What are the hardware requirements for Magistral Small 2506?

Magistral Small 2506 requires approximately 47.3 GB of VRAM and supports a context window of 40K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Magistral Small 2506 and how large is it?

Magistral Small 2506 is developed by AlexHung29629, a model with 23.6b parameters. The model is published as open weights on Hugging Face and indexed on LLM Explorer with full benchmark history.

How does Magistral Small 2506 perform on standard benchmarks?

Magistral Small 2506 has the following published scores: LMSYS ELO 1304. Compare against reference models on this page or on the LLM Explorer leaderboards.

Magistral Small 2506 by AlexHung29629 — VRAM 47.3GB, 40K context

Name: Magistral Small 2506
Author: AlexHung29629

Magistral Small 2506 is an open-source language model by AlexHung29629. Features: 23.6b LLM, VRAM: 47.3GB, Context: 40K, LLM Explorer Score: 0.32, ELO: 1304.

Arxiv:1910.09700 Autotrain compatible Endpoints compatible Mistral Region:us Safetensors Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/AlexHung29629/Magistral-Small-2506

Magistral Small 2506 Benchmarks

LMSys ELO 1304 vs 1272 (so35) 2.5%

^nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").

What is the LLM Explorer Rank (Score)

Magistral Small 2506 Parameters and Internals

LLM Name	Magistral Small 2506
Repository 🤗	https://huggingface.co/AlexHung29629/Magistral-Small-2506
Model Size	23.6b
Required VRAM	47.3 GB
Updated	2025-08-03
Maintainer	AlexHung29629
Model Type	mistral
Model Files	4.8 GB: 1-of-10 4.8 GB: 2-of-10 4.8 GB: 3-of-10 4.9 GB: 4-of-10 4.8 GB: 5-of-10 4.8 GB: 6-of-10 4.9 GB: 7-of-10 4.8 GB: 8-of-10 4.8 GB: 9-of-10 3.9 GB: 10-of-10
Model Architecture	MistralForCausalLM
Context Length	40960
Model Max Length	40960
Transformers Version	4.52.4
Tokenizer Class	LlamaTokenizerFast
Padding Token	<pad>
Vocabulary Size	131072
Torch Data Type	bfloat16

Rank the Magistral Small 2506 capabilities

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Instruction Following and Task Automation
Factuality and Completeness of Knowledge
Censorship and Alignment
Data Analysis and Insight Generation
Text Generation
Text Summarization and Feature Extraction
Code Generation
Multi-Language Support and Translation

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