Ovms 2025 4

docs/articles_en/about-openvino/performance-benchmarks.rst

@@ -158,9 +158,9 @@ For a listing of all platforms and configurations used for testing, refer to the
 **Disclaimers**
 
 * System configurations used for Intel® Distribution of OpenVINO™ toolkit performance results
-  are based on release 2025.3, as of September 3rd, 2025.
+  are based on release 2025.4, as of December 1st, 2025.
 
-* OpenVINO Model Server performance results are based on release 2025.3, as of September 3rd, 2025.
+* OpenVINO Model Server performance results are based on release 2025.4, as of December 1st, 2025.
 
 The results may not reflect all publicly available updates. Intel technologies' features and
 benefits depend on system configuration and may require enabled hardware, software, or service

docs/articles_en/about-openvino/performance-benchmarks/model-accuracy-int8-fp32.rst

@@ -41,7 +41,7 @@ the table for more information.
    * - mobilenet-v2
      - ImageNet2012
      - accuracy @ top1
-     - -0.93%
+     - -0.91%
      - -0.93%
      - -0.91%
      - -1.03%
@@ -96,28 +96,28 @@ the table for more information.
      - 0.00%
      - 0.00%
      - 0.02%
-     - 0.01%
+     - 0.02%
    * - resnet-50
      - ImageNet2012
      - accuracy @ top1
      - 0.00%
      - 0.00%
      - 0.00%
-     - -0.04%
+     - -0.01%
    * - ssd-resnet34-1200
      - COCO2017_detection_80cl_bkgr
      - map
      - 0.02%
      - 0.02%
      - 0.02%
-     - 0.06%
+     - -0.23%
    * - yolo_v11
      - COCO2017_detection_80cl
      - AP@0.5:0.05:0.95
-     - 0.00%
-     - 0.00%
-     - 0.00%
-     - 
+     - 0.03%
+     - -2.21%
+     - -2.21%
+     - -2.21%
 .. list-table:: Model Accuracy for AMX-FP16, AMX-INT4, Arc-FP16 and Arc-INT4 (Arc™ B-series)
    :header-rows: 1
 
@@ -134,69 +134,62 @@ the table for more information.
      - 98.1%
      - 94.4%
      - 99.5%
-     - 92.6%
+     - 94.0%
    * - DeepSeek-R1-Distill-Qwen-1.5B
      - Data Default WWB
      - Similarity
      - 96.5%
      - 92.4%
      - 99.7%
-     - 92.1%
-   * - Gemma-3-1B-it
+     - 92.3%
+   * - Gemma-3-4B-it
      - Data Default WWB
      - Similarity
-     - 97.3%
      - 92.0%
-     - 99.2%
-     - 91.5%
-   * - GLM4-9B-Chat
-     - Data Default WWB
-     - Similarity
-     - 98.8%
-     - 93.3%
-     - %
-     - 95.0%
+     - 83.9%
+     - 
+     - 84.9%
    * - Llama-2-7B-chat
      - Data Default WWB
      - Similarity
      - 99.3%
      - 93.4%
      - 99.8%
-     - 91.9%
+     - 93.4%
    * - Llama-3-8B
      - Data Default WWB
      - Similarity
      - 98.8%
      - 94.3%
-     - %
+     - 99.7%
      - 94.5%
    * - Llama-3.2-3b-instruct
      - Data Default WWB
      - Similarity
-     - 98.2%
-     - 93.2%
-     - 98.4%
-     - 94.0%
-   * - Mistral-7b-instruct-V0.3
-     - Data Default WWB
-     - Similarity
-     - 98.3%
-     - 92.8%
-     - 99.9%
-     - 93.6%
+     - 97.9%
+     - 94.2%
+     - 99.7%
+     - 94.1%
    * - Phi4-mini-instruct
      - Data Default WWB
      - Similarity
-     - 96.4%
-     - 92.0%
-     - 99.3%
-     - 91.7%
+     - 89.1%
+     - 92.1%
+     - 99.5%
+     - 92.4%
    * - Qwen2-VL-7B
      - Data Default WWB
      - Similarity
-     - 97.8%
-     - 92.4%
+     - 97.5%
+     - 88.1%
      - 99.8%
+     - 91.4%
+   * - Qwen3-8B
+     - Data Default WWB
+     - Similarity
+     - 97.8%
+     - 92.3%
+     - 
      - 93.0%
    * - Flux.1-schnell
      - Data Default WWB
@@ -208,10 +201,10 @@ the table for more information.
    * - Stable-Diffusion-V1-5
      - Data Default WWB
      - Similarity
-     - 97.3%
-     - 95.1%
+     - 96.3%
+     - 93.3%
      - 99.5%
-     - 91.5%
+     - 93.7%
 
 Notes: For all accuracy metrics a "-", (minus sign), indicates an accuracy drop.
 The Similarity metric is the distance from "perfect" and as such always positive. 

docs/articles_en/about-openvino/performance-benchmarks/performance-benchmarks-faq.rst

@@ -55,11 +55,7 @@ Performance Information F.A.Q.
         - DeepSeek, HF
         - Auto regressive language
         - 128K
-      * - `GLM4-9B-chat <https://huggingface.co/THUDM/glm-4-9b-chat/tree/main>`__
-        - THUDM
-        - Transformer
-        - 128K
-      * - `Gemma-3-1B-it <https://huggingface.co/google/gemma-3-1b-it>`__
+      * - `Gemma-3-4B-it <https://huggingface.co/google/gemma-3-4b-it>`__
         - Hugginface
         - Text-To-Text Decoder-only
         - 128K
@@ -75,16 +71,8 @@ Performance Information F.A.Q.
         - Meta AI
         - Auto regressive language
         - 128K
-      * - `Mistral-7b-Instruct-V0.3 <https://huggingface.co/mistralai/Mistral-7B-v0.3>`__
-        - Mistral AI
-        - Auto regressive language
-        - 32K
-      * - `Phi3-4k-mini-Instruct <https://huggingface.co/microsoft/Phi-3-mini-4k-instruct>`__
-        - Huggingface
-        - Auto regressive language
-        - 4096
       * - `Phi4-mini-Instruct <https://huggingface.co/microsoft/Phi-4-mini-instruct>`__
-        - Huggingface
+        - Hugginface
         - Auto regressive language
         - 4096
       * - `Qwen-2-VL-7B-instruct <https://huggingface.co/Qwen/Qwen2-VL-7B-instruct>`__
@@ -95,7 +83,7 @@ Performance Information F.A.Q.
         - Huggingface
         - Auto regressive language
         - 32K
-      * - `Stable-Diffusion-V1-5 <https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5>`__
+     * - `Stable-Diffusion-V1-5 <https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5>`__
         - Hugginface
         - Latent Diffusion Model
         - 77