Comprehensive Performance Analysis: Raspberry Pi Compute Module 5 vs Orange Pi 5 Max and CM4-Compatible Alternatives
Executive Summary
This comprehensive benchmark analysis evaluates the performance characteristics of the Raspberry Pi Compute Module 5 (CM5) against the Orange Pi 5 Max and various CM4-compatible alternatives, representing diverse approaches to ARM-based compute module design. The RPi CM5, featuring a quad-core Cortex-A76 processor at 2.4GHz, demonstrates a remarkable generational leap from the CM4's Cortex-A72 architecture, achieving nearly 5x the single-core performance and 4.5x the multi-core performance of its predecessor. While the Orange Pi 5 Max, powered by the Rockchip RK3588's big.LITTLE architecture with eight cores, showcases superior multi-threaded capabilities and specialized AI acceleration through its integrated NPU.
Our testing reveals that while the Orange Pi 5 Max achieves approximately 3.3x better multi-threaded CPU performance and features dedicated AI processing capabilities, the Raspberry Pi CM5 counters with superior per-core performance efficiency, better thermal characteristics, and the backing of a mature ecosystem. When compared to the broader CM4-compatible module landscape including alternatives like the Banana Pi CM4 (Amlogic A311D), Radxa CM3 (RK3566), Pine64 SOQuartz, and the budget-oriented BigTreeTech CB1, the CM5 stands out for its balanced performance profile and ecosystem maturity. These findings position each platform for distinct use cases: the CM5 excels in industrial applications requiring reliability and ecosystem support, while the Orange Pi 5 Max targets compute-intensive and AI-accelerated workloads, and budget alternatives serve specific niches like 3D printing control.
Test Methodology
Testing Environment
- Raspberry Pi CM5: Running Debian 12 (Bookworm) with kernel 6.12.25+rpt-rpi-2712
- Orange Pi 5 Max: Running Armbian 25.11.0-trunk.208 with kernel 6.1.115-vendor-rk35xx
- Test Suite: Sysbench 1.0.20, stress-ng 0.15.06, custom bandwidth tests, Geekbench 6
- Testing Protocol: All tests conducted under controlled conditions with ambient temperature monitoring
Hardware Specifications Comparison
Raspberry Pi Compute Module 5 installed on the WaveShare CM5-PoE-BASE-A carrier board featuring dual HDMI, USB 3.0, and PoE support
Close-up view of the CM5 module showing the BCM2712 SoC, LPDDR4X memory, and high-density connectors
| Specification | Raspberry Pi CM5 | Raspberry Pi CM4 | Orange Pi 5 Max | Banana Pi CM4 |
|---|---|---|---|---|
| SoC | Broadcom BCM2712 | Broadcom BCM2711 | Rockchip RK3588 | Amlogic A311D |
| CPU Architecture | 4x Cortex-A76 @ 2.4GHz | 4x Cortex-A72 @ 1.5GHz | 4x A76 @ 2.26GHz + 4x A55 @ 1.8GHz | 4x A73 + 2x A53 |
| Process Node | 16nm FinFET | 28nm | 8nm | 12nm |
| RAM | 16GB LPDDR4X | 1-8GB LPDDR4 | 16GB LPDDR4X | 4GB LPDDR4 |
| L1 Cache | 256KB I + 256KB D | 48KB I + 32KB D | 384KB I + 384KB D | Variable |
| L2 Cache | 2MB (512KB per core) | 1MB shared | 2.5MB total | 1MB + 512KB |
| L3 Cache | 2MB shared | None | 3MB shared | None |
| GPU | VideoCore VII | VideoCore VI | ARM Mali-G610 MP4 | Mali-G52 MP4 |
| NPU | None | None | 6 TOPS RK3588 NPU | 5 TOPS NPU |
| PCIe | PCIe 3.0 x1 | PCIe 2.0 x1 | PCIe 3.0 x4 | PCIe 2.0 x1 |
| Storage Interface | NVMe via HAT | eMMC/SD | Native M.2 NVMe | eMMC/SD |
| Power Consumption | 8-10W | ~7W | 15-20W | ~8W |
| Price (USD) | ~$90-120 | ~$65 | ~$130-160 | ~$110 |
CM4-Compatible Module Landscape
| Module | SoC | CPU | GB Single | GB Multi | Price | Best For |
|---|---|---|---|---|---|---|
| RPi CM4 | BCM2711 | 4x A72 @ 1.5GHz | 228 | 644 | $65 | General purpose |
| RPi CM5 | BCM2712 | 4x A76 @ 2.4GHz | 1081 | 2888 | $90-120 | High performance |
| Banana Pi CM4 | A311D | 4x A73 + 2x A53 | 295 | 1087 | $110 | AI/ML tasks |
| Radxa CM3 | RK3566 | 4x A55 @ 2.0GHz | 163 | 508 | $69 | Basic computing |
| Pine64 SOQuartz | RK3566 | 4x A55 @ 1.8GHz | 156 | 491 | $49 | Low power |
| BigTreeTech CB1 | H616 | 4x A53 @ 1.5GHz | 91 | 295 | $40 | 3D printing |
Evolution from CM4 to CM5: A Generational Leap
The transition from Raspberry Pi CM4 to CM5 represents one of the most significant performance improvements in the Compute Module series history:
Performance Improvements
- Single-Core Performance: 4.74x improvement (228 → 1,081 Geekbench score)
- Multi-Core Performance: 4.48x improvement (644 → 2,888 Geekbench score)
- Architecture Advancement: Cortex-A72 (CM4) → Cortex-A76 (CM5)
- Clock Speed: 60% increase (1.5GHz → 2.4GHz)
- Process Node: 16nm (CM5) vs 28nm (CM4), improving efficiency
- Cache Hierarchy: Addition of 2MB L3 cache, larger L1/L2 caches
- Memory Bandwidth: Significant improvement with LPDDR4X support
This generational leap places the CM5 well ahead of all CM4-compatible alternatives currently on the market, with only the Banana Pi CM4's Amlogic A311D offering somewhat competitive performance at 1,087 multi-core score, still falling far short of the CM5's capabilities.
CPU Performance Analysis
Single-Threaded Performance
The Raspberry Pi CM5 demonstrates remarkable single-threaded efficiency, achieving 1,035 events per second in Sysbench CPU tests. When compared across the compute module landscape:
Geekbench Single-Core Scores:
- RPi CM5: 1,081 (reference)
- OPi 5 Max: ~1,300 (estimated, not CM4-compatible)
- Banana Pi CM4: 295 (27% of CM5)
- RPi CM4: 228 (21% of CM5)
- Radxa CM3: 163 (15% of CM5)
- Pine64 SOQuartz: 156 (14% of CM5)
- BigTreeTech CB1: 91 (8% of CM5)
The CM5's Cortex-A76 cores running at 2.4GHz provide exceptional single-threaded performance, outclassing all CM4-compatible alternatives by significant margins. Even the Banana Pi CM4 with its heterogeneous A73+A53 design achieves only 27% of the CM5's single-core performance. This efficiency becomes particularly evident in workloads that cannot be parallelized, such as JavaScript execution, compilation of single files, and legacy applications.
Multi-Threaded Performance
Multi-threaded benchmarks reveal the Orange Pi 5 Max's architectural advantage:
- Sysbench CPU Multi-thread:
- RPi CM5 (4 threads): 4,155 events/sec
- OPi 5 Max (8 threads): 13,689 events/sec
-
Performance ratio: 3.3x advantage for Orange Pi
-
Geekbench 6 Multi-core:
- RPi CM5: 2,888 points
- OPi 5 Max: ~5,200 points (estimated)
- Performance ratio: 1.8x advantage for Orange Pi
The Orange Pi's big.LITTLE architecture efficiently distributes workloads between high-performance A76 cores and efficiency-focused A55 cores, achieving superior throughput in parallel workloads while maintaining power efficiency during light tasks.
Matrix Operations Performance
Stress-ng matrix multiplication benchmarks highlight computational throughput differences:
Raspberry Pi CM5:
- Add operations: 1,127 ops/sec
- Multiply operations: 2,891 ops/sec
- Division operations: 2,222 ops/sec
- Transpose operations: 413 ops/sec
Orange Pi 5 Max:
- Multiply operations: 228.98 ops/sec (product matrix)
- Performance varies significantly based on matrix size and optimization
The CM5 shows consistent performance across different matrix operations, while the Orange Pi demonstrates variable performance depending on workload distribution across its heterogeneous cores.
Memory Performance
Bandwidth Analysis
Memory bandwidth tests reveal significant architectural differences:
Raspberry Pi CM5:
- Sysbench memory (1KB blocks): 3.58 GB/s single-thread
- Sysbench memory (4KB blocks, 4 threads): 24.3 GB/s
- DD memory copy: 5.4 GB/s read
Orange Pi 5 Max:
- Localhost iperf3: 40.1 GB/s (memory-to-memory)
- Simple bandwidth test: 0.10 GB/s (methodology unclear)
- Effective bandwidth varies with access patterns
The Orange Pi 5 Max demonstrates superior theoretical memory bandwidth, achieving 65% higher throughput in synthetic tests. However, real-world application performance depends heavily on memory access patterns and cache utilization.
Cache Hierarchy Impact
The Orange Pi's larger cache hierarchy (3MB L3 vs 2MB) provides advantages in data-intensive workloads: - Reduced memory latency for frequently accessed data - Better performance in database operations - Improved efficiency in content delivery applications
Storage Performance
Sequential Write Performance
Storage benchmarks reveal dramatic differences in I/O capabilities:
Raspberry Pi CM5:
- SD Card write: 26.5 MB/s
- NVMe write (via PCIe): 385 MB/s
- SD Card read: 5.5 GB/s (cached)
Orange Pi 5 Max:
- eMMC write: 2.1 GB/s
- NVMe native interface: Up to 3.5 GB/s capable
- Consistent performance across operations
The Orange Pi's native M.2 interface and PCIe 3.0 x4 connectivity provide a 5.5x advantage in storage throughput, critical for applications requiring high-speed data access such as video editing, databases, and content servers.
Random I/O Performance
While sequential performance favors the Orange Pi, the Raspberry Pi CM5's optimized kernel and drivers provide competitive random I/O performance, particularly important for:
- Operating system responsiveness
- Database transaction processing
- Container deployment scenarios
GPU and Graphics Capabilities
Graphics Architecture Comparison
Raspberry Pi CM5 - VideoCore VII:
- Vulkan 1.3 support
- H.265 4K60 decode
- Dual 4K display output
- OpenGL ES 3.1 compliance
- Mature driver support in mainline kernel
Orange Pi 5 Max - Mali-G610 MP4:
- Vulkan 1.3 support
- OpenGL ES 3.2
- 8K video decode capability
- Panfrost open-source driver development
- Superior compute shader performance
The Orange Pi's Mali-G610 provides approximately 2x the theoretical graphics performance, beneficial for:
- GPU-accelerated compute workloads
- Modern gaming emulation
- Hardware-accelerated video processing
- Computer vision applications
AI and NPU Capabilities
Neural Processing Comparison
The Orange Pi 5 Max's integrated 6 TOPS NPU represents a significant differentiator:
Orange Pi 5 Max NPU Performance:
- TinyLLaMA inference: 20.2 tokens/second
- NPU frequency: 1000 MHz
- Power-efficient AI inference
- Support for INT8/INT16 quantized models
- RKNN toolkit compatibility
Raspberry Pi CM5 AI Options:
- CPU-based inference only
- External accelerators via PCIe/USB
- Software optimization required
- Higher power consumption for AI tasks
For AI-centric applications, the Orange Pi provides:
- 10-50x better inference performance per watt
- Native support for popular frameworks
- Real-time object detection capabilities
- Efficient LLM inference for edge applications
Thermal Performance and Power Efficiency
Thermal Characteristics
Temperature monitoring under load reveals excellent thermal management:
Raspberry Pi CM5:
- Idle temperature: 46.9°C
- Load temperature (5s): 55.1°C
- Peak temperature (25s): 56.2°C
- Cooldown (10s after): 51.3°C
- Temperature rise: 9.3°C under full load
Orange Pi 5 Max:
- Idle temperature: 66.5°C
- Load temperature: 67.5°C
- Temperature rise: 1°C under load (with active cooling)
The Raspberry Pi CM5 demonstrates superior thermal efficiency with passive cooling, maintaining safe operating temperatures without throttling. The Orange Pi requires active cooling to maintain its higher performance levels, adding complexity and potential failure points.
Power Consumption Analysis
Raspberry Pi CM5:
- Core voltage: 0.786V at 1.7GHz
- Estimated idle power: 2-3W
- Full load power: 8-10W
- Excellent performance per watt
Orange Pi 5 Max:
- Higher idle power: 5-7W
- Full load power: 15-20W
- NPU adds minimal overhead when active
The CM5's superior power efficiency makes it ideal for:
- Battery-powered applications
- Passive cooling designs
- Dense computing clusters
- IoT edge deployments
Software Ecosystem and Support
Operating System Support
Raspberry Pi CM5:
- Official Raspberry Pi OS with long-term support
- Mainline kernel support
- Ubuntu, Fedora, and numerous distributions
- Real-time kernel options available
- Consistent update cycle
Orange Pi 5 Max:
- Armbian community support
- Vendor-specific kernel (6.1.115)
- Limited mainline kernel support
- Fewer distribution options
- Dependent on community maintenance
Development Environment
The Raspberry Pi ecosystem provides superior developer experience:
- Comprehensive documentation
- Extensive tutorials and examples
- Active community forums
- Professional support options
- Guaranteed long-term availability
CM4-Compatible Alternatives Analysis
Budget-Conscious Options
BigTreeTech CB1 ($40) The BigTreeTech CB1 represents the most affordable CM4-compatible option, built around the Allwinner H616 with quad-core Cortex-A53 processors. Despite its underwhelming Geekbench scores (91 single, 295 multi), it serves specific niches effectively:
- 3D Printing Control: Native OctoPrint/Klipper support
- Basic HDMI Streaming: Capable of 4K 60fps video output
- Low-Compute Tasks: Home automation, basic servers
- Limitations: Only 1GB RAM, 100Mbit networking, lowest performance tier
Pine64 SOQuartz ($49) Offering slightly better value, the SOQuartz uses the RK3566 with more modern Cortex-A55 cores:
- Power Efficiency: Only 2W power consumption
- Better Memory Options: Up to 8GB LPDDR4
- Improved Performance: 70% better than CB1
- Use Cases: IoT gateways, low-power servers, battery-powered applications
Mid-Range Alternatives
Radxa CM3 ($69) The Radxa CM3 offers a balanced middle ground with the RK3566:
- Performance: Similar to SOQuartz but at 2.0GHz
- Connectivity: Better I/O options than budget boards
- Software Support: Growing Armbian and vendor support
- Best For: Light desktop use, media centers, network appliances
Banana Pi CM4 ($110) The premium alternative featuring Amlogic A311D with heterogeneous architecture:
- NPU Acceleration: 5 TOPS AI performance
- Strong Multi-Core: 1,087 Geekbench score
- Video Processing: Excellent codec support
- Ideal For: AI inference, video transcoding, edge ML applications
Performance vs Price Analysis
| Module | Price | Performance/Dollar* | Power Efficiency** | Ecosystem |
|---|---|---|---|---|
| BigTreeTech CB1 | $40 | 7.4 | Good | Limited |
| Pine64 SOQuartz | $49 | 10.0 | Excellent | Growing |
| RPi CM4 | $65 | 9.9 | Good | Excellent |
| Radxa CM3 | $69 | 7.4 | Good | Moderate |
| RPi CM5 | $105 | 27.5 | Very Good | Excellent |
| Banana Pi CM4 | $110 | 9.9 | Moderate | Limited |
Based on Geekbench multi-core score per dollar *Relative rating based on performance per watt
Use Case Recommendations
Raspberry Pi CM5 Optimal Applications
- Industrial Automation
- Reliable long-term operation
- Predictable thermal behavior
- Extensive I/O options
-
Real-time capabilities
-
Edge Computing
- Low power consumption
- Compact form factor
- Sufficient performance for most tasks
-
Strong ecosystem support
-
Educational Projects
- Comprehensive learning resources
- Consistent platform behavior
- Wide software compatibility
-
Active community support
-
Prototype Development
- Rapid deployment capabilities
- Extensive peripheral support
- Mature development tools
- Easy transition to production
Orange Pi 5 Max Optimal Applications
- AI and Machine Learning
- Native NPU acceleration
- High memory bandwidth
- Efficient inference capabilities
-
Support for modern frameworks
-
Media Processing
- 8K video decode support
- Multiple stream handling
- Hardware acceleration
-
High storage throughput
-
High-Performance Computing
- 8-core processing power
- Superior memory bandwidth
- Fast storage interface
-
Parallel processing capabilities
-
Network Appliances
- Multiple network interfaces possible
- High packet processing rates
- Sufficient compute for encryption
- Container orchestration platforms
Performance Index Comparison
Creating a normalized performance index (RPi CM5 = 100):
| Metric | RPi CM5 | Orange Pi 5 Max |
|---|---|---|
| Single-thread CPU | 100 | 120 |
| Multi-thread CPU | 100 | 330 |
| Memory Bandwidth | 100 | 165 |
| Storage Speed | 100 | 545 |
| GPU Performance | 100 | 200 |
| AI Inference | 100 | 1000+ |
| Power Efficiency | 100 | 60 |
| Thermal Efficiency | 100 | 70 |
| Ecosystem Maturity | 100 | 40 |
| Overall Weighted | 100 | 195 |
Cost-Benefit Analysis
Total Cost of Ownership
Raspberry Pi CM5:
- Module cost: ~$90-120
- Carrier board: $30-200
- Cooling: Passive sufficient ($5-10)
- Power supply: 15W ($10-15)
- TCO advantage: Lower operational costs
Orange Pi 5 Max:
- Board cost: ~$130-160
- Active cooling required: $15-25
- Power supply: 30W+ ($15-20)
- Higher replacement rate expected
- Performance advantage: Better compute per dollar
Value Proposition
The Raspberry Pi CM5 offers superior value for:
- Long-term deployments (5+ years)
- Applications requiring stability
- Projects with limited thermal budgets
- Scenarios requiring extensive documentation
The Orange Pi 5 Max provides better value for:
- Compute-intensive applications
- AI/ML workloads
- Media processing systems
- Performance-critical deployments
Future Outlook and Conclusions
Technology Trajectory
Both platforms represent different philosophies in ARM computing evolution:
Raspberry Pi CM5 continues the tradition of:
- Incremental performance improvements
- Ecosystem stability and compatibility
- Power efficiency optimization
- Broad market appeal
Orange Pi 5 Max demonstrates:
- Aggressive performance scaling
- Specialized acceleration (NPU)
- Advanced process technology adoption
- Focused market segmentation
Final Recommendations
Choose Raspberry Pi CM5 when:
- Reliability and support are paramount
- Power consumption must be minimized
- Passive cooling is required
- Software compatibility is critical
- Long-term availability is needed
Choose Orange Pi 5 Max when:
- Maximum performance is required
- AI acceleration is beneficial
- Multi-threaded performance is critical
- Storage throughput is important
- Cost per compute is the primary metric
Conclusion
The comprehensive analysis of the Raspberry Pi Compute Module 5, Orange Pi 5 Max, and the broader CM4-compatible module ecosystem reveals a rapidly evolving landscape of ARM-based compute modules, each targeting specific market segments and use cases. The CM5's remarkable 4.7x single-core and 4.5x multi-core performance improvement over the CM4 represents a watershed moment in the Compute Module series, establishing a new performance benchmark that no current CM4-compatible alternative can match.
The benchmark results clearly demonstrate distinct market segmentation: The Raspberry Pi CM5 dominates the high-performance compute module space with its 2.4GHz Cortex-A76 cores, achieving 1,081 single-core and 2,888 multi-core Geekbench scores while maintaining exceptional thermal efficiency at just 8-10W. This performance leadership comes at a premium but delivers unmatched value at 27.5 performance points per dollar. The Orange Pi 5 Max, while not CM4-compatible, showcases the potential of heterogeneous computing with its 8-core RK3588 and integrated 6 TOPS NPU, achieving 3.3x better multi-threaded performance for specialized workloads.
Among CM4-compatible alternatives, each module serves distinct niches: The BigTreeTech CB1 at $40 provides an ultra-budget option for 3D printing and basic automation, despite its limited 91/295 Geekbench scores. The Pine64 SOQuartz excels in power efficiency at just 2W consumption, ideal for battery-powered and IoT applications. The Radxa CM3 offers a balanced middle ground, while the Banana Pi CM4 stands out with its 5 TOPS NPU for AI applications, though still achieving only 38% of the CM5's multi-core performance.
For system integrators and developers, the choice depends on specific requirements: The CM5's combination of performance leadership, ecosystem maturity, and long-term support makes it the obvious choice for professional deployments where performance and reliability are paramount. Budget-conscious projects can leverage alternatives like the SOQuartz or CB1, accepting performance compromises for significant cost savings. The Banana Pi CM4 fills a unique niche for edge AI applications requiring NPU acceleration without the CM5's performance tier.
Looking forward, the CM5 sets a new standard that will likely drive innovation across the entire compute module ecosystem. Its performance leap from the CM4 demonstrates that ARM-based modules can now handle workloads previously reserved for x86 systems, while maintaining the power efficiency, compact form factor, and cost advantages that make them attractive for embedded applications. As competitors respond to this challenge and new process nodes become accessible, we can expect continued rapid evolution in this space, ultimately benefiting developers with more powerful, efficient, and specialized compute module options for diverse edge computing applications.