# OpenAI Fine-Tune vs Together AI Fine-Tune: which fine-tuning platform wins in 2026?

**Source:** https://promtable.com/compare/openai-fine-tune-vs-together-fine-tune

> OpenAI Fine-Tune wins on flagship base models (GPT-4o, GPT-5), reliability, and OpenAI ecosystem features (DPO, vision). Together AI Fine-Tune wins on open-weight base models (Llama, Mistral, Qwen), LoRA pricing, and self-host options. Pick OpenAI for closed-flagship fine-tunes, Together for open-weight + cost flexibility.

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OpenAI Fine-Tune wins on flagship base models (GPT-4o, GPT-5), reliability, and OpenAI ecosystem features (DPO, vision). Together AI Fine-Tune wins on open-weight base models (Llama, Mistral, Qwen), LoRA pricing, and self-host options. Pick OpenAI for closed-flagship fine-tunes, Together for open-weight + cost flexibility.

## At a glance

| Dimension | OpenAI Fine-Tune | Together AI Fine-Tune |
|---|---|---|
| Base models | GPT-4o-mini, GPT-4o, GPT-5 tiers | Llama, Mistral, Qwen, DeepSeek open-weight |
| Fine-tune methods | **Supervised + DPO + vision** ✓ | Supervised + DPO + LoRA |
| LoRA / PEFT | Limited | **First-class LoRA fine-tunes** ✓ |
| Vision / multimodal fine-tune | **Yes (GPT-4o vision)** ✓ | Limited |
| Dataset size limits | Higher max | Higher max |
| Hosting after fine-tune | OpenAI-hosted (paid per-token after fine-tune) | **Together-hosted or export weights for self-host** ✓ |
| Self-host export | No (OpenAI-only) | **Yes (open-weight base + LoRA download)** ✓ |
| Pricing | Per-token training + premium inference tier | **Per-token training + standard inference** ✓ |
| Best for | Closed-flagship fine-tunes, vision, OpenAI ecosystem | Open-weight fine-tunes, LoRA, self-host export |

## Verdict

OpenAI Fine-Tune is the right pick for closed-flagship fine-tunes (GPT-4o, GPT-5 tier), vision fine-tuning, and OpenAI ecosystem features (DPO, evals, Playground integration). Together AI Fine-Tune is the right pick for open-weight fine-tunes (Llama, Mistral, Qwen, DeepSeek) where LoRA pricing is friendlier and you can export weights for self-host. Many production stacks fine-tune on Together (open-weight) for cost + ownership; OpenAI for closed-flagship task-specific quality.

## When to pick which

- **OpenAI Fine-Tune** — Closed-flagship base, vision, DPO, OpenAI ecosystem.
- **Together AI Fine-Tune** — Open-weight base, LoRA, self-host export, cost flexibility.

## FAQ

### Open-weight fine-tunes?

Together AI — Llama, Mistral, Qwen, DeepSeek with weight export.

### Vision fine-tune?

OpenAI — GPT-4o vision fine-tune leads.

### Self-host after fine-tune?

Together — export weights for self-host; OpenAI is OpenAI-hosted only.

## Related

- [/alternatives/openai-fine-tune](https://promtable.com/alternatives/openai-fine-tune)
- [/glossary/instruction-tuning](https://promtable.com/glossary/instruction-tuning)

*Last updated: 2026-06-01*
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Original page: https://promtable.com/compare/openai-fine-tune-vs-together-fine-tune
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