[whatmodel.sh](../../README.md) / GPT-3.5 Turbo (0125)
# GPT-3.5 Turbo (0125)
OpenAI · released 2024-01 · proprietary
**score 47.8** ±0.5 · #411 of 732 · from 35 benchmark results across 17 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 (0125) among every model on that task or test, worst to best.
task vs the others rank counts tests
agent tasks ▁▁▃●█▄▅▅▄▄▂▁ #256 of 313* 0.5% 1
coding ▁▁▁▃▅▇●▇▅▄▃▁ #277 of 469 21% 3
chat & writing ▁▁▁▃▆●▆█▆▇▇▃ #299 of 402 12% 5
math ▁▁▃●█▆▄█▄▂▁▁ #373 of 431 13% 7
agent tasks · #256 of 313 · 0.5% of its score
Beats [DeepSeek R1 0528 Qwen3 8B](deepseek-r1-0528-qwen3-8b.md), [Sky-T1 32B Preview](sky-t1-32b-preview.md), [Magistral Small 1.0 (2506)](magistral-small-2506.md) here, though they rank higher overall. Trails [Granite 4.0 1B](granite-4-0-1b.md), [SmolLM3 3B](smollm3-3b.md), [Qwen3-VL 2B Instruct](qwen3-vl-2b-instruct.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
BFCL v3 52.5% ▁▁▁▁▁▃▅●▅▄▁▁ beats 47/114 medium · avg 51% 0.5% blunt · at its level
coding · #277 of 469 · 21% 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 38/42 easy · avg 75% 10% sharp · at its level
CRUXEval (ZeroEval, 800 problems) 54.8% ▁▂▆▃●█▆▃▂·▁▁ beats 15/47 medium · avg 57% 6.9% sharp · at its level
Aider code editing (133 Exercism … 50.4% ▁·▁▂▄▁●▄█▆▂▂ beats 15/62 medium · avg 51% 4.7% sharp · at its level
chat & writing · #299 of 402 · 12% 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
Arena-Hard (v0.1, GPT-4-1106 judg… 23.3% ▂▁▁▅●▅▄▃▃▅▃▁ beats 20/60 medium · avg 47% 5.7% sharp · at its level
LMArena (text) 1225.6 ▁▁▁▂▃●▃▆▆▇█▃ beats 80/385 avg 1260 2.5% blunt
Polyrating rating (bias-aware re-… 1147.0 ▂▁▂▁▃▇█▆●▄▂▂ beats 64/95 avg 1187 2.3% typical
AlpacaEval 2 length-controlled 38.7% ▃▃·▁▁█●▅·▁·▁ beats 13/22 medium · avg 39% 1.5% sharp · at its level
IFEval prompt-level loose 66.9% ▁▃▁▃●▄▁▁█▃▃▁ beats 6/22 easy · avg 78% 0.5% typical
math · #373 of 431 · 13% of its score
Trails [OPT-1.3B](opt-1-3b.md), [OPT-2.7B](opt-2-7b.md), [Gemma 2B (base)](gemma-2b-base.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
GSM8K (ZeroEval, zero-shot) 80.4% ▁▄·▁▄●▅▅▅███ beats 12/43 very easy · avg 87% 3.7% sharp
MATH level 5 11.6% ▂▂●▆▄█▆▃▅▂▃▄ beats 8/92 hard · avg 28% 3.5% sharp
MATH Level 5 (ZeroEval, 721 probl… 13.7% ▁▁●▆█▂▇▇▄··▂ beats 2/38 hard · avg 30% 2.4% sharp
GSM8K 74.3% ▂▃··▃●▅█▆▃▂▃ beats 5/22 very easy · avg 88% 1.6% sharp
MATH (subset unspecified) 41.2% ▃▃●▂▂▆·····▁ beats 10/22 medium · avg 58% 0.9% at its level
FrontierMath tiers 1–3 v2 0.0% ●··▁▁▃▄▄█▆▄▃ beats 1/86 very hard · avg 1.2% 0.4% sharp · too hard for it
OTIS mock AIME 2024–2025 2.2% ▃●▄▃▃▃▇▇█▃▂▅ beats 17/197 very hard · avg 7.2% 0.3% sharp · too hard for it · noisy
other tests (19)
test result vs the others in its run difficulty counts why
MMLU-Redux (ZeroEval, 2,778 quest… 68.4% ▁·▁▁▄●▄▅█▄▂▁ beats 13/53 easy · avg 75% 11% at its level
LiveBench 2024-07-26 34.5% ▁▁▁▂▂▃▂█●▃▅▃ beats 37/61 medium · avg 38% 9.4% at its level
EvoEval (overall) 53.1% ▂▂▃▄▄█▁▄▅●▁▃ beats 37/42 medium · avg 52% 7.0% at its level
MMLU 70.2% ▁▂·▁·▁▁●·▃▃▅ beats 10/22 easy · avg 75% 3.8% at its level
BIG-Bench Hard 66.6% ▁▄·▁▁·●█·▄▄▁ beats 9/22 medium · avg 72% 3.4% sharp · at its level
BigCodeBench (Instruct) 38.2% ▃▂·▆▆██●▅▃▃▃ beats 22/33 medium · avg 39% 2.8% at its level
BigCodeBench (Complete) 50.6% ▂▁▁▄▂▅▇█▆●▂▂ beats 51/58 medium · avg 48% 2.7% at its level
WildBench v2 WB-Score (task macro) 30.0160 WB-Score ▁·▁▁·▃●▄▇▆▅▆ beats 14/52 avg 43.019 WB-Score 2.6% sharp
ZebraLogic (ZeroEval, 1,000 puzzl… 10.1% ▁▁▃●██▁▁·▁▁▁ beats 10/57 hard · avg 15% 2.4% sharp
GPQA Diamond 27.2% ▁▁●▁▂▄▆▄▅▇█▅ beats 10/211 hard · avg 39% 2.0% sharp
BRIDGE clinical text, zero-shot a… 35.3% ▁▁▁▁▁▂▂▃▄●█▃ beats 47/97 hard · avg 31% 1.3% blunt · at its level
HindiGen-v1 3C3H score (Inception… 49.1% ▁▁▁▁·▂·●▆█▇▁ beats 6/31 medium · avg 46% 1.3% at its level
Open Persian LLM Leaderboard v2 a… 50.7% ▂▄▂▃●▄▃▆▃▅▂▂ beats 35/81 medium · avg 57% 1.2% blunt · at its level
PopQA 45.0% ▁▁▁▃▃▃▁█▁▃·● beats 19/21 hard · avg 31% 0.7% at its level
Open PT-LLM Leaderboard average (… 72.2% ▃▁▁▁▁▁▁▁▂●▅▁ beats 193/276 medium · avg 72% 0.6% at its level · noisy
Icelandic LLM Leaderboard average… 12.9% ▁▁●▂▂▄▁▃▄▄▄█ beats 2/38 very hard · avg 14% 0.4% typical
TruthfulQA MC2 62.9% ▃▃▂·▃▃·●█▂▅▂ beats 10/21 medium · avg 60% 0.1% blunt · at its level
Chess Puzzles (Epoch) 0.0% ●▁▁▁▁▂▃▃▃▂▁▁ beats 14/137 very hard · avg 5.8% 0.1% sharp · too hard for it · noisy
Mystery Game Puzzles (Epoch) 3.0% ●··▂▃▇▄█▂▄▂▁ beats 3/77 very hard · avg 9.4% 0.0% sharp · too hard for it · noisy
source results share of the score
WildEval ZeroEval published results 5 27% ▇▇▇
Top Leaderboard Ranking = Top Coding Pr… 2 17% ▇▇
2 OLMo 2 Furious 8 12% ▇
LiveBench model judgments 1 9% ▇
Epoch AI 6 6% ▇
Arena-Hard-Auto v0.1 leaderboard 1 6% ▇
BigCodeBench paper 2 6% ▇
Aider code editing benchmark runs 1 5% ▇
WildBench v2 leaderboard data 1 3% ▇
LMArena leaderboard dataset 1 2% ▇
Polyrating: A Cost-Effective and Bias-A… 1 2% ▇
BRIDGE Medical Leaderboard 1 1% ▇
Inception Arabic/Hindi leaderboards 1 1% ▇
Open Persian LLM Leaderboard v2 1 1% ▇
Open Portuguese LLM Leaderboard 1 1% ▇
Berkeley Function Calling Leaderboard r… 1 1% ▇
Miðeind Icelandic LLM Leaderboard 1 0% ▇
prior: pull toward the middle 0% ▇
weighted average = 47.8
Licences: WildEval ZeroEval published results ([Apache-2.0](../../licenses/zeroeval-published-results-Apache-2.0.txt)) · Top Leaderboard Ranking = Top Coding Proficiency ([CC BY 4.0](../../licenses/paper-2403.19114v1-CC-BY-4.0.txt)) · 2 OLMo 2 Furious ([CC BY 4.0](../../licenses/paper-2501.00656v3-CC-BY-4.0.txt)) · LiveBench model judgments ([Apache-2.0 (stated in the LiveBench datasheet)](https://www.apache.org/licenses/LICENSE-2.0)) · Epoch AI ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · Arena-Hard-Auto v0.1 leaderboard ([Apache-2.0](../../licenses/arena-hard-auto-v0-1-Apache-2.0.txt)) · BigCodeBench paper ([CC BY 4.0](http://creativecommons.org/licenses/by/4.0/)) · Aider code editing benchmark runs ([Apache-2.0](../../licenses/aider-polyglot-runs-Apache-2.0.txt)) · WildBench v2 leaderboard data ([Apache-2.0](https://github.com/allenai/WildBench/blob/d6b8dcaf377d173d031980f97c16e1a82618c03d/LICENSE)) · 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/)) · BRIDGE Medical Leaderboard ([MIT](../../licenses/bridge-medical-leaderboard-MIT.txt)) · Inception Arabic/Hindi leaderboards ([Apache-2.0](../../licenses/hf-leaderboard-spaces-Apache-2.0.txt)) · Open Persian LLM Leaderboard v2 ([Apache-2.0](../../licenses/hf-leaderboard-spaces-Apache-2.0.txt)) · Open Portuguese LLM Leaderboard ([Apache-2.0](../../licenses/hf-leaderboard-spaces-Apache-2.0.txt)) · Berkeley Function Calling Leaderboard result archive ([Apache-2.0](../../licenses/bfcl-result-archive-v4-Apache-2.0.txt)) · Miðeind Icelandic LLM Leaderboard ([CC BY 4.0](../../licenses/mideind-icelandic-llm-leaderboard-CC-BY-4.0.txt))
## How to run it
### Through an API
api (OpenAI) · $0.75/M blended · $0.5 in / $1.5 out per 1M tokens · [developers.openai.com](https://developers.openai.com/api/docs/models/gpt-3.5-turbo)
[price source](https://developers.openai.com/api/docs/models/gpt-3.5-turbo): USD per 1M tokens, OpenAI API standard processing, model page 'gpt-3.5-turbo' whose default snapshot is gpt-3.5-turbo-0125: input $0.5, output $1.5; cached-input, Batch and other tiers excluded. Currency: USD. OpenAI official model page, observed 2026-09-28. · [context source](https://developers.openai.com/api/docs/models/gpt-3.5-turbo): OpenAI model page for gpt-3.5-turbo (default snapshot gpt-3.5-turbo-0125): 16,385 context window. Observed 2026-09-28.
[interactive version](https://whatmodel.sh/model/gpt-3-5-turbo-0125) · [JSON](https://whatmodel.sh/api?format=json&all=1)