU-enabled models available as early releases: Qwen-Image, Z-Image-Turbo, Granite 4.0 H Tiny, Fun-ASR-Nano, LFM2.5-8B-A1B, MiniCPM5-2B, RF-DETR, BGE Reranker-V2-M3, and BGE M3

• Broader LLM model support and more model compression techniques

  • OpenVINO™ GenAI adds Multi-Token Prediction (MTP) speculative decoding for Gemma 4, Qwen3.5, and Qwen3.6 on CPUs and GPUs, enabling higher throughput and lower latency without sacrificing accuracy.
  • Preview: OpenVINO™ GenAI introduces DFlash acceleration for Qwen models on GPUs, and visual-token support to reduce latency and speed up GenAI pipelines on Intel® Core™ Ultra Series 3 processors.
  • With Tree Drafting (Top-K) for EAGLE-3 now supported in OpenVINO™ GenAI, developers can unlock higher throughput on VLM pipelines compared to Chain Drafting (Top-1).
  • Xe3 integrated graphics optimizations in OpenVINO™ GenAI improve AI inference performance for Gemma 4 models processing long-context inputs on Intel® Core™ Ultra Series 3 processors.
  • OpenVINO extends Instrumentation and Tracing Technology (ITT) profiling support to the NPU, enabling developers to use Intel® VTune™ Profiler to analyze CPU, GPU, and NPU execution through one consistent toolchain.

• More portability and performance to run AI at the edge, in the cloud, or locally.

  • OpenVINO™ GenAI introduces support for ASRPipeline in Node.js, enabling JavaScript developers to run automatic speech recognition (such as Whisper and Qwen3-ASR) with streaming and performance metrics using a pipeline API similar to C++ and Python.
  • Preview: Bounded dynamic-shape support on NPUs now extends to vision workloads such as image super-resolution. This capability has been validated with the ESPCN model.
  • Preview: OpenVINO™ Model Server now includes preview support for idle model management, which unloads models when they are not in use to reduce memory usage.
  • OpenVINO™ Model Server adds support for new agentic models such as Muse Glimmer 30B and Qwen3.8 27B.

Support Change and Deprecation Notices

• Discontinued in 2026.0:

  • The deprecated openvino.runtime namespace has been removed. Please use the openvino namespace directly.
  • The deprecated openvino.Type.undefined has been removed. Please use openvino.Type.dynamic instead.
  • Support for Debian 10 has been discontinued due to the end of its standard support.
  • The PostponedConstant constructor signature has been updated for improved usability:
    • Old (removed): Callable[[Tensor], None]
    • New: Callable[[], Tensor]
  • The deprecated OpenVINO™ GenAI predefined generation configs were removed.
  • The deprecated OpenVINO GenAI support for whisper stateless decoder model has been removed. Please use a stateful model.
  • The deprecated OpenVINO GenAI StreamerBase put method, bool return type for callbacks, and ChunkStreamer class has been removed.
  • NNCF create_compressed_model() method is now deprecated and removed in 2026. Please use nncf.prune() method for unstructured pruning and nncf.quantize() for INT8 quantization.
  • NNCF optimization methods for TensorFlow models and TensorFlow backend in NNCF are deprecated and removed in 2026. It is recommended to use PyTorch analogous models for training-aware optimization methods and OpenVINO™ IR, PyTorch, and ONNX models for post-training optimization methods from NNCF.
  • The following experimental NNCF methods are deprecated and removed: NAS, Structural Pruning, AutoML, Knowledge Distillation, Mixed-Precision Quantization, Movement Sparsity.
  • CPU plugin now requires support for the AVX2 instruction set as a minimum system requirement. The SSE instruction set will no longer be supported.
  • Dropped support for the TensorFlow Serving (TFS) API in OpenVINO Model Server 2026.3. KServe API is recommended for classic model deployments.
  • Support for Stateful Models was removed in OpenVINO Model Server 2026.3. These capabilities were originally introduced for Kaldi audio models which are no longer applicable. Current audio model support relies on the OpenAI API and pipelines implemented with the OpenVINO™ GenAI library.
  • Support for Python 3.10 will be discontinued in OpenVINO 2026.5, due to its end-of-life (EOL) status.

• Deprecated and to be removed in the future:

  • auto shape and auto batch size (reshaping a model in runtime) will be removed in the future. OpenVINO’s dynamic shape models are recommended instead.
  • Starting with 2026.0 release major internal refactoring of the graph iteration mechanism has been implemented for improved performance and maintainability. The legacy path can be enabled by setting the ONNX_ITERATOR=0 environment variable. This legacy path is deprecated and will be removed in future releases.
  • Modifying runtime information (RTMap) through read-only ConstOutput objects in the Python API is deprecated and will be removed in a 2027.0 release. Users should use non-const outputs when updating runtime metadata; attempts to modify RTMap through ConstOutput now emit a deprecation warning to ease migration.
  • OpenVINO Model Server:
    • The dedicated OpenVINO operator for Kubernetes and OpenShift is now deprecated in favor of the recommended KServe operator. The OpenVINO operator will remain functional in upcoming OpenVINO™ Model Server releases but will no longer be actively developed. Since KServe provides broader capabilities, no loss of functionality is expected. On the contrary, more functionalities will be accessible and migration between other serving solutions and OpenVINO Model Server will be much easier.
    • Directed Acyclic Graph Scheduler will be deprecated in favor of pipelines managed by MediaPipe scheduler and will be removed in 2026.3. That approach gives more flexibility, includes wider range of calculators and has support for using processing accelerators.
    • OpenVINO™ GenAI:
      • start_chat() / finish_chat() APIs are deprecated and will be removed in a future major release. Pass a ChatHistory object directly to generate() instead.

You can find OpenVINO™ toolkit 2026.4 release here:

• Download archives* with OpenVINO™
• OpenVINO™ for Python: pip install openvino==2026.4.0

Release documentation is available here: https://docs.openvino.ai/2026
Release Notes are available here: https://docs.openvino.ai/2026/about-openvino/release-notes-openvino.html