[whatmodel.sh](../../README.md) / GPT-3.5 Turbo (0613)
# GPT-3.5 Turbo (0613)
OpenAI · released 2023-06 · proprietary
**score 47.2** ±0.7 · comparison only: no current run option verified · from 18 benchmark results across 11 sources
Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only.
## What it's good at
● marks GPT-3.5 Turbo (0613) among every model on that task or test, worst to best.
task vs the others rank counts tests
coding ▁▁▁▃▄▆●▇▅▄▃▁ #335 of 539 18% 4
chat & writing ▁▁▁▃▅●▇█▇▇▇▃ #358 of 474 42% 4
math ▁▁▃▆█●▅█▃▁▁▁ #225 of 530* 4.0% 1
coding · #335 of 539 · 18% of its score
Beats [Qwen3-VL 32B Instruct](qwen3-vl-32b-instruct.md), [Solar Open 100B (102B-A12B)](solar-open-100b.md), [Qwen3-VL 30B-A3B Instruct](qwen3-vl-30b-a3b-instruct.md) here, though they rank higher overall. Trails [DeepSeek LLM 67B Chat](deepseek-llm-67b-chat.md), [Mistral NeMo Instruct (2407)](mistral-nemo-instruct-2407.md), [QwQ-32B-Preview](qwq-32b-preview.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
Aider code editing (133 Exercism … 50.4% ▁·▁▂▄▁●▄█▆▂▂ beats 14/62 medium · avg 51% 11% sharp · at its level
Tulu 2: CodexEval pass@10 88.0% ▃▃▃·▆▃▃█▃▂▂● beats 24/25 easy · avg 83% 2.7% sharp
CRUXEval-I 49.0% ▁··▁▅▂█▇●··▁ beats 21/22 medium · avg 52% 2.1% at its level
CRUXEval-O 49.4% ▁·▂▃▁█▁●···▁ beats 20/22 medium · avg 51% 2.0% blunt · at its level
chat & writing · #358 of 474 · 42% of its score
Beats [Ministral 3 14B Reasoning 2512](ministral-3-14b-reasoning.md), [Gemma 4 E4B](gemma-4-e4b.md), [OpenThinker3 7B](openthinker3-7b.md) here, though they rank higher overall. Trails [EXAONE 3.5 2.4B Instruct](exaone-3-5-2-4b.md), [LFM2 700M](lfm2-700m.md), [Flan-T5 XXL](flan-t5-xxl.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
AlpacaEval 2 length-controlled 22.4% ▁▂▂▃▆▆●▆▄▄▁▁ beats 57/122 medium · avg 35% 20% sharp · at its level
Arena-Hard (v0.1, GPT-4-1106 judg… 24.8% ▂▁▁▅●▅▄▃▃▅▃▁ beats 24/60 medium · avg 47% 13% sharp · at its level
Polyrating rating (bias-aware re-… 1161.0 ▂▁▂▁▃▇█▆●▄▂▂ beats 70/95 avg 1187 5.2% typical
MT-Bench (GPT-4 single-answer gra… 8.3900 MT-Bench score (1-10) ▆▂▂▆▆▄██▂▄●▆ beats 25/28 avg 8.539 MT-Bench scor… 4.0% sharp · noisy
math · #225 of 530 · 4.0% of its score
Beats [MiniMax-M2.1](minimax-m2-1.md), [DeepSeek V3 (Dec '24)](deepseek-v3.md), [Qwen3 235B-A22B (Apr 2025, hybrid)](qwen3-235b-a22b.md) here, though they rank higher overall. Trails [Mixtral 8x7B v0.1 (base)](mixtral-8x7b-v0-1.md), [Phi-4-mini-reasoning 3.8B](phi-4-mini-reasoning.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
GSM8K 76.5% ▆▂▅█▅▅▃●···▂ beats 24/25 easy · avg 72% 4.0% sharp · at its level
other tests (9)
test result vs the others in its run difficulty counts why
CLIcK Korean (Daekeun Kim) 51.0% ▆●▄▄█▄▂·▄█·▄ beats 3/23 medium · avg 56% 10% at its level
HAE-RAE 1.0 (Daekeun Kim) 52.7% ▄●▄▂▂█▂▄▂█▂▄ beats 2/22 medium · avg 61% 8.5% at its level
BIG-Bench Hard 70.8% ▁·····▂█▂●·▁ beats 24/25 easy · avg 74% 8.1% sharp · at its level
MMLU 65.7% ▁▁▃▃▄█▄▁●··▁ beats 21/25 medium · avg 73% 3.6% at its level
EQ-Bench v1 (60 questions, paper … 49.2% ▂··▄·▂▆▄▂●·▂ beats 10/14 medium · avg 53% 2.5% at its level
API-Bank evaluation system, zero-… 47.2% ▄······█··●▄ beats 3/4 medium · avg 50% 1.6% at its level
KMMLU-HARD (Daekeun Kim, zero-sho… 21.0% ●···▁▁▁▂▃·▆▂ beats 0/23 very hard · avg 29% 1.0% sharp · too hard for it
Tulu 2: TyDiQA gold passage, 1-sh… 51.2% ▂··▁▃▂▂▂●▅▁▂ beats 13/25 medium · avg 59% 0.8% blunt · at its level
FANToM BeliefQ [Choice], short co… 53.5% █·····●····▁ beats 9/11 very hard · avg 55% 0.4% sharp · too hard for it · noisy
source results share of the score
AlpacaEval 1.0 and 2.0 leaderboards 1 20% ▇▇
Tulu 2 5 19% ▇▇
Daekeun Kim Korean LLM benchmark runs 3 19% ▇▇
Arena-Hard-Auto v0.1 leaderboard 1 13% ▇▇
Aider code editing benchmark runs 1 11% ▇
Polyrating: A Cost-Effective and Bias-A… 1 5% ▇
CRUXEval published evaluation results 2 4% ▇
LMSYS Chatbot Arena leaderboard Space: … 1 4% ▇
EQ-Bench: An Emotional Intelligence Ben… 1 2% ▇
API-Bank: A Comprehensive Benchmark for… 1 2% ▇
FANToM: A Benchmark for Stress-testing … 1 0% ▇
prior: pull toward the middle 0% ▇
weighted average = 47.2
Licences: AlpacaEval 1.0 and 2.0 leaderboards ([Apache-2.0](https://www.apache.org/licenses/LICENSE-2.0)) · Tulu 2 ([CC BY 4.0](../../licenses/CC-BY-4.0.txt)) · Daekeun Kim Korean LLM benchmark runs ([MIT](../../licenses/korean-personal-benchmark-runs-MIT.txt)) · Arena-Hard-Auto v0.1 leaderboard ([Apache-2.0](../../licenses/arena-hard-auto-v0-1-Apache-2.0.txt)) · Aider code editing benchmark runs ([Apache-2.0](../../licenses/aider-polyglot-runs-Apache-2.0.txt)) · Polyrating: A Cost-Effective and Bias-Aware Rating System for LLM Evaluation ([CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/)) · CRUXEval published evaluation results ([MIT](../../licenses/cruxeval-published-results-MIT.txt)) · 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)) · API-Bank: A Comprehensive Benchmark for Tool-Augmented LLMs ([CC BY 4.0](../../licenses/acl-2023-emnlp-main-187-CC-BY-4-0.txt)) · FANToM: A Benchmark for Stress-testing Machine Theory of Mind in Interactions ([CC BY 4.0](../../licenses/acl-2023-emnlp-main-890-CC-BY-4-0.txt))
## How to run it
No verified API or local download yet.
[interactive version](https://whatmodel.sh/model/gpt-3-5-turbo-0613) · [JSON](https://whatmodel.sh/api?format=json&all=1)