Model ID
MiniMaxAI/MiniMax-M3.1-Flash
Capabilities
MiniMax M3.1 Flash
MiniMax M3.1 Flash is a native multimodal Mixture-of-Experts model for coding, agentic workflows, computer use, and cybersecurity across text, image, and video. Its approximately 428B parameters, 23B of them active per token, are arranged in a 60-layer architecture with 6,144 hidden size, 64 attention heads, and 4 KV heads. MiniMax Sparse Attention runs in every layer, while the MoE blocks route each token to 4 of 128 experts plus a shared expert. Supporting a 1M-token context, it combines quantization-aware training with a DSpark draft head for speculative decoding.
About this model
MiniMax M3.1 Flash is a native multimodal Mixture-of-Experts model for coding, agentic workflows, computer use, and cybersecurity across text, image, and video. Its approximately 428B parameters, 23B of them active per token, are arranged in a 60-layer architecture with 6,144 hidden size, 64 attention heads, and 4 KV heads. MiniMax Sparse Attention runs in every layer, while the MoE blocks route each token to 4 of 128 experts plus a shared expert. Supporting a 1M-token context, it combines quantization-aware training with a DSpark draft head for speculative decoding.
Model specifications and capabilities are published by the model author and reproduced here from HF Model Card (MiniMaxAI/MiniMax-M3.1-Flash). Released under MiniMax Community. Further reading: Blog
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Hardware
B200
1,440 GB
Engine
vLLM
SGLang
Deployment code
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Performance under load
Tokens/s (total)
Benchmarks measured by Vultr on B200 · vLLM. Throughput and latency vary with concurrency, input length and engine configuration, so treat these as a comparison baseline, not a service guarantee.
NVIDIA HGX B200
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