Model Accuracy

The following two tables present the absolute accuracy drop calculated as the accuracy difference between OV-accuracy and the original frame work accuracy for FP32, and the same for INT8, BF16 and FP16 representations of a model on three platform architectures (percent point). The third table presents the GenAI model accuracies as absolute accuracy values. Please also refer to notes below the table for more information.

• A - Intel® Core™ Ultra 9-185H (AVX2), INT8 and FP32
• B - Intel® Xeon® 6338, (VNNI), INT8 and FP32
• C - Intel® Xeon 6972P (VNNI, AMX), INT8, BF16, FP32
• D - Intel® Arc-B60, INT8 and FP16

.. list-table:: Model Accuracy for INT8 :header-rows: 1

• • OpenVINO™ Model name
  • dataset
  • Metric Name
  • A, INT8
  • B, INT8
  • C, INT8
  • D, INT8
• • bert-base-cased
  • SST-2_bert_cased_padded
  • spearman@cosine
  • 2.57%
  • 2.65%
  • 2.95%
  • 2.70%
• • mobilenet-v2
  • ImageNet2012
  • accuracy @ top1
  • -0.91%
  • -0.91%
  • -1.07%
  • -1.07%
• • resnet-50
  • ImageNet2012
  • accuracy @ top1
  • -0.12%
  • -0.12%
  • -0.15%
  • -0.17%
• • ssd-resnet34-1200
  • COCO2017_detection_80cl_bkgr
  • map
  • 0.00%
  • 0.00%
  • 0.07%
  • 0.06%
• • yolov26n
  • COCO2017_detection_80cl_bkgr
  • map
  • -0.53%
  • -0.50%
  • -0.47%
  • -0.51%

.. list-table:: Model Accuracy for BF16, FP32 and FP16 (FP16: Arc only. BF16: Xeon® 6972P only) :header-rows: 1

• • OpenVINO™ Model name
  • dataset
  • Metric Name
  • A, FP32
  • B, FP32
  • C, FP32
  • D, FP16
• • bert-base-cased
  • SST-2_bert_cased_padded
  • spearman@cosine
  • 0.00%
  • 0.00%
  • 0.00%
  • 0.00%
• • mobilenet-v2
  • ImageNet2012
  • accuracy @ top1
  • -0.00%
  • -0.00%
  • -0.00%
  • -0.00%
• • resnet-50
  • ImageNet2012
  • accuracy @ top1
  • 0.00%
  • 0.00%
  • 0.00%
  • 0.00%
• • ssd-resnet34-1200
  • COCO2017_detection_80cl_bkgr
  • map
  • 0.02%
  • 0.02%
  • 0.02%
  • 0.02%
• • yolo_v11
  • COCO2017_detection_80cl
  • [email protected]:0.05:0.95
  • -0.03%
  • -2.21%
  • -2.21%
  • -2.21%
• • yolo_v26
  • COCO2017_detection_80cl
  • [email protected]:0.05:0.95
  • 0.00%
  • 0.00%
  • 0.00%
  • 0.00% .. list-table:: Model Accuracy for AMX-FP16, AMX-INT4, Arc-FP16 and Arc-INT4 (Arc™ B-series) :header-rows: 1
• • OpenVINO™ Model name
  • dataset
  • Metric Name
  • A, AMX-FP16
  • B, AMX-INT4
  • C, Arc-FP16
  • D, Arc-INT4
• • DeepSeek-R1-Distill-Llama-8B
  • Data Default WWB
  • Similarity
  • 97.9%
  • 91.2%
  • 99.8%
  • 94.9%
• • GPT-OSS-20B
  • Data Default WWB
  • Similarity
  • 98.8%
  • 93.3%
  • 91.1%
  • 94.5%
• • GPT-OSS-120B
  • Data Default WWB
  • Similarity
  • 93.4%
  • 93.4%
  • 94.8%
• • Llama-3.2-3b-instruct
  • Data Default WWB
  • Similarity
  • 98.3%
  • 91.9%
  • 99.8%
  • 93.4%
• • MiniCPM-V-2.6
  • Data Default WWB
  • Similarity
  • 94.2%
  • 90.8%
  • 95.1%
  • 90.5%
• • Mistral-7B-instruct
  • Data Default WWB
  • Similarity
  • 98.5%
  • 92.3%
  • 98.5%
  • 92.3%
• • Phi4-mini-instruct
  • Data Default WWB
  • Similarity
  • 97.1%
  • 96.0%
  • 98.0%
  • 95.1%
• • Qwen3.5-9B
  • Data Default WWB
  • Similarity
  • 97.3%
  • 89.8%
  • 98.2%
  • 88.7%
• • Qwen3-30B-A3B
  • Data Default WWB
  • Similarity
  • 97.7%
  • 94.0%
  • 99.5%
  • 94.8%
• • Qwen3.6-27B
  • Data Default WWB
  • Similarity
  • 96.5%
  • 94.4%
  • 94.7%
• • Qwen3.6-35B-A3B
  • Data Default WWB
  • Similarity
  • 95.2%
• • Flux.1-schnell
  • Data Default WWB
  • Similarity
  • 95.5%
  • 95.9%
  • 96.2%
• • Stable-Diffusion-V1-5
  • Data Default WWB
  • Similarity
  • 97.1%
  • 94.9%
  • 94.3%
  • 99.4%
• • LTX-VIDEO
  • Data Default WWB
  • Similarity
  • 64.1%
  • 57.6%

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. Similarity is cosine similarity - the dot product of two vectors divided by the product of their lengths.

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