What fine-tuning means for AI teams
What fine-tuning means for AI teams. A short analysis built from the index, with the model-level data behind the claim.
Why fine-tuning matters for teams
Teams rarely fail because they picked the wrong model on a benchmark. They fail because the model, the prompt, the retrieval layer and the cost model were never evaluated together. Fine-Tuning is one of the places where those assumptions meet.
The useful move is to make the decision explicit: what quality bar, what budget, what latency and what fallback. Without those four numbers, every model looks plausible.
What to measure
- Quality on a fixed set of representative examples.
- Cost per successful task, including retries.
- Latency at the 50th and 95th percentile.
- Failure rate and how often a fallback is needed.
A practical checklist
Start with GPT-5.2 Pro as the quality candidate and GPT-5.1 Reasoning as the value candidate. Run both on the same examples and compare the cost of a correct answer, not the cost of a token.
Models relevant to fine-tuning
| Model | Provider | Input / 1M | Context | Avg score |
|---|---|---|---|---|
| GPT-5.2 Pro | OpenAI | $21.32 | 2,000,000 | 89.6 |
| GPT-5.2 Max | OpenAI | $21.39 | 1,000,000 | 89.2 |
| Claude Opus 4.5 Pro | Anthropic | $20.98 | 200,000 | 89.1 |
| GPT-5.1 Reasoning | OpenAI | $20.27 | 400,000 | 88.3 |
| Claude Opus 4.5 Reasoning | Anthropic | $20.91 | 2,000,000 | 88.2 |
Related
FAQ
What is the main takeaway of this fine-tuning analysis?
Teams rarely fail because they picked the wrong model on a benchmark. They fail because the model, the prompt, the retrieval layer and the cost model were never evaluated together. Fine-Tuning is one of the places where those assumptions meet.
Are the figures official?
No. The figures come from the AI & LLM Index demo dataset. Prices and scores are compiled for comparison; benchmark values are modelled estimates.
How should I use this in a buying decision?
Use it to build a shortlist, then test the shortlist on your own examples. Check the model pages for current price and context details.