Llama 3.1 Cerebras Chat on TruthfulQA: score, rank and what it means
Llama 3.1 Cerebras Chat scores 78.4 on TruthfulQA, placing it #241 of 600 models in the index. The benchmark average is 73.6 and the leader is GPT-5.1 Max at 91.1.
Score distribution
The chart shows the top 10 models on TruthfulQA, with Llama 3.1 Cerebras Chat highlighted. Longer bars mean higher scores.
Top 10 leaderboard
| # | Model | Provider | Context | Input / 1M | Avg 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 Cerebras Chat sits at #241 of 600. The leader, GPT-5.1 Max, scores 91.1, so the gap to the top is 12.7 points. The median model in this index scores 76.3, which puts Llama 3.1 Cerebras Chat above the midpoint.
Cerebras models average 70.4 on this benchmark. Llama 3.1 Cerebras Chat is 8.0 points above that provider line, so it is a stronger result than the average Cerebras model.
Among multimodal models, the average is 80.1. Llama 3.1 Cerebras Chat is 1.7 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 78.4 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 $4.07 per million input tokens, Llama 3.1 Cerebras 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.
| Model | Provider | Context | Input / 1M | Avg score |
|---|---|---|---|---|
| ERNIE 4.0 Pro | Baidu | 200000 | $12.58 | 77.5 |
| MiniMax-Text Instruct | MiniMax | 128000 | $4.59 | 77.7 |
| Gemini 2.5 Pro Mini | Google DeepMind | 128000 | $10.07 | 74.2 |
| Llama 3.1 | Meta AI | 200000 | $4.16 | 76.3 |
| Llama 3.1 Cerebras Chat | Cerebras | 32768 | $4.07 | 76.0 |
| Yi-Large Chat | 01.AI | 65536 | $3.49 | 73.5 |
| Command R+ Instruct | Cohere | 200000 | $4.16 | 76.2 |
Other benchmarks for Llama 3.1 Cerebras Chat
| Benchmark | Score | Rank | Percentile | Relative |
|---|---|---|---|---|
| MMLU-Pro | 79.6 | 191 | 68th | |
| GPQA Diamond | 69.7 | 275 | 54th | |
| SWE-bench Verified | 64.6 | 219 | 64th | |
| HumanEval | 82.5 | 175 | 71th | |
| MATH-500 | 77.4 | 270 | 55th | |
| AIME 2025 | 74.1 | 177 | 71th | |
| LiveCodeBench | 68.3 | 221 | 63th | |
| IFEval | 83.5 | 229 | 62th | |
| HellaSwag | 84.7 | 260 | 57th | |
| MMMU | 73.1 | 218 | 64th | |
| Arena Elo | 1391.6 | 243 | 60th |
FAQ
What score does Llama 3.1 Cerebras Chat get on TruthfulQA?
Llama 3.1 Cerebras Chat scores 78.4 on TruthfulQA in this index. The benchmark average is 73.6 and the top score is 91.1.
How does Llama 3.1 Cerebras Chat rank on TruthfulQA?
It ranks #241 of 600 models, in the 60th percentile. The leader is GPT-5.1 Max at 91.1.
Is Llama 3.1 Cerebras 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 Llama 3.1 Cerebras Chat cost?
Llama 3.1 Cerebras Chat costs $4.07 per million input tokens and $16.82 per million output tokens.