[whatmodel.sh](../../README.md) / Koala 13B
# Koala 13B
UC Berkeley (BAIR) · released 2023-04 · 13B params · koala-academic-research-only
**score 31.8** ±1.1 · comparison only: no current run option verified · from 12 benchmark results across 7 sources
Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only.
## What it's good at
● marks Koala 13B among every model on that task or test, worst to best.
task vs the others rank counts tests
coding ▁▁●▃▄▆█▇▅▄▃▁ #522 of 539* 13% 1
chat & writing ▁▁●▃▅▄▇█▇▇▇▃ #456 of 474 31% 3
coding · #522 of 539 · 13% of its score
Beats [Molmo 7B-D (0924)](molmo-7b-d.md), [DeepSeek R1 Distill Qwen 1.5B](deepseek-r1-distill-qwen-1-5b.md), [Qwen3 0.6B](qwen3-0-6b.md) here, though they rank higher overall. Trails [Falcon3 1B Instruct](falcon3-1b.md), [Gemma 2B IT](gemma-2b.md), [Granite 3.0 1B-A400M Instruct](granite-3-0-1b-a400m.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
HumanEval 11.0% ▄··▄····▄▄●▄ beats 5/6 medium · avg 63% 13% sharp
chat & writing · #456 of 474 · 31% of its score
Beats [Phi-2 (2.7B)](phi-2.md) here, though they rank higher overall. Trails [Gemma 3 270M IT](gemma-3-270m.md), [SmolLM2 360M Instruct](smollm2-360m.md), [Granite 4.0 350M](granite-4-0-350m.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
LMArena (text) 1071.0 ▁▁●▂▃▃▃▆▆▇█▃ beats 12/385 avg 1260 12% blunt
Polyrating rating (bias-aware re-… 967.0 ▂▁▂●▃▇█▆▇▄▂▂ beats 11/95 avg 1187 11% typical
MT-Bench (GPT-4 single-answer gra… 5.3500 MT-Bench score (1-10) ▆▂▂▆●▄██▂▄▄▆ beats 10/28 avg 8.539 MT-Bench scor… 8.4% sharp · noisy
other tests (7)
test result vs the others in its run difficulty counts why
MMLU 44.6% ▄▄·▄···▄▄·●█ beats 5/8 easy · avg 79% 19% at its level · several runs
BIG-Bench Hard 34.6% ███··●·█·█·█ beats 3/6 medium · avg 68% 15% sharp
ARC Challenge 52.9% ▄·▄···●·▄··▄ beats 3/5 easy · avg 86% 11% sharp · at its level
HellaSwag 77.5% ▄·▄●··▄····▄ beats 3/5 very easy · avg 92% 5.0% at its level
EQ-Bench v1 (60 questions, paper … 24.9% ▂··●·▂▆▄▂█·▂ beats 1/14 medium · avg 53% 4.0% typical
This is not a Dataset (negation),… 52.8% █▁····●▁·▁·▁ beats 11/14 hard · avg 66% 1.2% sharp · too hard for it · noisy
TruthfulQA MC2 50.1% ▃···█·▃····● beats 5/5 medium · avg 56% 0.4% blunt · at its level
source results share of the score
InstructEval 3 35% ▇▇▇▇
Enhancing Chat Language Models by Scali… 4 28% ▇▇▇
LMArena leaderboard dataset 1 12% ▇
Polyrating: A Cost-Effective and Bias-A… 1 11% ▇
LMSYS Chatbot Arena leaderboard Space: … 1 8% ▇
EQ-Bench: An Emotional Intelligence Ben… 1 4% ▇
This is not a Dataset: A Large Negation… 1 1% ▇
prior: pull toward the middle 1% ▇
weighted average = 31.8
Licences: InstructEval ([MIT / Apache-2.0](https://github.com/declare-lab/instruct-eval/blob/49bbd2ce7e3a5bdc4024ba62a5c9c68c107ee966/LICENSE)) · Enhancing Chat Language Models by Scaling High-quality Instructional Conversations ([CC BY 4.0](../../licenses/acl-2023-emnlp-main-183-CC-BY-4-0.txt)) · LMArena leaderboard dataset ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · Polyrating: A Cost-Effective and Bias-Aware Rating System for LLM Evaluation ([CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/)) · LMSYS Chatbot Arena leaderboard Space: MT-Bench column of the published leaderboard table ([Apache-2.0](https://www.apache.org/licenses/LICENSE-2.0)) · EQ-Bench: An Emotional Intelligence Benchmark for Large Language Models ([CC BY 4.0](../../licenses/paper-2312.06281v2-CC-BY-4.0.txt)) · This is not a Dataset: A Large Negation Benchmark to Challenge Large Language Models ([CC BY 4.0](../../licenses/acl-2023-emnlp-main-531-CC-BY-4-0.txt))
## How to run it
No verified API or local download yet.
Model repository and documentation: [huggingface.co/young-geng](https://huggingface.co/young-geng/koala)
[interactive version](https://whatmodel.sh/model/koala-13b) · [JSON](https://whatmodel.sh/api?format=json&all=1)