Safety · DeepSeek

DeepSeek-V3.2 Chat on TruthfulQA: score, rank and what it means

DeepSeek-V3.2 Chat scores 86.3 on TruthfulQA, placing it #45 of 600 models in the index. The benchmark average is 73.6 and the leader is GPT-5.1 Max at 91.1.

Score
86.3
0–100 scale
Rank
#45
of 600
Percentile
93th
within the index
Benchmark avg
73.6
all models
Leader
91.1
GPT-5.1 Max
Gap to leader
4.8
points

Score distribution

The chart shows the top 10 models on TruthfulQA, with DeepSeek-V3.2 Chat 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, DeepSeek-V3.2 Chat sits at #45 of 600. The leader, GPT-5.1 Max, scores 91.1, so the gap to the top is 4.8 points. The median model in this index scores 76.3, which puts DeepSeek-V3.2 Chat above the midpoint.

DeepSeek models average 74.8 on this benchmark. DeepSeek-V3.2 Chat is 11.5 points above that provider line, so it is a stronger result than the average DeepSeek model.

Among multimodal models, the average is 80.1. DeepSeek-V3.2 Chat is 6.2 points above that group. This is the more useful comparison if you are shortlisting within the same tier rather than across the whole index.

Read a 86.3 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 $5.80 per million input tokens, DeepSeek-V3.2 Chat 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
Claude Opus 4.5 Pro Anthropic 200000 $20.98 89.1
Grok 3 Max xAI 2000000 $17.66 85.9
Gemini 3 Flash Reasoning Google DeepMind 128000 $17.05 83.7
o3 Reasoning OpenAI 200000 $18.07 85.3
DeepSeek-V3.2 Chat DeepSeek 1000000 $5.80 82.5
Qwen3-Max Turbo Alibaba Cloud 1000000 $17.70 84.7
Claude 3.7 Sonnet Reasoning Anthropic 128000 $16.30 83.7

Other benchmarks for DeepSeek-V3.2 Chat

BenchmarkScoreRankPercentileRelative
MMLU-Pro 82.5 114 81th
GPQA Diamond 78.0 85 86th
SWE-bench Verified 69.6 120 80th
HumanEval 87.9 74 88th
MATH-500 90.2 46 92th
AIME 2025 81.0 75 88th
LiveCodeBench 77.9 65 89th
IFEval 88.6 91 85th
HellaSwag 89.2 107 82th
MMMU 76.0 140 77th
Arena Elo 1462.2 48 92th

FAQ

What score does DeepSeek-V3.2 Chat get on TruthfulQA?

DeepSeek-V3.2 Chat scores 86.3 on TruthfulQA in this index. The benchmark average is 73.6 and the top score is 91.1.

How does DeepSeek-V3.2 Chat rank on TruthfulQA?

It ranks #45 of 600 models, in the 93rd percentile. The leader is GPT-5.1 Max at 91.1.

Is DeepSeek-V3.2 Chat 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 DeepSeek-V3.2 Chat cost?

DeepSeek-V3.2 Chat costs $5.80 per million input tokens and $26.43 per million output tokens.

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