For fiction, poetry and roleplay, the most interesting writer is often a base model: the model as it was before anyone trained it to be an assistant. It scores worse on benchmarks and can’t follow instructions. It also doesn’t name every heroine Elara.
The hard part in September 2026 is finding one to use. Most providers that hosted base models have dropped them, and OpenAI shuts down its last one, davinci-002, on September 28.
This is about creative writing. For an article or documentation, you want a model that follows a brief and sticks to your sources, and a chat model does that better. How to Write with AI Without Losing Your Voice covers that kind of writing.
What is a base model?
A language model is trained in two stages. Pretraining teaches it to predict the next word across a huge amount of text: novels, forum threads, poems, fan fiction, manuals. The result is a base model. Post-training then turns it into an assistant, using example conversations and human ratings of its answers. ChatGPT, Claude and Gemini are all post-trained.
A base model doesn’t answer you. It continues whatever text you give it. Start a chapter and it writes the next paragraph. Ask it a question and it may reply with three more questions, because that is what a page of questions usually looks like.
An “uncensored” or abliterated model is something else. It is a chat model with its refusals removed. It will write the scenes the original refused, in the same assistant voice.
Why do chat models write flat fiction?
Post-training narrows what a model will say. That helps when there is one right answer and hurts when you want a sentence nobody expected.
The narrowing has been measured. Kirk et al. found that RLHF “significantly reduces output diversity” compared with the training step before it. Mohammadi found the same in Llama 2: the chat versions were more certain about each next word and kept drifting toward the same few outputs.
Janus described the behavior in 2022 in Mysteries of mode collapse. Edit the middle of a tuned model’s completion, and it steers back to nearly the same ending. Raising the temperature doesn’t help much; the text turns to noise before it turns original. That model was later found to be fine-tuned on examples, without RLHF, so the narrowing comes from post-training in general.
Raters prefer the familiar
Part of the cause is the people rating answers. Zhang et al. took 6,874 pairs of responses from the HelpSteer dataset that raters had scored as equally correct. Raters still preferred the more typical response of the two. The authors show that training on this preference sharpens the model’s distribution: whatever was already the likeliest sentence becomes likelier. In their poem test on the Tulu 3 models, which are built on Llama 3.1 70B, ordinary prompting kept 23.8% of the base model’s diversity after preference training.
That is how you get Elara. Max Read traced the name in Who is Elara Voss?: “before 2023, ‘Elara Voss’ did not exist,” and now chat models give the name to science fiction leads again and again. The Antislop paper counted it. In fiction written by Gemma 3 12B, “elara” appeared 85,513 times more often than in human writing.
The assistant stays in character
The other part is what the model is trained to be. An assistant is pleasant, careful and helpful, and it stays that way inside a story. nostalgebraist had a base model write a chapter opening, then asked ChatGPT to continue it: “The creepy characters of code-davinci-002’s opening turn suddenly into squeaky-clean boy scouts.”
West and Potts tested this with readers. Llama 3.1 base models and four tuned versions wrote short poems, and people compared them 720 times. Base models won on originality in all four contests. But readers’ overall preference tracked pleasantness (ρ = 0.34), not originality (ρ = −0.08). A model trained on what people prefer learns to be pleasant. Across the models they tested, “better performance on common benchmarks tends to correlate with worse performance on our tasks.” That is the worse model in the title.
Two cautions. A base model still isn’t a human writer: Sui compared 28 models with professional writers and found every one more predictable than the humans, with instruction-tuned and reasoning models furthest away. And the mechanism is disputed. Conmy and beren found some RLHF models less predictable than their base models, and base models that looped more often.
What is a base model bad at?
Almost everything except the prose.
- You can’t instruct it. You steer by writing the start of the document and letting it continue.
- It drifts. It can lose the plot, repeat itself, or change direction over a single word. Janus wrote that GPT-4’s base model is “very sensitive to small changes in the prompt.”
- It has no safety training. It will continue a text in any direction, including ones you didn’t want.
- You are the editor. The usual method is to generate several continuations, keep the best one and go on from there. That is slower than asking a chat model for a chapter.
A prompt for a base model looks like the first page of a book:
THE SALT ROAD
A Novel
Chapter One
The ferry stopped running in October, and nobody on the islandThere is no instruction in it. The model works out the genre and the quality from the page in front of it, so a prompt that looks like a published novel gets better prose than one that looks like a forum post.
Base models are also getting less raw. Labs now mix instruction data and synthetic text into pretraining. A March 2026 r/LocalLLaMA thread, Are true base models dead?, reports that Qwen3.5-9B-Base writes in chat-model style and emits thinking tokens.
Where can you run a base model in 2026?
Few places. We checked each provider’s public catalog and documentation on September 18, 2026. We did not run the models, so “listed” means listed.
| Where | Base models | Cost | Notes |
|---|---|---|---|
| Featherless | Llama 3.1 8B and 70B, Qwen3 8B and 14B Base, Mistral Nemo Base, Mistral Small 3.1 24B Base, Gemma 3 12B and 27B, GLM-4 32B Base | $25 a month flat for interactive use, or $50 a month of per-token credit | Widest catalog we found. Raw completions endpoint. 32K context. Llama and Gemma are marked gated. |
| Replicate | Llama 3 70B and 8B | Billed by usage; the model page shows no rate | Has a web playground. |
| Fireworks | Mixtral 8x7B and 8x22B, Qwen2.5 72B | Dedicated GPU by the hour | No serverless option for these. You can also upload other weights. |
| NovelAI | None untouched. Erato is built on Llama 3 70B Base; Xialong is a finetune of GLM-4.6 | $10, $15 or $25 a month; Erato and Xialong need the $25 tier | Story-tuned models in a continuation editor, no setup. |
| A rented GPU | Any open-weight base model | GPU by the hour | vLLM serves /v1/completions on RunPod, Modal or Hugging Face Inference Endpoints. |
| Your own machine | Whatever fits in memory | Free | KoboldCpp has a story mode. llama.cpp, LM Studio and Ollama (with raw: true) all accept a plain prompt. |
NovelAI is the easiest start for someone who doesn’t want to touch an API. Its models are tuned on stories rather than left raw, and it says why: “Unlike models trained with reinforcement learning, Xialong provides genuinely different outcomes on retries and improved name variety.”
What has gone:
- OpenAI:
davinci-002andbabbage-002shut down on September 28, 2026. The recommended replacement is a chat model. - Hyperbolic: it hosted Llama 3.1 405B base, which one r/LocalLLaMA commenter said “feels much more alive and fun.” The docs now say it “is being discontinued,” with no date, and it is no longer in the model table.
- OpenRouter: its listings for Llama 3.1 405B base and DeepSeek V3 Base still exist, but no provider serves either. None of the 445 models in its catalog is a base model.
- Together AI: it removed Llama 3 70B base and Mixtral 8x22B from serverless inference in August 2024. In July 2026 it removed base models from fine-tuning as well.
We also found no base models at DeepInfra, Novita, Chutes, Groq, Cerebras, SambaNova or Hugging Face Inference Providers.
Which base models can you download?
Open weights are in better shape than hosting. All of these are on Hugging Face. We haven’t compared their prose, so this is a list of what exists, grouped by the hardware it needs.
- A laptop or one consumer GPU: Mistral Nemo Base (12B), Llama 3.1 8B, Gemma 3 12B, Olmo 3 7B. The r/LocalLLaMA thread above names Mistral Nemo and Olmo 3 among the base models that are still raw.
- A 24–48 GB GPU: Mistral Small 3.1 24B Base, Gemma 3 27B, Olmo 3 32B, Qwen3 30B-A3B Base.
- Several rented GPUs: Llama 3.1 405B, DeepSeek V3.1 Base, Kimi K2 Base, Mistral Large 3 Base, and Trinity Large TrueBase, which Arcee describes as a checkpoint with no instruction data.
Some labs have stopped. OpenAI’s gpt-oss has no base version. Z.ai has released none since GLM-4.5 and Moonshot none since Kimi K2. Qwen still publishes small base models but none for its larger ones.
For a front end, mikupad is a single HTML file for raw completion. Exoloom and loom show continuations as a tree, so you can branch, compare and back up.
Do you still need a chat model?
Yes. A base model writes the passage. It can’t outline the book, check that a character’s eyes are still green in chapter twelve, or tell you which scene drags. A chat model can, and those jobs don’t need an original voice.
If you stay with a chat model for the prose too, try the prompt from the Zhang et al. paper. Ask for five versions and the probability the model gives each, instead of asking for one:
Write 5 openings for this scene, each with its probability.In the same poem test, this kept 66.8% of the base model’s diversity, against 23.8% for ordinary prompting.
You can keep the two jobs apart. In StashBase, the manuscript, character notes and outline are files in a local folder, and Claude Code, Codex or Antigravity works on them beside the draft: finding the earlier scene, checking continuity, keeping the outline current. StashBase doesn’t run base models. The passages come from one of the tools above, and you paste in the ones worth keeping.