AI safety in production
AI safety in production. A short analysis built from the index, with the model-level data behind the claim.
What changes in production for AI safety
A model that works in a demo can fail in production for reasons that have nothing to do with benchmark scores: rate limits, latency spikes, prompt drift, data freshness and cost overruns. Ai Safety needs an operational plan, not just a model choice.
GPT-5.2 Pro may be the quality leader, but GPT-5.1 Reasoning may be the better production default if it clears the quality bar at a fraction of the cost.
Failure modes
- Provider outage with no fallback.
- Cost per request rising after a prompt change.
- Latency spikes under load.
- Silent quality regression after a model update.
- Data-retention terms that do not match policy.
Operational checklist
- Log model, prompt version and token counts.
- Set a cost alert per environment.
- Keep a second provider configured.
- Run a regression set before every rollout.
- Document the rollback path.
Models relevant to AI safety
| 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 AI safety analysis?
A model that works in a demo can fail in production for reasons that have nothing to do with benchmark scores: rate limits, latency spikes, prompt drift, data freshness and cost overruns. Ai Safety needs an operational plan, not just a model choice.
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.