Updated External Benchmarking details for OV-2025.4 (#33029)

Updated landing page with new date and release number. Updated brief
system description with new machines (PDF) Updated detailed system
description with new machines (XLSX) Updated FAQ file with new models.
Updated Accuracy table to reflect new number and new models.

### Details:
 - *item1*
 - *...*

### Tickets:
 - *ticket-id*

---------

Co-authored-by: Tatiana Savina <[email protected]>

Author: mfhansen

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

@@ -158,7 +158,7 @@ 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.
 

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
      - [email protected]: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
@@ -75,14 +71,6 @@ 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
         - Auto regressive language

docs/sphinx_setup/_static/download/benchmarking_OV_platform_list.pdf

@@ -1,3 +1,3 @@
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+size 61867