Best local LLM for writing
Last updated 2026-08-28 · Page updated 2026-08-31
Top 8 open-source picks for creative and long-form writing, ranked by benchmark performance and real-world fit. Updated monthly.
Verdict (August 2026): Mistral Small 3.2 24B is our current top pick for creative and long-form writing (24B params · Apache 2.0). The full ranking and per-model reasoning follow.
This ranking is built from real specs — parameter count, VRAM at 4-bit quantization, license terms, and available benchmark scores — pulled from BestLLMfor's tracked catalog of 239 models, not vendor marketing. Data reflects the catalog as of 2026-08-28; see our full methodology for how models are scored and re-ranked.
Mistral Small 3.2 24B
Mistral AI's June 2025 refresh of Small 3.1: a 24B Apache 2.0 dense model with vision input, sharper function calling, and roughly half the rate of runaway generations seen in 3.1.
Gemma 4 31B
Google's dense 31B multimodal model with native text, image, and audio support across 140+ languages. Ranked #3 on Chatbot Arena's open leaderboard with a 256K context window.
Qwen 3.5 27B
Alibaba's dense 27B Qwen 3.5 with a 262K context window and calibrated thinking mode. One of the best quality-to-size trade-offs in the open 25B-30B class.
Llama 3.3 70B Instruct
Meta's Llama 3.3 70B — same quality tier as Llama 3.1 405B at one-sixth the size, thanks to improved post-training. Weights are gated on Hugging Face.
Mistral Nemo 12B Instruct
Mistral AI and NVIDIA's co-developed 12B instruct model with 128k context, the Tekken tokenizer, and strong European multilingual coverage.
Gemma 3 12B
The 12B sweet spot of Google's Gemma 3 line — multimodal, 128K context, and 140 languages. Fits on a single consumer GPU with room for batching.
Qwen 3 8B
Alibaba's 8B dense model with a toggleable thinking mode and broad multilingual coverage. Punches well above its weight for an 8B and runs comfortably on a single consumer GPU.
Gemma 4 E4B
Google's 4B-effective multimodal Gemma variant tuned for laptops and edge devices, handling text, image, and audio across 140 languages with a 128K context.
Which hardware should you buy to run Mistral Small 3.2 24B?
To run Mistral Small 3.2 24B locally at Q4, you need ~14 GB of VRAM. The best value for this today is a RTX 5070 Ti 16GB (GIGABYTE Gaming OC) (16 GB VRAM).
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Frequently asked questions
What is the best local LLM for creative and long-form writing?
Mistral Small 3.2 24B tops this ranking — a 24B model, licensed under Apache 2.0, needing about 14 GB of VRAM at Q4 quantization. See the full list below for the runner-ups and how they compare.
How much VRAM do I need to run Mistral Small 3.2 24B?
At Q4 quantization, Mistral Small 3.2 24B needs about 14 GB of VRAM and fits comfortably on a single 24 GB GPU.
Which of these models fit an 8 GB GPU?
At Q4 quantization, Mistral Nemo 12B Instruct, Gemma 3 12B, Qwen 3 8B fit within 8 GB of VRAM.
Are the models on this creative and long-form writing list free for commercial use?
Licenses across this list include Apache 2.0, Gemma, Llama 3.3 Community. Check the specific license of each model on its catalog page before deploying commercially, as terms vary by author.
What context window do these models support?
Context windows on this list range from 125k to 255k tokens, depending on the model.