# The best open models to fine-tune in 2026

The best open models to fine-tune in 2026 are Qwen 3.5 (Apache 2.0, the strongest permissively licensed base), Llama 3.3 70B (the most documented training target in the industry), GLM 4.7 (MIT at $0.45 per million input tokens), and Llama 4 Scout (1M context, so training examples never truncate). Pick by license, language coverage, and the shape of your training data.

## Ranked list

1. **Qwen3.5 397B A17b** (Best results under a permissive license). Qwen · 256K context · $0.60 in / $3.60 out per M tokens · Apache 2.0. Apache 2.0 with 397B parameters and strong multilingual pretraining. The fine-tune spends its budget on your domain instead of general capability, and the result is unambiguously yours. https://allocate.network/models/qwen-3-5
2. **Meta Llama 3.3 70B Instruct Turbo** (Most predictable). Meta · 128K context · $1.04 in / $1.04 out per M tokens · Llama community. The 70B Llama line is the most documented fine-tuning target in the industry: every method, LoRA, QLoRA, full fine-tuning, has published recipes and known behavior on it. When the training run must work the first time, start here. https://allocate.network/models/meta-llama-3-3-70b-instruct-turbo
3. **GLM 4.7 FP8** (Best MIT base). Zai Org · 198K context · $0.45 in / $2 out per M tokens · MIT. MIT licensed at $0.45 per million input tokens with 198K context. A capable mid-size base with the most permissive license there is; whatever you train on it is yours without conditions. https://allocate.network/models/z-ai-glm-4-7
4. **Llama 4 Scout Instruct (17Bx16E)** (Best for long-context tasks). Meta · 1M context · $0.18 in / $0.59 out per M tokens · Llama community. Fine-tune it on tasks whose inputs are whole documents: the 1M-token window means training examples don’t need truncating, which no other open base on this list offers. https://allocate.network/models/meta-llama-4-scout-17b-16e-instruct

## Data table

| Rank | Model | Input | Output | Page |
| --- | --- | --- | --- | --- |
| 1 | Qwen3.5 397B A17b | $0.60 | $3.60 | https://allocate.network/models/qwen-3-5.md |
| 2 | Meta Llama 3.3 70B Instruct Turbo | $1.04 | $1.04 | https://allocate.network/models/meta-llama-3-3-70b-instruct-turbo.md |
| 3 | GLM 4.7 FP8 | $0.45 | $2 | https://allocate.network/models/z-ai-glm-4-7.md |
| 4 | Llama 4 Scout Instruct (17Bx16E) | $0.18 | $0.59 | https://allocate.network/models/meta-llama-4-scout-17b-16e-instruct.md |

Provider list prices from the Allocate catalog, checked 2026-07-21.

## What actually decides a good fine-tune

Data beats base model. A few thousand resolved real cases, actual disputes with actual outcomes, actual support threads with the answer that worked, outperform any amount of synthetic data on any base. Validate the dataset before spending on training.

Method is a cost decision more than a quality one. LoRA captures most task gains at a fraction of full fine-tuning’s cost and is the production default; QLoRA fits bigger bases on smaller hardware; full fine-tuning pays off only for deep domain shifts with large datasets.

The run itself is cheap relative to what it replaces: a typical LoRA run on a 70B base costs tens to low hundreds of dollars in GPU time at current rental rates. The fine-tuning cost calculator prices your exact run, and on Allocate the same run is quoted flat in writing before you commit.

## Common questions

### How much data do I need to fine-tune?

A few hundred excellent examples measurably change behavior; a few thousand is a strong production dataset. Past that, data quality matters far more than volume. Run your JSONL through the dataset validator to catch format errors and count tokens before training.

### What does a fine-tuning run cost?

A LoRA run on a 70B base with a few million training tokens typically lands between tens and a few hundred dollars of GPU time at current H100 rental rates. Full fine-tuning costs roughly 3 to 5x more. The fine-tuning cost calculator gives a per-run estimate.

### Do I own the fine-tuned model?

On permissively licensed bases, yes, if your platform lets you keep the weights. On Allocate, fine-tuned weights stay inside your isolation boundary, belong to you contractually, and go with you if you leave.

### Should I fine-tune or use RAG?

They solve different problems. RAG gives the model facts at request time; fine-tuning changes how the model behaves, its judgment, format discipline, and domain language. Production systems that get both right usually use both.

### Which base should a first fine-tune use?

Llama 3.3 70B if you want the most predictable run, Qwen 3.5 if you want the strongest permissively licensed result. Either way, start with LoRA, three epochs, and your few hundred best examples, then evaluate before scaling the dataset.

## Related

- [Fine-tuning cost calculator](https://allocate.network/tools/fine-tuning-cost-calculator)
- [Dataset validator (JSONL)](https://allocate.network/tools/fine-tuning-dataset-validator)
- [Best open-weight models](https://allocate.network/best/open-weight-models)
- [Open models with the longest context](https://allocate.network/best/open-models-with-the-longest-context)
- [What is fine-tuning?](https://allocate.network/glossary/fine-tuning)

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