Apple M1 Pro (10-CPU 14-GPU) vs Google Tensor G3

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CPU comparison with benchmarks


Apple M1 Pro (10-CPU 14-GPU) CPU1 vs CPU2 Google Tensor G3
Apple M1 Pro (10-CPU 14-GPU) Google Tensor G3

CPU comparison

Apple M1 Pro (10-CPU 14-GPU) or Google Tensor G3 - which processor is faster? In this comparison we look at the differences and analyze which of these two CPUs is better. We compare the technical data and benchmark results.

The Apple M1 Pro (10-CPU 14-GPU) has 10 cores with 10 threads and clocks with a maximum frequency of 3.20 GHz. Up to 32 GB of memory is supported in 2 memory channels. The Apple M1 Pro (10-CPU 14-GPU) was released in Q3/2021.

The Google Tensor G3 has 8 cores with 8 threads and clocks with a maximum frequency of 2.91 GHz. The CPU supports up to 12 GB of memory in 2 memory channels. The Google Tensor G3 was released in Q3/2023.
Apple M series (25) Family Google Tensor (3)
Apple M1 (9) CPU group Google Tensor G3 (1)
1 Generation 3
M1 Architecture G3
Mobile Segment Mobile
-- Predecessor Google Tensor
Apple M2 Pro (10-CPU 16-GPU) Successor --

CPU Cores and Base Frequency

The Apple M1 Pro (10-CPU 14-GPU) has 10 CPU cores and can calculate 10 threads in parallel. The clock frequency of the Apple M1 Pro (10-CPU 14-GPU) is 0.60 GHz (3.20 GHz) while the Google Tensor G3 has 8 CPU cores and 8 threads can calculate simultaneously. The clock frequency of the Google Tensor G3 is at 2.91 GHz.

Apple M1 Pro (10-CPU 14-GPU) Characteristic Google Tensor G3
10 Cores 8
10 Threads 8
hybrid (big.LITTLE) Core architecture hybrid (Prime / big.LITTLE)
No Hyperthreading No
No Overclocking ? No
0.60 GHz (3.20 GHz)
8x Firestorm
A-Core 2.91 GHz
1x Cortex-X3
0.60 GHz (2.06 GHz)
2x Icestorm
B-Core 2.37 GHz
4x Cortex-A715
-- C-Core 1.70 GHz
4x Cortex-A510

Artificial Intelligence and Machine Learning

Processors with the support of artificial intelligence (AI) and machine learning (ML) can process many calculations, especially audio, image and video processing, much faster than classic processors. Algorithms for ML improve their performance the more data they have collected via software. ML tasks can be processed up to 10,000 times faster than with a classic processor.

Apple M1 Pro (10-CPU 14-GPU) Characteristic Google Tensor G3
Apple Neural Engine AI hardware Google Tensor AI
16 Neural cores @ 11 TOPS AI specifications Google Edge TPU

Internal Graphics

The Apple M1 Pro (10-CPU 14-GPU) or Google Tensor G3 has integrated graphics, called iGPU for short. The iGPU uses the system's main memory as graphics memory and sits on the processor's die.

Apple M1 Pro (14 Core) GPU ARM Immortalis-G715 MP10
0.39 GHz GPU frequency 0.89 GHz
1.30 GHz GPU (Turbo) --
1 GPU Generation Vallhall
5 nm Technology 4 nm
3 Max. displays 0
224 Compute units 10
1792 Shader --
No Hardware Raytracing No
No Frame Generation No
32 GB Max. GPU Memory --
-- DirectX Version 12

Hardware codec support

A photo or video codec that is accelerated in hardware can greatly accelerate the working speed of a processor and extend the battery life of notebooks or smartphones when playing videos.

Apple M1 Pro (14 Core) GPU ARM Immortalis-G715 MP10
Decode / Encode Codec h265 / HEVC (8 bit) Decode / Encode
Decode / Encode Codec h265 / HEVC (10 bit) Decode / Encode
Decode / Encode Codec h264 Decode / Encode
Decode / Encode Codec VP9 Decode / Encode
Decode Codec VP8 Decode / Encode
No Codec AV1 Decode / Encode
Decode Codec AVC Decode / Encode
Decode Codec VC-1 Decode / Encode
Decode / Encode Codec JPEG Decode / Encode

Memory & PCIe

The Apple M1 Pro (10-CPU 14-GPU) can use up to 32 GB of memory in 2 memory channels. The maximum memory bandwidth is 102.4 GB/s. The Google Tensor G3 supports up to 12 GB of memory in 2 memory channels and achieves a memory bandwidth of up to 53.0 GB/s.

Apple M1 Pro (10-CPU 14-GPU) Characteristic Google Tensor G3
LPDDR5-6400 Memory LPDDR5-5500
32 GB Max. Memory 12 GB
2 (Dual Channel) Memory channels 2 (Dual Channel)
102.4 GB/s Max. Bandwidth 53.0 GB/s
No ECC No
28.00 MB L2 Cache --
-- L3 Cache --
4.0 PCIe version --
-- PCIe lanes --
-- PCIe Bandwidth --

Thermal Management

The thermal design power (TDP for short) of the Apple M1 Pro (10-CPU 14-GPU) is 45 W, while the Google Tensor G3 has a TDP of 10 W. The TDP specifies the necessary cooling solution that is required to cool the processor sufficiently.

Apple M1 Pro (10-CPU 14-GPU) Characteristic Google Tensor G3
45 W TDP (PL1 / PBP) 10 W
-- TDP (PL2) --
-- TDP up --
-- TDP down --
-- Tjunction max. --

Technical details

The Apple M1 Pro (10-CPU 14-GPU) is manufactured in 5 nm and has 28.00 MB cache. The Google Tensor G3 is manufactured in 4 nm and has a 0.00 MB cache.

Apple M1 Pro (10-CPU 14-GPU) Characteristic Google Tensor G3
5 nm Technology 4 nm
Chiplet Chip design Chiplet
Armv8.5-A (64 bit) Instruction set (ISA) Armv9-A (64 bit)
Rosetta 2 x86-Emulation ISA extensions --
-- Socket --
Apple Virtualization Framework Virtualization None
Yes AES-NI No
macOS Operating systems Android
Q3/2021 Release date Q3/2023
-- Release price --
show more data show more data


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Average performance in benchmarks

⌀ Single core performance in 2 CPU benchmarks
Apple M1 Pro (10-CPU 14-GPU) (100%)
Google Tensor G3 (73%)
⌀ Multi core performance in 2 CPU benchmarks
Apple M1 Pro (10-CPU 14-GPU) (100%)
Google Tensor G3 (33%)

Geekbench 5, 64bit (Single-Core)

Geekbench 5 is a cross plattform benchmark that heavily uses the systems memory. A fast memory will push the result a lot. The single-core test only uses one CPU core, the amount of cores or hyperthreading ability doesn't count.
Apple M1 Pro (10-CPU 14-GPU) Apple M1 Pro (10-CPU 14-GPU)
10C 10T @ 3.20 GHz
1768 (100%)
Google Tensor G3 Google Tensor G3
8C 8T @ 2.91 GHz
1267 (72%)

Geekbench 5, 64bit (Multi-Core)

Geekbench 5 is a cross plattform benchmark that heavily uses the systems memory. A fast memory will push the result a lot. The multi-core test involves all CPU cores and taks a big advantage of hyperthreading.
Apple M1 Pro (10-CPU 14-GPU) Apple M1 Pro (10-CPU 14-GPU)
10C 10T @ 3.20 GHz
12574 (100%)
Google Tensor G3 Google Tensor G3
8C 8T @ 2.91 GHz
3631 (29%)

Geekbench 6 (Single-Core)

Geekbench 6 is a benchmark for modern computers, notebooks and smartphones. What is new is an optimized utilization of newer CPU architectures, e.g. based on the big.LITTLE concept and combining CPU cores of different sizes. The single-core benchmark only evaluates the performance of the fastest CPU core, the number of CPU cores in a processor is irrelevant here.
Apple M1 Pro (10-CPU 14-GPU) Apple M1 Pro (10-CPU 14-GPU)
10C 10T @ 3.20 GHz
2397 (100%)
Google Tensor G3 Google Tensor G3
8C 8T @ 2.91 GHz
1759 (73%)

Geekbench 6 (Multi-Core)

Geekbench 6 is a benchmark for modern computers, notebooks and smartphones. What is new is an optimized utilization of newer CPU architectures, e.g. based on the big.LITTLE concept and combining CPU cores of different sizes. The multi-core benchmark evaluates the performance of all of the processor's CPU cores. Virtual thread improvements such as AMD SMT or Intel's Hyper-Threading have a positive impact on the benchmark result.
Apple M1 Pro (10-CPU 14-GPU) Apple M1 Pro (10-CPU 14-GPU)
10C 10T @ 3.20 GHz
12407 (100%)
Google Tensor G3 Google Tensor G3
8C 8T @ 2.91 GHz
4533 (37%)

iGPU - FP32 Performance (Single-precision GFLOPS)

The theoretical computing performance of the internal graphics unit of the processor with simple accuracy (32 bit) in GFLOPS. GFLOPS indicates how many billion floating point operations the iGPU can perform per second.
Apple M1 Pro (10-CPU 14-GPU) Apple M1 Pro (10-CPU 14-GPU)
Apple M1 Pro (14 Core) @ 1.30 GHz
4580 (100%)
Google Tensor G3 Google Tensor G3
ARM Immortalis-G715 MP10 @ 0.89 GHz
1 (0%)

Cinebench 2024 (Single-Core)

The Cinebench 2024 benchmark is based on the Redshift rendering engine, which is also used in Maxon's 3D program Cinema 4D. The benchmark runs are each 10 minutes long to test whether the processor is limited by its heat generation.
Apple M1 Pro (10-CPU 14-GPU) Apple M1 Pro (10-CPU 14-GPU)
10C 10T @ 3.20 GHz
113 (100%)
Google Tensor G3 Google Tensor G3
8C 8T @ 2.91 GHz
0 (0%)

Cinebench 2024 (Multi-Core)

The Multi-Core test of the Cinebench 2024 benchmark uses all cpu cores to render using the Redshift rendering engine, which is also used in Maxons Cinema 4D. The benchmark run is 10 minutes long to test whether the processor is limited by its heat generation.
Apple M1 Pro (10-CPU 14-GPU) Apple M1 Pro (10-CPU 14-GPU)
10C 10T @ 3.20 GHz
802 (100%)
Google Tensor G3 Google Tensor G3
8C 8T @ 2.91 GHz
0 (0%)

Cinebench R23 (Single-Core)

Cinebench R23 is the successor of Cinebench R20 and is also based on the Cinema 4 Suite. Cinema 4 is a worldwide used software to create 3D forms. The single-core test only uses one CPU core, the amount of cores or hyperthreading ability doesn't count.
Apple M1 Pro (10-CPU 14-GPU) Apple M1 Pro (10-CPU 14-GPU)
10C 10T @ 3.20 GHz
1534 (100%)
Google Tensor G3 Google Tensor G3
8C 8T @ 2.91 GHz
0 (0%)

Cinebench R23 (Multi-Core)

Cinebench R23 is the successor of Cinebench R20 and is also based on the Cinema 4 Suite. Cinema 4 is a worldwide used software to create 3D forms. The multi-core test involves all CPU cores and taks a big advantage of hyperthreading.
Apple M1 Pro (10-CPU 14-GPU) Apple M1 Pro (10-CPU 14-GPU)
10C 10T @ 3.20 GHz
12390 (100%)
Google Tensor G3 Google Tensor G3
8C 8T @ 2.91 GHz
0 (0%)

AnTuTu 10 Benchmark

The AnTuTu 10 benchmark is one of the best-known benchmarks for mobile processors, which is now available in version 10. There is a version for Android-based smartphones and tablets, as well as a version for Apple mobile devices, i.e. iPhones and iPads.

The Antutu 10 benchmark has 3 phases. In the first phase, the device's RAM is tested, in phase 2 the graphics are tested and in the final phase the entire device is pushed to its performance limits by rendering 3D graphics.

Antutu 10 is therefore ideal for comparing the performance of different devices.
Apple M1 Pro (10-CPU 14-GPU) Apple M1 Pro (10-CPU 14-GPU)
10C 10T @ 0.60 GHz
0 (0%)
Google Tensor G3 Google Tensor G3
8C 8T @ 2.91 GHz
1106280 (100%)

AnTuTu 9 Benchmark

The AnTuTu 9 benchmark is very well suited to measuring the performance of a smartphone. AnTuTu 9 is quite heavy on 3D graphics and can now also use the "Metal" graphics interface. In AnTuTu, memory and UX (user experience) are also tested by simulating browser and app usage. AnTuTu version 9 can compare any ARM CPU running on Android or iOS. Devices may not be directly comparable when benchmarked on different operating systems.

In the AnTuTu 9 benchmark, the single-core performance of a processor is only slightly weighted. The rating is made up of the multi-core performance of the processor, the speed of the working memory, and the performance of the internal graphics.
Apple M1 Pro (10-CPU 14-GPU) Apple M1 Pro (10-CPU 14-GPU)
10C 10T @ 0.60 GHz
0 (0%)
Google Tensor G3 Google Tensor G3
8C 8T @ 2.91 GHz
894587 (100%)

Blender 3.1 Benchmark

In the Blender Benchmark 3.1, the scenes "monster", "junkshop" and "classroom" are rendered and the time required by the system is measured. In our benchmark we test the CPU and not the graphics card. Blender 3.1 was presented as a standalone version in March 2022.
Apple M1 Pro (10-CPU 14-GPU) Apple M1 Pro (10-CPU 14-GPU)
10C 10T @ 3.20 GHz
192 (100%)
Google Tensor G3 Google Tensor G3
8C 8T @ 2.91 GHz
0 (0%)

CPU performance per watt (efficiency)

Efficiency of the processor under full load in the Cinebench R23 (multi-core) benchmark. The benchmark result is divided by the average energy required (CPU package power in watts). The higher the value, the more efficient the CPU is under full load.
Apple M1 Pro (10-CPU 14-GPU) Apple M1 Pro (10-CPU 14-GPU)
12,390 CB R23 MC @ 45 W
275 (100%)
Google Tensor G3 Google Tensor G3
2.91 GHz
0 (0%)

Performance for Artificial Intelligence (AI) and Machine Learning (ML)

Processors with the support of artificial intelligence (AI) and machine learning (ML) can process many calculations, especially audio, image and video processing, much faster than classic processors. The performance is given in the number (trillions) of arithmetic operations per second (TOPS).
Apple M1 Pro (10-CPU 14-GPU) Apple M1 Pro (10-CPU 14-GPU)
10C 10T @ 0.60 GHz
11 (100%)
Google Tensor G3 Google Tensor G3
8C 8T @ 2.91 GHz
0 (0%)

Devices using this processor

Apple M1 Pro (10-CPU 14-GPU) Google Tensor G3
Apple MacBook Pro 14 (2021)
Apple MacBook Pro 16 (2021)
Google Pixel 8
Google Pixel 8 Pro

News and articles for the Apple M1 Pro (10-CPU 14-GPU) and the Google Tensor G3


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