What are the hardware requirements for Decido 1 Think 26B A4b?

Decido 1 Think 26B A4b requires approximately 51.8 GB of VRAM. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Decido 1 Think 26B A4b and how large is it?

Decido 1 Think 26B A4b is developed by decido-ai, a model with 26b parameters. The model is published as open weights on Hugging Face and indexed on LLM Explorer with full benchmark history.

How does Decido 1 Think 26B A4b perform on standard benchmarks?

Decido 1 Think 26B A4b has the following published scores: LMSYS ELO 1438. Compare against reference models on this page or on the LLM Explorer leaderboards.

Decido 1 Think 26B A4b by decido-ai — VRAM 51.8GB

Name: Decido 1 Think 26B A4b
Author: decido-ai

Decido 1 Think 26B A4b is an open-source language model by decido-ai. Features: 26b LLM, VRAM: 51.8GB, License: apache-2.0, Quantized, LLM Explorer Score: 0.47, ELO: 1438.

Base model:finetune:google/gem... Base model:google/gemma-4-26b-... Calibration Classification Conversational Decido Decision-model En Gemma4 Gguf Image-text-to-text Llama.cpp Q8 Quantized Reasoning Region:us Safetensors Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/decido-ai/decido-1-think-26b-a4b

Decido 1 Think 26B A4b Benchmarks

LMSys ELO 1438 vs 1272 (so35) 13.1%

^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)

Decido 1 Think 26B A4b Parameters and Internals

LLM Name	Decido 1 Think 26B A4b
Repository 🤗	https://huggingface.co/decido-ai/decido-1-think-26b-a4b
Base Model(s)	Gemma 4 26B A4B It google/gemma-4-26B-A4B-it
Model Size	26b
Required VRAM	51.8 GB
Updated	2026-11-10
Maintainer	decido-ai
Model Type	gemma4
Model Files	26.9 GB 5.2 GB: 1-of-11 4.9 GB: 2-of-11 4.9 GB: 3-of-11 4.4 GB: 4-of-11 4.9 GB: 5-of-11 4.4 GB: 6-of-11 4.9 GB: 7-of-11 4.4 GB: 8-of-11 4.9 GB: 9-of-11 4.9 GB: 10-of-11 4.0 GB: 11-of-11
Supported Languages	en
GGUF Quantization	Yes
Quantization Type	q8\|gguf
Model Architecture	Gemma4ForConditionalGeneration
License	apache-2.0
Transformers Version	5.5.0.dev0
Tokenizer Class	GemmaTokenizer
Padding Token	<pad>

Rank the Decido 1 Think 26B A4b capabilities

Have you tried this model? Rate its performance — this feedback helps the ML community find the right model for their needs.

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

Best Alternatives to Decido 1 Think 26B A4b

Best Alternatives	Context / RAM	Downloads	Likes
Gemma 4 26B A4B It GGUF	0K / 0.5 GB	1436859	1035
Decido 1 26B A4b	0K / 51.6 GB	166	1
...t Q4 0 Unquantized Heretic Ara	0K / 51.6 GB	370	1
Gemma 4 26B A4B It Qat GGUF	0K / 0.3 GB	349063	368
Swarm Sovereign 26B	0K / 51.6 GB	50	1
Gemopus 4 26B A4B It GGUF	0K / 1.2 GB	2783	101
OTel 2.0 Decision 26B A4B 4bit	0K / 17.3 GB	914755	4
...6B A4B It Qat Q4 0 Unquantized	0K / 51.6 GB	33696	50
Gemma 4 26B A4b It 4bit	0K / 15.4 GB	27543	79
...yberSecurity 26B A4B V2 Q8 MLX	0K / 40.6 GB	393	1

Note: green Score (e.g. "73.2") means that the model is better than decido-ai/decido-1-think-26b-a4b.