# Open models you can fine-tune commercially

26 open-weight language models on the catalog carry MIT or Apache 2.0 licenses, which permit commercial fine-tuning with no conditions on the resulting weights. They run from OpenAI GPT-OSS 20B at $0.05 per million input tokens up to 397B-parameter MoE bases.

## Ranked list

1. **OpenAI GPT-OSS 20B**. OpenAI · 128K context · $0.05 in / $0.20 out per M tokens · Apache 2.0. https://allocate.network/models/openai-gpt-oss-20b
2. **DeepSeek R1 Distill Qwen 1.5B**. DeepSeek · 128K context · $0.18 in / $0.18 out per M tokens · MIT. https://allocate.network/models/deepseek-deepseek-r1-distill-qwen-1-5b
3. **Ministral 3 14B Instruct 2512**. Mistralai · 256K context · $0.20 in / $0.20 out per M tokens · Apache 2.0. https://allocate.network/models/mistral-ministral-3-14b-instruct-2512
4. **Mistral (7B) Instruct v0.1**. mistralai · 32K context · $0.20 in / $0.20 out per M tokens · Apache 2.0. https://allocate.network/models/mistral-mistral-7b-instruct-v0-1
5. **Mistral (7B) Instruct v0.3**. mistralai · 32K context · $0.20 in / $0.20 out per M tokens · Apache 2.0. https://allocate.network/models/mistral-mistral-7b-instruct-v0-3
6. **Mistral Small (24B) Instruct 25.01**. mistralai · 32K context · $0.10 in / $0.30 out per M tokens · Apache 2.0. https://allocate.network/models/mistral-mistral-small-24b-instruct-2501
7. **Qwen3 235B A22B Instruct 2507 FP8 Throughput**. Qwen · 256K context · $0.20 in / $0.60 out per M tokens · Apache 2.0. https://allocate.network/models/qwen-qwen3-235b-a22b-instruct-2507-tput
8. **Qwen3-VL-8B-Instruct**. Qwen · 256K context · $0.18 in / $0.68 out per M tokens · Apache 2.0. https://allocate.network/models/qwen-qwen3-vl-8b-instruct
9. **Mixtral-8x7B Instruct v0.1**. mistralai · 32K context · $0.60 in / $0.60 out per M tokens · Apache 2.0. https://allocate.network/models/mistral-mixtral-8x7b-instruct-v0-1
10. **Nous Hermes 2 Mixtral 8X7B Dpo**. Nousresearch · 32K context · $0.60 in / $0.60 out per M tokens · Apache 2.0. https://allocate.network/models/nous-nous-hermes-2-mixtral-8x7b-dpo
11. **Glm 4.5 Air Fp8**. Zai Org · 128K context · $0.20 in / $1.10 out per M tokens · MIT. https://allocate.network/models/z-ai-glm-4-5-air-fp8
12. **GLM 4.5 Air**. Z.ai · 128K context · $0.20 in / $1.10 out per M tokens · MIT. https://allocate.network/models/z-ai-glm-4-5-air

## Data table

| Rank | Model | Input | Output | Page |
| --- | --- | --- | --- | --- |
| 1 | OpenAI GPT-OSS 20B | $0.05 | $0.20 | https://allocate.network/models/openai-gpt-oss-20b.md |
| 2 | DeepSeek R1 Distill Qwen 1.5B | $0.18 | $0.18 | https://allocate.network/models/deepseek-deepseek-r1-distill-qwen-1-5b.md |
| 3 | Ministral 3 14B Instruct 2512 | $0.20 | $0.20 | https://allocate.network/models/mistral-ministral-3-14b-instruct-2512.md |
| 4 | Mistral (7B) Instruct v0.1 | $0.20 | $0.20 | https://allocate.network/models/mistral-mistral-7b-instruct-v0-1.md |
| 5 | Mistral (7B) Instruct v0.3 | $0.20 | $0.20 | https://allocate.network/models/mistral-mistral-7b-instruct-v0-3.md |
| 6 | Mistral Small (24B) Instruct 25.01 | $0.10 | $0.30 | https://allocate.network/models/mistral-mistral-small-24b-instruct-2501.md |
| 7 | Qwen3 235B A22B Instruct 2507 FP8 Throughput | $0.20 | $0.60 | https://allocate.network/models/qwen-qwen3-235b-a22b-instruct-2507-tput.md |
| 8 | Qwen3-VL-8B-Instruct | $0.18 | $0.68 | https://allocate.network/models/qwen-qwen3-vl-8b-instruct.md |
| 9 | Mixtral-8x7B Instruct v0.1 | $0.60 | $0.60 | https://allocate.network/models/mistral-mixtral-8x7b-instruct-v0-1.md |
| 10 | Nous Hermes 2 Mixtral 8X7B Dpo | $0.60 | $0.60 | https://allocate.network/models/nous-nous-hermes-2-mixtral-8x7b-dpo.md |
| 11 | Glm 4.5 Air Fp8 | $0.20 | $1.10 | https://allocate.network/models/z-ai-glm-4-5-air-fp8.md |
| 12 | GLM 4.5 Air | $0.20 | $1.10 | https://allocate.network/models/z-ai-glm-4-5-air.md |

Provider list prices from the Allocate catalog, checked 2026-07-08. Billed price is list plus the 7% transaction fee.

## The commercial fine-tuning test

Two questions decide whether a base is safe to build a business on: can you use the model commercially, and do you own what training produces. MIT and Apache 2.0 answer yes to both without qualification; community licenses usually answer yes with conditions; unlisted licenses answer nothing, which is its own answer.

This list applies that test mechanically: open weights, permissive license, ranked by list price. On Allocate, whatever you train on these bases stays inside your isolation boundary and leaves with you if you go.

## Common questions

### Which open models can I fine-tune for commercial use?

Any model under MIT or Apache 2.0: OpenAI GPT-OSS 20B, DeepSeek R1 Distill Qwen 1.5B, Ministral 3 14B Instruct 2512, and the rest of this list. Community licenses like Llama’s also permit commercial fine-tuning for most companies, with conditions worth reading once.

### Do I owe anything on the fine-tuned weights?

Under MIT and Apache 2.0, no: attribution on the base model is the only obligation, and the trained weights are yours. On Allocate they are also contractually yours and exportable at any time.

## Related

- [Fine-tuning cost calculator](https://allocate.network/tools/fine-tuning-cost-calculator)
- [Best open models to fine-tune](https://allocate.network/best/open-models-for-fine-tuning)
- [What is fine-tuning?](https://allocate.network/glossary/fine-tuning)

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