MiniMax M2.5 vs Qwen3.5 397B

MiniMax M2.5 vs Qwen3.5 397B

A side-by-side developer comparison of benchmarks, use cases, and agentic performance.

M

Challenger A

MiniMax M2.5

VS
Q

Challenger B

Qwen3.5 397B

MiniMax M2.5 and Qwen3.5 397B represent two distinct architectural philosophies in the current high-performance open-weights model landscape. MiniMax M2.5 is specifically engineered for high-throughput, agentic workflows, utilizing a Mixture-of-Experts (MoE) architecture with Lightning Attention that prioritizes cost-efficiency and speed for real-world production environments. Its training is heavily optimized around complex task decomposition, making it a strong contender for developers building autonomous agents that need to manage multi-step tool calls, office automation, and coding tasks efficiently.

In contrast, Qwen3.5 397B adopts a native multimodal foundation strategy, integrating advanced reasoning capabilities with native vision and video understanding. It employs a hybrid architecture combining Gated Delta Networks with sparse Mixture-of-Experts to achieve frontier-tier performance while maintaining high decoding efficiency. For developers, this model offers a broader versatility, balancing rigorous logical reasoning and coding performance with advanced cross-modal processing, making it well-suited for applications that require depth in both reasoning and integrated visual input handling.

Visual comparison

MiniMax M2.5 vs Qwen3.5 397B infographic

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Benchmark scores

Higher is better

SWE-Bench Verified
MiniMax M2.5
80.2%
Qwen3.5 397B
N/A
Artificial Analysis Intelligence Index
MiniMax M2.5
42
Qwen3.5 397B
45
MMLU-Pro
MiniMax M2.5
N/A
Qwen3.5 397B
87.8%
GPQA
MiniMax M2.5
N/A
Qwen3.5 397B
88.4%

Strengths and weaknesses

MiniMax M2.5
✓Superior inference speed with throughputs up to 100 tokens per second.
✓Cost-effective for high-frequency agentic tasks (1/7 to 1/20 the cost of frontier models).
✓Exceptional performance on agentic benchmarks like SWE-Bench Verified.
✓Optimized 'Architect Mindset' planning capability for decomposing complex coding tasks.
✕Primarily focused on text and agentic workflows, lacking native multimodal video capabilities.
✕More limited in broad, world-knowledge reasoning compared to larger parameter models.
✕Higher verbosity in generation which can impact token costs for simpler queries.
Qwen3.5 397B
✓Native multimodal architecture supporting image and video inputs seamlessly.
✓Extremely strong academic reasoning performance (AIME26 and GPQA).
✓Balanced hybrid architecture using Gated Delta Networks for efficient high-throughput.
✓Expanded multilingual support covering over 201 languages and dialects.
✓Unified 'thinking' and 'non-thinking' modes within a single model deployment.
✕Higher potential for hallucinations compared to smaller, specialized agentic models.
✕Larger active memory footprint requiring significant hardware resources for local inference.
✕Increased complexity in deployment due to the integrated multimodal architecture.

When to use each model

Choose MiniMax M2.5 when your primary requirement is building high-efficiency, autonomous AI agents for production. It is an ideal choice for tasks involving complex coding repositories, document automation, and multi-step API function chaining where cost-per-task and latency are critical bottlenecks. Its specialized optimization for task decomposition makes it superior for internal tooling and workflow automation where consistent, low-latency performance is required.

Choose Qwen3.5 397B when your application demands robust reasoning capabilities alongside native multimodal awareness. It is better suited for sophisticated research, complex analytical tasks, and applications that require a unified model to interpret visual media and video content alongside text. This model is the preferred selection for projects requiring deep scientific reasoning or multilingual support across a diverse, global user base where depth of understanding outweighs extreme cost-minimization.

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