[whatmodel.sh](../../README.md) / GPT-3.5 Turbo (1106) # GPT-3.5 Turbo (1106) OpenAI · released 2023-11 · proprietary **score 47.6** ±0.6 · comparison only: no current run option verified · from 12 benchmark results across 8 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 (1106) among every model on that task or test, worst to best. 
task            vs the others          rank  counts  tests
coding          ▁▁▁▃▄▆●▇▅▄▃▁    #333 of 539     47%      3
chat & writing  ▁▁▁▃▅●▇█▇▇▇▃    #375 of 474     31%      6
math            ▁▁▃●██▅█▃▁▁▁   #435 of 530*    4.9%      1
coding · #333 of 539 · 47% of its score Beats [Qwen3-VL 32B Instruct](qwen3-vl-32b-instruct.md), [Solar Open 100B (102B-A12B)](solar-open-100b.md), [GLM-4.7-Flash](glm-4-7-flash.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
HumanEval    76.8%  ▁·▁▁▃▄▆▅●▃▁▁ beats 77/99  easy · avg 76%     23%  sharp · at its level
HumanEval+   70.7%  ▁·▁▁▁▃▄▄▄●▃▁ beats 72/99  easy · avg 70%     20%  sharp · at its level
MBPP+        69.7%  ▁···▁▁▂▅▇█●▂ beats 64/75  easy · avg 66%    4.0%  at its level
chat & writing · #375 of 474 · 31% of its score Beats [Gemma 4 E4B](gemma-4-e4b.md), [OpenThinker3 7B](openthinker3-7b.md) here, though they rank higher overall. Trails [LFM2 350M](lfm2-350m.md), [Gemma 3 1B](gemma-3-1b.md), [Qwen3 0.6B](qwen3-0-6b.md) here, though they rank lower overall. 
test                                                      result  vs the others in its run   difficulty                counts  why
AlpacaEval 2 length-controlled                             19.3%  ▁▂▂▃▆●█▆▄▄▁▁ beats 51/122  medium · avg 35%             13%  sharp · at its level
Arena-Hard (v0.1, GPT-4-1106 judg…                         18.9%  ▂▁▁▅●▅▄▃▃▅▃▁ beats 13/60   medium · avg 47%            8.3%  sharp · at its level
LMArena (text)                                            1204.3  ▁▁▁▂▃●▃▆▆▇█▃ beats 71/385  avg 1260                    3.6%  blunt
Polyrating rating (bias-aware re-…                        1106.0  ▂▁▂▁▃▇●▆▇▄▂▂ beats 50/95   avg 1187                    3.3%  typical
MT-Bench (GPT-4 single-answer gra…  8.3200 MT-Bench score (1-10)  ▆▂▂▆▆▄██▂▄●▆ beats 24/28   avg 8.539 MT-Bench scor…    2.5%  sharp · noisy
AlpacaEval 1.0 win rate (vs text-…                         86.3%  ▂▂·▆·▇▃●█▆▄▂ beats 24/45   very easy · avg 94%         0.3%  sharp · noisy
math · #435 of 530 · 4.9% of its score Beats [GPT-3.5 Turbo (0125)](gpt-3-5-turbo-0125.md) here, though they rank higher overall. Trails [OPT-2.7B](opt-2-7b.md), [Gemma 2B (base)](gemma-2b-base.md), [Stable Code 3B](stable-code-3b.md) here, though they rank lower overall. 
test          result  vs the others in its run  difficulty      counts  why
MATH level 5   15.9%  ▂▂●▆▄█▆▃▅▂▃▄ beats 14/92  hard · avg 28%    4.9%  sharp
other tests (2) 
test                  result  vs the others in its run   difficulty        counts  why
LiveBench 2024-07-26   34.1%  ▁▁▁▂▂▃▂█●▃▅▃ beats 34/61   medium · avg 38%     14%  at its level
GPQA Diamond           28.0%  ▁▁▁●▂▄▆▄▅▇█▅ beats 13/211  hard · avg 39%      2.8%  sharp
## How the score was worked out Each result implies a score on its own, after adjusting for how hard that test is and who ran it. The model's score is their weighted average, with a small pull toward the middle for thinly tested models. Each test's weight is its *counts* column above; here it is added up by who ran the tests. [Method](../method.md) 
source                                    results  share of the score
EvalPlus / EvalPerf published evaluatio…        3   47% ▇▇▇▇▇▇
LiveBench model judgments                       1   14% ▇▇
AlpacaEval 1.0 and 2.0 leaderboards             2   13% ▇▇
Arena-Hard-Auto v0.1 leaderboard                1    8% ▇
Epoch AI                                        2    8% ▇
LMArena leaderboard dataset                     1    4% ▇
Polyrating: A Cost-Effective and Bias-A…        1    3% ▇
LMSYS Chatbot Arena leaderboard Space: …        1    3% ▇
prior: pull toward the middle                        0% ▇
weighted average                                   = 47.6
Licences: EvalPlus / EvalPerf published evaluation runs ([Apache-2.0](../../licenses/evalplus-published-runs-Apache-2.0.txt)) · LiveBench model judgments ([Apache-2.0 (stated in the LiveBench datasheet)](https://www.apache.org/licenses/LICENSE-2.0)) · AlpacaEval 1.0 and 2.0 leaderboards ([Apache-2.0](https://www.apache.org/licenses/LICENSE-2.0)) · Arena-Hard-Auto v0.1 leaderboard ([Apache-2.0](../../licenses/arena-hard-auto-v0-1-Apache-2.0.txt)) · Epoch AI ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · 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)) ## How to run it No verified API or local download yet. [interactive version](https://whatmodel.sh/model/gpt-3-5-turbo-1106) · [JSON](https://whatmodel.sh/api?format=json&all=1)