Knowledge benchmark

# TriviaQA leaderboard

> TriviaQA results for 25 AI models, led by Llama 2-70B at 87.6%. What the benchmark measures, who runs it, and a source for every score.
- Canonical page: https://noometry.com/benchmarks/triviaqa
- Last updated: 2026-10-10
- Title: TriviaQA Leaderboard (October 2026): Scores by Model

As of October 2026, Llama 2-70B has the highest published TriviaQA score on Noometry at 87.6%, out of 25 models with results.

Last verified October 10, 2026

## About TriviaQA

Trivia questions paired with evidence documents.

- **Category:** [Knowledge](https://noometry.com/best/knowledge)
- **Introduced:** 2017
- **Format:** Short answer
- **Unit:** Percent (random guessing ≈ 0%)
- **Official site:** [nlp.cs.washington.edu](https://nlp.cs.washington.edu/triviaqa/)

## Top 15 models

Top models on TriviaQA

1.  Llama 2-70B 87.6%
2.  Claude 2 87.5%
3.  Claude 1.3 86.7%
4.  GPT-3.5-turbo 85.8%
5.  GPT-4 84.8%
6.  Llama 2-34B 84.6%
7.  DeepSeek-V3 82.9%
8.  Llama 3.1-405B 82.7%
9.  Mixtral 8x7B 82.2%
10.  DeepSeek-V2 (MoE-236B, May 2024) 80%
11.  Falcon-40B 79.9%
12.  Llama 2-13B 79.6%
13.  Claude Instant 78.9%
14.  Llama 13b 77.9%
15.  Mistral 7B 75.2%
16.  7075808590

Sponsored placements are available on pages like this one. [Advertise on Noometry](https://noometry.com/advertise)

## All results

TriviaQA results by model
| # | Model | Provider | Score | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- | --- |
| 1 | [Llama 2-70B](https://noometry.com/models/llama-2-70b) |  [![](/logos/meta.svg) Meta](https://noometry.com/providers/meta) | 87.6% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| 2 | [Claude 2](https://noometry.com/models/claude-2) | [Anthropic](https://noometry.com/providers/anthropic) | 87.5% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| 3 | [Claude 1.3](https://noometry.com/models/claude-1-3) | [Anthropic](https://noometry.com/providers/anthropic) | 86.7% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| 4 | [GPT-3.5-turbo](https://noometry.com/models/gpt-3-5-turbo) | [OpenAI](https://noometry.com/providers/openai) | 85.8% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| 5 | [GPT-4](https://noometry.com/models/gpt-4) | [OpenAI](https://noometry.com/providers/openai) | 84.8% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| 6 | [Llama 2-34B](https://noometry.com/models/llama-2-34b) |  [![](/logos/meta.svg) Meta](https://noometry.com/providers/meta) | 84.6% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| 7 | [DeepSeek-V3](https://noometry.com/models/deepseek-v3) |  [![](/logos/deepseek.svg) DeepSeek](https://noometry.com/providers/deepseek) | 82.9% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| 8 | [Llama 3.1-405B](https://noometry.com/models/llama-3-1-405b) |  [![](/logos/meta.svg) Meta](https://noometry.com/providers/meta) | 82.7% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| 9 | [Mixtral 8x7B](https://noometry.com/models/mixtral-8x7b) |  [![](/logos/mistral.svg) Mistral AI](https://noometry.com/providers/mistral) | 82.2% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| 10 | [DeepSeek-V2 (MoE-236B, May 2024)](https://noometry.com/models/deepseek-v2) |  [![](/logos/deepseek.svg) DeepSeek](https://noometry.com/providers/deepseek) | 80% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| 11 | [Falcon-40B](https://noometry.com/models/falcon-40b) |  [![](/logos/tii.svg) Technology Innovation Institute](https://noometry.com/providers/tii) | 79.9% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| 12 | [Llama 2-13B](https://noometry.com/models/llama-2-13b) |  [![](/logos/meta.svg) Meta](https://noometry.com/providers/meta) | 79.6% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| 13 | [Claude Instant](https://noometry.com/models/claude-instant) | [Anthropic](https://noometry.com/providers/anthropic) | 78.9% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| 14 | [Llama 13b](https://noometry.com/models/llama-13b) |  [![](/logos/meta.svg) Meta](https://noometry.com/providers/meta) | 77.9% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| 15 | [Mistral 7B](https://noometry.com/models/mistral-7b) |  [![](/logos/mistral.svg) Mistral AI](https://noometry.com/providers/mistral) | 75.2% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| 16 | [phi-3-medium 14B](https://noometry.com/models/phi-3-medium-14b) |  [![](/logos/microsoft.svg) Microsoft](https://noometry.com/providers/microsoft) | 73.9% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| 17 | [Llama 2-7B](https://noometry.com/models/llama-2-7b) |  [![](/logos/meta.svg) Meta](https://noometry.com/providers/meta) | 73.7% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| 18 | [Gemma 7B](https://noometry.com/models/gemma-7b) |  [![](/logos/google.svg) Google](https://noometry.com/providers/google) | 72.3% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| 19 | [Qwen2.5 72B Instruct](https://noometry.com/models/qwen2-5-72b-instruct) |  [![](/logos/alibaba.svg) Alibaba (Qwen)](https://noometry.com/providers/alibaba) | 71.9% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| 20 | [Llama 3-8B](https://noometry.com/models/llama-3-8b) |  [![](/logos/meta.svg) Meta](https://noometry.com/providers/meta) | 67.7% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| 21 | [Falcon-7B](https://noometry.com/models/falcon-7b) |  [![](/logos/tii.svg) Technology Innovation Institute](https://noometry.com/providers/tii) | 64.6% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| 22 | [Phi 3 Mini 4k Instruct](https://noometry.com/models/phi-3-mini-4k-instruct) |  [![](/logos/microsoft.svg) Microsoft](https://noometry.com/providers/microsoft) | 64% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| 23 | [Phi 3 Small 8k Instruct](https://noometry.com/models/phi-3-small-8k-instruct) |  [![](/logos/microsoft.svg) Microsoft](https://noometry.com/providers/microsoft) | 58.1% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| 24 | [Gemma 2B](https://noometry.com/models/gemma-2b) |  [![](/logos/google.svg) Google](https://noometry.com/providers/google) | 53.2% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| 25 | [Phi-2](https://noometry.com/models/phi-2) |  [![](/logos/microsoft.svg) Microsoft](https://noometry.com/providers/microsoft) | 45.2% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |

## Compare the leaders

-   [Llama 2-70B vs Claude 2](https://noometry.com/compare/claude-2-vs-llama-2-70b)
-   [Llama 2-70B vs Claude 1.3](https://noometry.com/compare/claude-1-3-vs-llama-2-70b)
-   [Llama 2-70B vs GPT-3.5-turbo](https://noometry.com/compare/gpt-3-5-turbo-vs-llama-2-70b)
-   [Llama 2-70B vs GPT-4](https://noometry.com/compare/gpt-4-vs-llama-2-70b)
-   [Claude 2 vs Claude 1.3](https://noometry.com/compare/claude-1-3-vs-claude-2)
-   [Claude 2 vs GPT-3.5-turbo](https://noometry.com/compare/claude-2-vs-gpt-3-5-turbo)

## Other knowledge benchmarks

-   [GPQA Diamond](https://noometry.com/benchmarks/gpqa-diamond)
-   [Humanity's Last Exam](https://noometry.com/benchmarks/hle)
-   [SimpleQA Verified](https://noometry.com/benchmarks/simpleqa-verified)
-   [MMLU-Pro](https://noometry.com/benchmarks/mmlu-pro)
-   [Confabulations](https://noometry.com/benchmarks/confabulations)
-   [Vectara Hallucination Rate](https://noometry.com/benchmarks/vectara-hallucination)
-   [LMArena Expert](https://noometry.com/benchmarks/arena-expert)
-   [GPQA (HELM)](https://noometry.com/benchmarks/helm-gpqa)
-   [ARC (AI2) Challenge](https://noometry.com/benchmarks/arc-challenge) (reference)
-   [BoolQ](https://noometry.com/benchmarks/boolq) (reference)
-   [MMLU](https://noometry.com/benchmarks/mmlu) (reference)
-   [OpenBookQA](https://noometry.com/benchmarks/openbookqa) (reference)

## Frequently asked questions

### What does TriviaQA measure?

Trivia questions paired with evidence documents.

### Which model has the highest TriviaQA score?

As of October 2026, Llama 2-70B has the highest published TriviaQA score on Noometry at 87.6%, out of 25 models with results.

### What is the best open-weight model on TriviaQA?

Llama 2-70B has the highest TriviaQA accuracy among open-weight models at 87.6%, ranking 1 of 25 overall.

### Cite this page

Noometry. (2026). TriviaQA leaderboard. Retrieved October 10, 2026, from https://noometry.com/benchmarks/triviaqa

Quote Noometry with a link back to this page. It is also available in [Markdown](https://noometry.com/md/benchmarks/triviaqa.md).