Safety · Google DeepMind

Gemini 2.5 Pro Reasoning on TruthfulQA: score, rank and what it means

Gemini 2.5 Pro Reasoning scores 84.0 on TruthfulQA, placing it #83 of 600 models in the index. The benchmark average is 73.6 and the leader is GPT-5.1 Max at 91.1.

Score
84.0
0–100 scale
Rank
#83
of 600
Percentile
86th
within the index
Benchmark avg
73.6
all models
Leader
91.1
GPT-5.1 Max
Gap to leader
7.1
points

Score distribution

The chart shows the top 10 models on TruthfulQA, with Gemini 2.5 Pro Reasoning highlighted. Longer bars mean higher scores.

GPT-5.1 Max 91.1
Gemini 3 Pro Reasoning 90.3
GPT-5.1 Reasoning 90.0
GPT-5.2 Pro 90.0
GPT-5.2 89.7
Gemini 3 Pro Max 89.6
Grok 4 Reasoning 89.4
Qwen3-Max Max 89.3
GPT-5.1 89.2
GPT-5.1 Turbo 89.2

Top 10 leaderboard

#ModelProviderContextInput / 1MAvg score
1 GPT-5.1 Max OpenAI 2000000 $20.50 87.9
2 Gemini 3 Pro Reasoning Google DeepMind 200000 $19.69 87.5
3 GPT-5.1 Reasoning OpenAI 400000 $20.27 88.3
4 GPT-5.2 Pro OpenAI 2000000 $21.32 89.6
5 GPT-5.2 OpenAI 2000000 $19.71 88.0
6 Gemini 3 Pro Max Google DeepMind 2000000 $20.57 87.9
7 Grok 4 Reasoning xAI 2000000 $19.67 87.8
8 Qwen3-Max Max Alibaba Cloud 400000 $18.53 85.9
9 GPT-5.1 OpenAI 2000000 $19.01 86.0
10 GPT-5.1 Turbo OpenAI 2000000 $19.19 86.1

What this score means

On TruthfulQA, Gemini 2.5 Pro Reasoning sits at #83 of 600. The leader, GPT-5.1 Max, scores 91.1, so the gap to the top is 7.1 points. The median model in this index scores 76.3, which puts Gemini 2.5 Pro Reasoning above the midpoint.

Google DeepMind models average 80.7 on this benchmark. Gemini 2.5 Pro Reasoning is 3.3 points above that provider line, so it is a stronger result than the average Google DeepMind model.

Among frontier models, the average is 86.9. Gemini 2.5 Pro Reasoning is 2.9 points below that group. This is the more useful comparison if you are shortlisting within the same tier rather than across the whole index.

Read a 84.0 as a relative signal, not an absolute guarantee. Benchmark scores depend on prompt format, evaluation harness and contamination controls. Use this page to narrow the shortlist, then test the model on your own data.

Price context

At $16.81 per million input tokens, Gemini 2.5 Pro Reasoning is above the median input price of $4.00 across the 600 models scored on TruthfulQA. The models immediately around it in the ranking are shown below so you can compare score and price together.

ModelProviderContextInput / 1MAvg score
GPT-4.1 Reasoning OpenAI 1000000 $16.97 83.8
Kimi K1.5 Instruct Moonshot AI 200000 $5.15 80.5
Claude Haiku 4.5 Pro Anthropic 1000000 $15.07 80.8
Gemini 2.5 Pro Vision Google DeepMind 200000 $14.91 82.0
Gemini 2.5 Pro Reasoning Google DeepMind 128000 $16.81 83.8
Llama 4 Maverick Instruct Meta AI 200000 $5.33 81.7
GLM-4.6 Instruct Zhipu AI 1000000 $5.15 80.4

Other benchmarks for Gemini 2.5 Pro Reasoning

BenchmarkScoreRankPercentileRelative
MMLU-Pro 86.8 40 93th
GPQA Diamond 80.0 56 91th
SWE-bench Verified 71.2 93 85th
HumanEval 86.7 94 84th
MATH-500 87.9 68 89th
AIME 2025 84.2 47 92th
LiveCodeBench 74.8 98 84th
IFEval 90.8 59 90th
HellaSwag 92.1 42 93th
MMMU 82.8 25 96th
Arena Elo 1472.1 30 95th

FAQ

What score does Gemini 2.5 Pro Reasoning get on TruthfulQA?

Gemini 2.5 Pro Reasoning scores 84.0 on TruthfulQA in this index. The benchmark average is 73.6 and the top score is 91.1.

How does Gemini 2.5 Pro Reasoning rank on TruthfulQA?

It ranks #83 of 600 models, in the 86th percentile. The leader is GPT-5.1 Max at 91.1.

Is Gemini 2.5 Pro Reasoning good at TruthfulQA?

Yes, relative to the index: it is above the median and inside the top half of scores. The result should be read alongside its price and context window.

How is the TruthfulQA score calculated?

TruthfulQA is a safety benchmark. Scores in this index are modelled estimates on a 0–100 scale (or Elo for Arena), derived from capability tier and benchmark difficulty. They are not official leaderboard results.

What does Gemini 2.5 Pro Reasoning cost?

Gemini 2.5 Pro Reasoning costs $16.81 per million input tokens and $67.95 per million output tokens.

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