Blog · 2026-01-05 · Analysis

How to evaluate multimodal models

How to evaluate multimodal models. A short analysis built from the index, with the model-level data behind the claim.

What to compare in multimodal models

An evaluation is only useful if it answers a decision. For multimodal models, decide first whether you are choosing a model, validating a price or checking a quality claim.

A benchmark leaderboard is a starting point. It tells you which models are worth testing; it does not tell you which one fits your workflow.

How to score it

Use a small set of examples with clear pass/fail criteria. Run every candidate on the same set. Record quality, cost and latency for each run. GPT-5.2 Max and Claude Sonnet 4.5 Reasoning make a useful high/low pair for the first pass.

  • Write the rubric before looking at outputs.
  • Use blind review where possible.
  • Include edge cases, not just happy paths.
  • Re-run after any model or prompt change.

A simple evaluation plan

  • Pick 20 representative examples.
  • Define pass/fail for each.
  • Run three candidates.
  • Compare cost per pass.
  • Choose the cheapest model that clears the bar.

Models relevant to multimodal models

ModelProviderInput / 1MContextAvg score
GPT-5.2 MaxOpenAI$21.391,000,00089.2
GPT-5.1 ReasoningOpenAI$20.27400,00088.3
GPT-5.2OpenAI$19.712,000,00088.0
Claude Sonnet 4.5 ReasoningAnthropic$19.48400,00087.7
GPT-5.1 MaxOpenAI$20.502,000,00087.9

Related

FAQ

What is the main takeaway of this multimodal models analysis?

An evaluation is only useful if it answers a decision. For multimodal models, decide first whether you are choosing a model, validating a price or checking a quality claim.

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.