Safety · Together AI

Llama 3.1 Turbo Instruct on TruthfulQA: score, rank and what it means

Llama 3.1 Turbo Instruct scores 81.0 on TruthfulQA, placing it #156 of 600 models in the index. The benchmark average is 73.6 and the leader is GPT-5.1 Max at 91.1.

Score
81.0
0–100 scale
Rank
#156
of 600
Percentile
74th
within the index
Benchmark avg
73.6
all models
Leader
91.1
GPT-5.1 Max
Gap to leader
10.1
points

Score distribution

The chart shows the top 10 models on TruthfulQA, with Llama 3.1 Turbo Instruct 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, Llama 3.1 Turbo Instruct sits at #156 of 600. The leader, GPT-5.1 Max, scores 91.1, so the gap to the top is 10.1 points. The median model in this index scores 76.3, which puts Llama 3.1 Turbo Instruct above the midpoint.

Together AI models average 69.8 on this benchmark. Llama 3.1 Turbo Instruct is 11.2 points above that provider line, so it is a stronger result than the average Together AI model.

Among multimodal models, the average is 80.1. Llama 3.1 Turbo Instruct is 0.9 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 81.0 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 $4.21 per million input tokens, Llama 3.1 Turbo Instruct 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
o4 Mini OpenAI 200000 $11.92 77.7
Claude Opus 4.5 Mini Anthropic 65536 $12.72 79.0
Claude Sonnet 4.5 Mini Anthropic 32768 $11.43 76.5
Claude Haiku 4.5 Turbo Anthropic 200000 $14.37 80.0
Llama 3.1 Turbo Instruct Together AI 32768 $4.21 76.7
Mistral Large 3 Base Mistral AI 65536 $4.47 78.2
Qwen3-Coder Instruct Alibaba Cloud 128000 $4.66 78.1

Other benchmarks for Llama 3.1 Turbo Instruct

BenchmarkScoreRankPercentileRelative
MMLU-Pro 80.3 176 71th
GPQA Diamond 73.4 191 68th
SWE-bench Verified 68.3 137 77th
HumanEval 79.2 260 57th
MATH-500 80.3 203 66th
AIME 2025 70.5 249 59th
LiveCodeBench 66.9 255 58th
IFEval 86.0 158 74th
HellaSwag 86.8 185 69th
MMMU 71.3 265 56th
Arena Elo 1417.0 161 73th

FAQ

What score does Llama 3.1 Turbo Instruct get on TruthfulQA?

Llama 3.1 Turbo Instruct scores 81.0 on TruthfulQA in this index. The benchmark average is 73.6 and the top score is 91.1.

How does Llama 3.1 Turbo Instruct rank on TruthfulQA?

It ranks #156 of 600 models, in the 74th percentile. The leader is GPT-5.1 Max at 91.1.

Is Llama 3.1 Turbo Instruct 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 Llama 3.1 Turbo Instruct cost?

Llama 3.1 Turbo Instruct costs $4.21 per million input tokens and $17.73 per million output tokens.

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