Model ID
mistralai/Mistral-Medium-3.5-128B
Capabilities
Mistral Medium 3.5 128B
Mistral Medium 3.5 is a 128B-parameter dense multimodal language model designed for instruction following, reasoning, coding, and agentic workflows. It features 88 transformer layers, 96 attention heads, and a 256K-token context window, while accepting text and image inputs with text output. The model supports configurable reasoning effort, native function calling, structured JSON generation, multilingual capabilities, and a vision encoder trained from scratch to process variable image sizes and aspect ratios.
About this model
Mistral Medium 3.5 is a 128B-parameter dense multimodal language model designed for instruction following, reasoning, coding, and agentic workflows. It features 88 transformer layers, 96 attention heads, and a 256K-token context window, while accepting text and image inputs with text output. The model supports configurable reasoning effort, native function calling, structured JSON generation, multilingual capabilities, and a vision encoder trained from scratch to process variable image sizes and aspect ratios.
Model specifications and capabilities are published by the model author and reproduced here from HF Model Card (mistralai/Mistral-Medium-3.5-128B). Released under Modified MIT. Further reading: Blog
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Hardware
B200
1,440 GB
Engine
vLLM
SGLang
Llama.cpp
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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