[whatmodel.sh](../../README.md) / GPT-4 Turbo (2024-04-09)
# GPT-4 Turbo (2024-04-09)
OpenAI · released 2024-04 · proprietary
**score 56.3** ±0.4 · #257 of 732 · from 49 benchmark results across 24 sources
Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only.
## What it's good at
● marks GPT-4 Turbo (2024-04-09) among every model on that task or test, worst to best.
task vs the others rank counts tests
agent tasks ▁▁▃▆█●▅▅▄▄▂▁ #177 of 313* 0.7% 2
coding ▁▁▁▃▅▇█●▅▄▃▁ #196 of 469 23% 7
chat & writing ▁▁▁▃▆▄▆●▆▇▇▃ #193 of 402 16% 4
math ▁▁▃▅●▆▄█▄▂▁▁ #278 of 431 11% 7
vision & documents ▁▁▂▂▁●▄▂▆▇█▄ #121 of 153 9.1% 12
agent tasks · #177 of 313 · 0.7% of its score
Beats [o1 (2024-12-17)](o1-2024-12-17.md), [o3-mini](o3-mini.md), [DeepSeek R1 0528](deepseek-r1-0528.md) here, though they rank higher overall. Trails [SmolLM3 3B](smollm3-3b.md), [Jamba 1.5 Mini](jamba-1-5-mini.md), [Jamba 1.5 Large](jamba-1-5-large.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
BFCL v3 67.9% ▁▁▁▁▁▃▅█▅●▁▁ beats 103/114 medium · avg 51% 0.4% blunt · at its level
BFCL (version not stated) 81.2% █·········●█ beats 3/5 easy · avg 77% 0.3% blunt
coding · #196 of 469 · 23% of its score
Beats [Gemini 2.5 Flash](gemini-2-5-flash.md), [Qwen3 30B-A3B Instruct 2507](qwen3-30b-a3b-instruct-2507.md), [Qwen3.5 4B](qwen3-5-4b.md) here, though they rank higher overall. Trails [QwQ-32B-Preview](qwq-32b-preview.md), [Ministral 3 8B Reasoning 2512](ministral-3-8b-reasoning.md), [OpenThinker 32B](openthinker-32b.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
HumanEval 90.2% ▁·▁▁▃▄▆▅█▃●▁ beats 96/99 easy · avg 76% 6.4% sharp
HumanEval+ 86.6% ▁·▁▁▁▃▄▄▄█▃● beats 96/99 easy · avg 70% 6.3% sharp
CRUXEval (ZeroEval, 800 problems) 78.9% ▁▂▆▃▆█▆●▂·▁▁ beats 40/47 medium · avg 57% 4.0% sharp
Aider code editing (133 Exercism … 63.9% ▁·▁▂▄▁▆▄●▆▂▂ beats 33/62 medium · avg 51% 3.2% sharp · at its level
LiveCodeBench official (Aug 1, 20… 28.7% ▃▃●▃··▃▅█▅▅▃ beats 2/15 hard · avg 24% 1.2% sharp · at its level
SciCode main problems (official l… 1.5% ▃···●··▂·▃·▂ beats 8/17 very hard · avg 1.0% 0.9% sharp · too hard for it
HumanEval (simple-evals) (maker) 88.2% ▆●▆▆▂··▂···▂ beats 4/15 easy · avg 83% 0.6% typical
chat & writing · #193 of 402 · 16% of its score
Beats [Seed-OSS 36B Instruct](seed-oss-36b.md), [Gemma 2 9B IT SimPO](gemma-2-9b-simpo.md), [Nemotron-Cascade-14B-Thinking](nemotron-cascade-14b-thinking.md) here, though they rank higher overall. Trails [LFM2.5 1.2B Instruct](lfm2-5-1-2b-instruct.md), [LFM2.5 1.2B Thinking](lfm2-5-1-2b-thinking.md), [LFM2.5 8B-A1B](lfm2-5-8b-a1b.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
AlpacaEval 2 length-controlled 55.0% ▁▂▂▃▆▆█▆▄●▁▁ beats 114/122 medium · avg 35% 8.4% sharp · at its level
Arena-Hard (v0.1, GPT-4-1106 judg… 82.6% ▂▁▁▅█▅▄▃▃▅●▁ beats 55/60 medium · avg 47% 3.8% sharp · at its level
LMArena (text) 1324.5 ▁▁▁▂▃▃▃●▆▇█▃ beats 161/385 avg 1260 1.9% blunt
Polyrating rating (bias-aware re-… 1258.0 ▂▁▂▁▃▇█▆▇▄●▂ beats 91/95 avg 1187 1.7% typical
math · #278 of 431 · 11% of its score
Trails [DeepSeek R1 Distill Qwen 1.5B](deepseek-r1-distill-qwen-1-5b.md), [Gemma 7B (base)](gemma-7b-base.md), [CodeGemma 7B (v1.0)](codegemma-7b.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
MATH level 5 46.7% ▂▂▅▆▄●▆▃▅▂▃▄ beats 39/92 hard · avg 28% 4.1% sharp · at its level
MATH Level 5 (ZeroEval, 721 probl… 46.5% ▁▁▁▆█▂●▇▄··▂ beats 23/38 hard · avg 30% 2.6% sharp · at its level
FrontierMath tiers 1–3 v2 0.7% ●··▁▁▃▄▄█▆▄▃ beats 3/86 very hard · avg 1.2% 1.5% sharp · too hard for it
OTIS mock AIME 2024–2025 6.7% ▃▃●▃▃▃▇▇█▃▂▅ beats 39/197 very hard · avg 7.2% 1.2% sharp · noisy
MATH (simple-evals, original test… 73.4% █····▄··●··█ beats 3/5 medium · avg 62% 0.7% at its level
MGSM (simple-evals) (maker) 89.6% ▁····▁▅▁●▁▃▁ beats 8/16 easy · avg 80% 0.7% typical
MathVista (testmini) 47.5% ▁··▁▄●▆▇▄▅█▄ beats 6/48 medium · avg 49% 0.4% at its level · small run
vision & documents · #121 of 153 · 9.1% of its score
Beats [DeepSeek V4 Flash Vision (Exp)](deepseek-v4-flash-vision-exp.md) here, though they rank higher overall. Trails [Qwen3.5 2B](qwen3-5-2b.md), [Qwen3-VL 4B Instruct](qwen3-vl-4b-instruct.md), [Gemma 4 E4B](gemma-4-e4b.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
CharXiv descriptive questions 79.9% ▄█▄▄▄·██·●·█ beats 10/12 easy · avg 69% 1.7% typical
CharXiv (reasoning questions) 37.1% ▂▂▂█▃●·▂▂··▂ beats 10/13 hard · avg 34% 1.3% at its level
Arena vision (style controlled) 1111.9 ▁▁▂▂▂▂●▄▅▅█▇ beats 31/152 avg 1061 1.3% blunt
MMMU val (VHELM, zero-shot exact … 55.4% ▄▄··▄▄●··▄▄█ beats 4/9 medium · avg 50% 1.1% at its level
MathVista testmini (VHELM, zero-s… 49.3% ▃·▅···▃●▃·▃█ beats 4/9 medium · avg 42% 0.9% at its level
CharXiv descriptive questions (te… 79.8% █··▄··█··●·▄ beats 5/6 easy · avg 69% 0.9% typical
MMMU (val) 55.5% ▁····▁▁▃●▆▆▅ beats 20/53 hard · avg 48% 0.7% at its level · small run
CharXiv reasoning questions (test… 33.8% ▄··▄▄▄●····▄ beats 5/6 hard · avg 30% 0.6% at its level
MathVista (testmini) 47.5% ▁··▁▄●▆▇▄▅█▄ beats 6/48 medium · avg 49% 0.4% at its level · small run
TextVQA 68.1% ▁▁▁●▅▁▃·▃▃▃█ beats 5/24 easy · avg 75% 0.1% blunt · small run
ChartQA 62.3% ▁··▁▁▁●▁▂▅▅█ beats 4/44 easy · avg 78% 0.1% blunt · noisy · small run
AI2D test 74.7% ▁▁▅●▃▇·▄·█·▃ beats 6/28 easy · avg 82% 0.0% blunt · small run
other tests (18)
test result vs the others in its run difficulty counts why
LiveBench 2024-07-26 52.9% ▁▁▁▂▂▃▂█▆▃●▃ beats 56/61 medium · avg 38% 8.1% at its level
MMLU-Redux (ZeroEval, 2,778 quest… 85.3% ▁·▁▁▄▄▄▅█●▂▁ beats 43/53 easy · avg 75% 6.1% typical
ZebraLogic (ZeroEval, 1,000 puzzl… 28.4% ▁▁▃▆█●▁▁·▁▁▁ beats 46/57 hard · avg 15% 3.9% sharp · at its level
GPQA Diamond (simple-evals) (make… 49.3% █▃●█·▃▃▅▃▃▃▅ beats 4/16 hard · avg 39% 3.2% sharp · at its level
GPQA Diamond 46.6% ▁▁▁▁▂▄●▄▅▇█▅ beats 55/211 hard · avg 39% 3.0% sharp · at its level
CLIcK Korean (Daekeun Kim) 72.8% ▆▄▄▄●▄▂·▄█·▄ beats 11/23 medium · avg 56% 2.8% at its level
BigCodeBench (Instruct) 48.0% ▃▂·▆▆██▆▅▃▃● beats 32/33 medium · avg 39% 2.4% at its level
HAE-RAE 1.0 (Daekeun Kim) 77.8% ▄▄▄▂▂●▂▄▂█▂▄ beats 9/22 medium · avg 61% 2.3% at its level
BigCodeBench (Complete) 58.2% ▂▁▁▄▂▅▇█▆▂▂● beats 57/58 medium · avg 48% 2.1% at its level
WildBench v2 WB-Score (task macro) 55.2212 WB-Score ▁·▁▁·▃█▄▇▆▅● beats 47/52 avg 43.019 WB-Score 2.0% sharp
MMLU (simple-evals, zero-shot CoT… 86.7% ▂▂·█●▄▂·█▂·▂ beats 6/16 easy · avg 79% 1.4% typical
KMMLU-HARD (Daekeun Kim, zero-sho… 30.6% █···●▁▁▂▃·▆▂ beats 8/23 very hard · avg 29% 1.2% sharp
Polish EQ-Bench v2 (SpeakLeash) 77.8% ▁··▁▁▂▂▆▅▆█● beats 69/70 medium · avg 62% 0.9% at its level
Open Persian LLM Leaderboard v2 a… 70.0% ▂▄▂▃█▄▃▆▃●▂▂ beats 73/81 medium · avg 57% 0.8% blunt · at its level
Chess Puzzles (Epoch) 6.0% █●▁▁▁▂▃▃▃▂▁▁ beats 46/137 very hard · avg 5.8% 0.2% sharp · too hard for it · noisy
DROP F1, 3-shot (simple-evals) (m… 86.0% ▂▂▄▆▆█·●···▄ beats 14/16 easy · avg 78% 0.2% blunt
SimpleQA (simple-evals) (maker) 24.2% ▄·▁▁▁●··█▁·▁ beats 6/14 hard · avg 17% 0.2% noisy
Vectara hallucination leaderboard… 98.3% ▁▁▂▃▄▄█▆▃●▂▁ beats 127/140 very easy · avg 94% 0.1% blunt · maxed out · noisy
source results share of the score
WildEval ZeroEval published results 4 17% ▇▇
EvalPlus / EvalPerf published evaluatio… 2 13% ▇▇
Epoch AI 5 10% ▇
AlpacaEval 1.0 and 2.0 leaderboards 1 8% ▇
LiveBench model judgments 1 8% ▇
OpenAI simple-evals README benchmark re… 7 7% ▇
Daekeun Kim Korean LLM benchmark runs 3 6% ▇
BigCodeBench paper 2 4% ▇
Arena-Hard-Auto v0.1 leaderboard 1 4% ▇
Aider code editing benchmark runs 1 3% ▇
LMArena leaderboard dataset 2 3% ▇
CharXiv leaderboard 2 3% ▇
WildBench v2 leaderboard data 1 2% ▇
VHELM: A Holistic Evaluation of Vision … 2 2% ▇
Polyrating: A Cost-Effective and Bias-A… 1 2% ▇
CharXiv: Charting Gaps in Realistic Cha… 2 2% ▇
Phi-3 Technical Report 5 1% ▇
LiveCodeBench official website results 1 1% ▇
SciCode leaderboard 1 1% ▇
SpeakLeash Polish EQ-Bench leaderboard 1 1% ▇
Open Persian LLM Leaderboard v2 1 1% ▇
Berkeley Function Calling Leaderboard r… 1 0% ▇
ChatGLM: A Family of Large Language Mod… 1 0% ▇
Vectara hallucination leaderboard 1 0% ▇
prior: pull toward the middle 0% ▇
weighted average = 56.3
Licences: WildEval ZeroEval published results ([Apache-2.0](../../licenses/zeroeval-published-results-Apache-2.0.txt)) · EvalPlus / EvalPerf published evaluation runs ([Apache-2.0](../../licenses/evalplus-published-runs-Apache-2.0.txt)) · Epoch AI ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · AlpacaEval 1.0 and 2.0 leaderboards ([Apache-2.0](https://www.apache.org/licenses/LICENSE-2.0)) · LiveBench model judgments ([Apache-2.0 (stated in the LiveBench datasheet)](https://www.apache.org/licenses/LICENSE-2.0)) · OpenAI simple-evals README benchmark results ([MIT](../../licenses/openai-simple-evals-MIT.txt)) · Daekeun Kim Korean LLM benchmark runs ([MIT](../../licenses/korean-personal-benchmark-runs-MIT.txt)) · BigCodeBench paper ([CC BY 4.0](http://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)) · Aider code editing benchmark runs ([Apache-2.0](../../licenses/aider-polyglot-runs-Apache-2.0.txt)) · LMArena leaderboard dataset ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · CharXiv leaderboard ([Apache-2.0](https://www.apache.org/licenses/LICENSE-2.0)) · WildBench v2 leaderboard data ([Apache-2.0](https://github.com/allenai/WildBench/blob/d6b8dcaf377d173d031980f97c16e1a82618c03d/LICENSE)) · VHELM: A Holistic Evaluation of Vision Language Models ([CC BY 4.0](http://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/)) · CharXiv: Charting Gaps in Realistic Chart Understanding in Multimodal LLMs ([CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/)) · Phi-3 Technical Report ([CC BY 4.0](http://creativecommons.org/licenses/by/4.0/)) · LiveCodeBench official website results ([CC-BY-SA-4.0](../../licenses/lcb-sharealike-NOTICE.txt)) · SciCode leaderboard ([Apache-2.0](../../licenses/scicode-Apache-2.0.txt)) · SpeakLeash Polish EQ-Bench leaderboard ([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)) · Berkeley Function Calling Leaderboard result archive ([Apache-2.0](../../licenses/bfcl-result-archive-v4-Apache-2.0.txt)) · ChatGLM: A Family of Large Language Models from GLM-130B to GLM-4 All Tools ([CC BY 4.0](http://creativecommons.org/licenses/by/4.0/)) · Vectara hallucination leaderboard ([Apache-2.0](https://www.apache.org/licenses/LICENSE-2.0))
## How to run it
### Through an API
api (OpenAI) · $15.00/M blended · $10 in / $30 out per 1M tokens · [developers.openai.com](https://developers.openai.com/api/docs/models/gpt-4-turbo)
[price source](https://developers.openai.com/api/docs/models/gpt-4-turbo): USD per 1M tokens, OpenAI API standard processing, model page 'gpt-4-turbo' whose default snapshot is gpt-4-turbo-2024-04-09: input $10, output $30; 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-4-turbo): OpenAI model page for gpt-4-turbo (default snapshot gpt-4-turbo-2024-04-09): 128,000 context window. Observed 2026-09-28.
[interactive version](https://whatmodel.sh/model/gpt-4-turbo-2024-04-09) · [JSON](https://whatmodel.sh/api?format=json&all=1)