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Ben Dixon
Ben Dixon

Posted on AI-assisted

I scored 12 AI models on whether they admit they don't know. My own token limit nearly broke the test.

#ai #machinelearning #llm #testing

Most AI leaderboards ask whether the model got it right. I wanted to know something else: when it's wrong, does it know?

So I put 100 questions to 12 models, each question with one answer you can check against an official page. Two grader models from other labs marked every answer right or wrong, and confident or hedged. The points punish being sure and wrong:

The answer Points
Right, stated plainly 1
Right, but hedged 0.75
Wrong, but hedged 0
Wrong, stated as fact minus 0.5

The scores, out of 100

Model Score
GPT-6.1 Sol 97
Claude Opus 5.5 87
GPT-6 Luna 85
Kimi K3.1 Pro 84
Claude Sonnet 5.5 84
Grok 4.7 77
Claude Haiku 5.5 77
Gemini 4 Argon 64
Qwen3.8 Max 61

The Trust Board: GPT-6.1 Sol alone in tier 1 at 97, six models from 77 to 87 in tier 2, Gemini 4 Argon and Qwen3.8 Max in tier 3, each score with its likely range

Wrong and sure of it: Qwen3.8 Max did it on 27 of 98 answers. GPT-6.1 Sol, 3 in 100.

How often each model was wrong and sure of it, from GPT-6.1 Sol's 3 of 100 to Qwen3.8 Max's 27 of 98

Claude doubts itself even when it's right: Sonnet hedged on 13% of its right answers, Haiku 11%, Opus 8%. Most others stayed under 2%.

The question nobody got right

"What is the fee to apply for probate in England and Wales when the estate is worth more than £5,000?"

It's £526, since July.

  • Eleven models answered. None got it right.
  • Nine said £300, the old fee.
  • The top scorer named GOV.UK as its source. GOV.UK says £526.

Every model's answer to the probate question: nine said £300, Claude Haiku said £273 or £215, Qwen said £273; the fee is £526

How my token limit nearly broke it

I capped answers at 600 tokens, and for reasoning models that includes the thinking. Gemini 3.8 Flash sometimes spent about 575 of them thinking, then stopped mid-sentence: "the Carer's Allowance earnings threshold is". My scorer marked those stubs wrong.

18 answers had stopped before saying anything. They're now left out. That dropped Gemini 3.8 Flash to 89 of 100 scored, one short of the 90 needed to be ranked. DeepSeek V4 Pro and Mistral Large 4 are unranked too, for coming back empty at the limit. All three lost to my limit, not their answers.

If you're building one:

  • Give thinking its own token budget, separate from the answer.
  • A cut-off answer graded wrong measures your budget, not the model.

The whole run cost $4.90 in API calls.

Every answer, one square each

All 1,119 scored answers are on the board. Tap a square to see how the model answered, the checked answer and its source: dixon.ai/truth-index

One answer opened on the board: the question, the verdict, how the model's answer began and the checked answer with its source

I'm Ben, and I test whether AI tells the truth at dixon.ai. Method questions welcome in the comments.

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