[whatmodel.sh](../../README.md) / GPT-4 (0314)
# GPT-4 (0314)
OpenAI · released 2023-03 · proprietary
**score 53.8** ±0.5 · comparison only: no current run option verified · from 40 benchmark results across 27 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 (0314) among every model on that task or test, worst to best.
task vs the others rank counts tests
coding ▁▁▁▃▄▆●▇▅▄▃▁ #255 of 539 32% 4
chat & writing ▁▁▁▃▅▄●█▇▇▇▃ #302 of 474 18% 5
math ▁▁▃▆●█▅█▃▁▁▁ #394 of 530 6.9% 3
coding · #255 of 539 · 32% of its score
Beats [Qwen3.5 4B](qwen3-5-4b.md), [Qwen3-VL 32B Instruct](qwen3-vl-32b-instruct.md), [Solar Open 100B (102B-A12B)](solar-open-100b.md) here, though they rank higher overall. Trails [QwQ-32B-Preview](qwq-32b-preview.md), [Ministral 3 3B Reasoning 2512](ministral-3-3b-reasoning.md), [s1.1 32B](s1-1-32b.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
HumanEval 88.4% ▁·▁▁▃▄▆▅█●▁▁ beats 93/99 easy · avg 76% 11% sharp
HumanEval+ 79.3% ▁·▁▁▁▃▄▄▄█●▁ beats 90/99 easy · avg 70% 10% sharp
CRUXEval (ZeroEval, 800 problems) 74.5% ▁▂▆▃▆█●▃▂·▁▁ beats 35/47 medium · avg 57% 6.2% sharp · at its level
Aider code editing (133 Exercism … 66.2% ▁·▁▂▄▁▆▄●▆▂▂ beats 40/62 medium · avg 51% 4.7% sharp · at its level
chat & writing · #302 of 474 · 18% of its score
Beats [Mistral Small 4 (119B-A6.5B)](mistral-small-4.md), [Qwen2.5 Coder 32B Instruct](qwen2-5-coder-32b.md), [Ministral 3 14B Reasoning 2512](ministral-3-14b-reasoning.md) here, though they rank higher overall. Trails [Flan-T5 XXL](flan-t5-xxl.md), [Granite 4.0 H 1B](granite-4-0-h-1b.md), [Granite 4.0 1B](granite-4-0-1b.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
AlpacaEval 2 length-controlled 35.3% ▁▂▂▃▆▆█●▄▄▁▁ beats 91/122 medium · avg 35% 11% sharp · at its level
LMArena (text) 1287.9 ▁▁▁▂▃▃●▆▆▇█▃ beats 118/385 avg 1260 2.6% blunt
Polyrating rating (bias-aware re-… 1213.0 ▂▁▂▁▃▇█▆▇●▂▂ beats 85/95 avg 1187 2.3% typical
MT-Bench (GPT-4 single-answer gra… 8.9600 MT-Bench score (1-10) ▆▂▂▆▆▄██▂▄▄● beats 26/28 avg 8.539 MT-Bench scor… 1.8% sharp · noisy
AlpacaEval 1.0 win rate (vs text-… 94.8% ▂▂·▆·▇▃▆█▆●▂ beats 40/45 very easy · avg 94% 0.1% sharp · maxed out · noisy
math · #394 of 530 · 6.9% of its score
Trails [Stable Code 3B](stable-code-3b.md), [Qwen3 0.6B](qwen3-0-6b.md), [Falcon 40B (base)](falcon-40b.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
MATH Level 5 (ZeroEval, 721 probl… 26.1% ▁▁▁▆●▂▇▇▄··▂ beats 15/38 hard · avg 30% 3.4% sharp · at its level
GSM8K 94.6% ▂▂▂▂▃▄▅▇██▆● beats 196/206 very easy · avg 85% 2.4% sharp · small run · several runs
OTIS mock AIME 2024–2025 0.6% ●▃▄▃▃▃▇▇█▃▂▅ beats 3/197 very hard · avg 7.2% 1.1% sharp · noisy
other tests (26)
test result vs the others in its run difficulty counts why
MMLU-Redux (ZeroEval, 2,778 quest… 81.6% ▁·▁▁▄▄▄▅●▄▂▁ beats 34/53 easy · avg 75% 9.4% typical
ZebraLogic (ZeroEval, 1,000 puzzl… 27.1% ▁▁▃▆█●▁▁·▁▁▁ beats 43/57 hard · avg 15% 4.5% sharp
New Yorker Caption Contest matchi… 84.5% █·····▃··▅·● beats 6/6 medium · avg 71% 3.8% sharp · at its level
AQuA-RAT 66.9% █··▁▁▁··▁▁▁● beats 27/27 medium · avg 58% 3.7% sharp · at its level · small run
GPQA Diamond 35.7% ▁▁▁▁▂●▆▄▅▇█▅ beats 29/211 hard · avg 39% 3.5% sharp · at its level
SVAMP 91.9% █▆▆▅▂▅▂▅▂▂▅● beats 30/30 very easy · avg 85% 2.8% sharp · small run · several runs
F2WTQ free-form table QA, 2-shot … 63.6% ▄···█·▄█··▄● beats 7/7 medium · avg 56% 2.3% at its level
EQ-Bench 3 (emotional intelligenc… 984.7 ▁·●▁▄▅▅▃█▄▁▁ beats 2/62 avg 1034 1.6% typical
DMath (English) few-shot CoT accu… 75.2% █·▄······▄·● beats 4/4 medium · avg 64% 1.5% at its level
clembench v1.0 clemscore (self-pl… 59.5% ▄······▄·█·● beats 4/4 medium · avg 38% 1.3% sharp · at its level
EQ-Bench v1 (60 questions, paper … 53.4% ▂··▄·▂▆▄▂●·▂ beats 13/14 medium · avg 53% 1.1% at its level
Integer-sequence explanation as P… 59.1% █······█··█● beats 3/3 medium · avg 47% 1.1% at its level
JEEBench total score (zero-shot, … 30.9% █··█····█··● beats 3/3 very hard · avg 20% 1.0% typical
Integer-sequence completion, unam… 78.6% ▄········▄·● beats 3/3 easy · avg 73% 0.9% typical
Conic10K mathQA (zero-shot CoT) 15.5% █··▄·······● beats 3/3 very hard · avg 10% 0.7% typical
ZeroSCROLLS Qasper (zero-shot F1) 50.7% ▂▂··▂·▂··▂●▄ beats 7/10 medium · avg 56% 0.6% blunt · at its level
CoNLL-2003 English NER, few-shot … 84.7% ▃···▅·▃▅··▃● beats 9/9 easy · avg 83% 0.5% small run
ZeroSCROLLS QuALITY (zero-shot ac… 89.2% ▃····▃·▃▃█▅● beats 10/10 easy · avg 81% 0.5% typical
COPA 98.1% ▁·▁▁▁▅▅█▆▂▁● beats 67/67 very easy · avg 96% 0.4% maxed out
RadNLI (3-class radiology NLI), z… 87.8% █·█········● beats 2/2 easy · avg 65% 0.4% at its level · small run
ZeroSCROLLS NarrativeQA (zero-sho… 27.6% ▃··▃·█▃·█●·▃ beats 9/10 hard · avg 29% 0.4% blunt · at its level
SelfAware unanswerable-question d… 75.5% █▄▄▄·······● beats 5/5 medium · avg 63% 0.3% blunt · at its level
FANToM BeliefQ [Choice], short co… 39.0% ●·····▂····▁ beats 0/11 very hard · avg 55% 0.3% sharp · noisy
ConvRe Re2Text, normal relation, … 98.7% ▂▂▄··▂▂█▄··● beats 12/12 easy · avg 90% 0.2% noisy
CAIL charge prediction, zero-shot… 55.2% █···█··█···● beats 3/3 medium · avg 54% 0.2% blunt · at its level
ZeroSCROLLS MuSiQue (zero-shot F1) 41.1% ▄·█··██·●▄▄▄ beats 7/10 medium · avg 43% 0.1% blunt · at its level
source results share of the score
WildEval ZeroEval published results 4 23% ▇▇▇
EvalPlus / EvalPerf published evaluatio… 2 21% ▇▇▇
AlpacaEval 1.0 and 2.0 leaderboards 2 11% ▇
Aider code editing benchmark runs 1 5% ▇
Epoch AI 2 5% ▇
It Ain't Over: A Multi-aspect Diverse M… 3 4% ▇
Do Androids Laugh at Electric Sheep? Hu… 1 4% ▇
Unnatural Error Correction: GPT-4 Can A… 1 4% ▇
LMArena leaderboard dataset 1 3% ▇
Automatic Model Selection with Large La… 2 2% ▇
Polyrating: A Cost-Effective and Bias-A… 1 2% ▇
Investigating Table-to-Text Generation … 1 2% ▇
Self-Consistency of Large Language Mode… 2 2% ▇
LMSYS Chatbot Arena leaderboard Space: … 1 2% ▇
ZeroSCROLLS: A Zero-Shot Benchmark for … 4 2% ▇
EQ-Bench 3 canonical leaderboard results 1 2% ▇
clembench: Using Game Play to Evaluate … 1 1% ▇
EQ-Bench: An Emotional Intelligence Ben… 1 1% ▇
Have LLMs Advanced Enough? A Challengin… 1 1% ▇
Conic10K: A Challenging Math Problem Un… 1 1% ▇
Large Language Model Is Not a Good Few-… 1 1% ▇
Is ChatGPT a Good Causal Reasoner? A Co… 1 0% ▇
Exploring the Boundaries of GPT-4 in Ra… 1 0% ▇
Do Large Language Models Know What They… 1 0% ▇
FANToM: A Benchmark for Stress-testing … 1 0% ▇
An Investigation of LLMs' Inefficacy in… 1 0% ▇
A Comprehensive Evaluation of Large Lan… 1 0% ▇
prior: pull toward the middle 0% ▇
weighted average = 53.8
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)) · AlpacaEval 1.0 and 2.0 leaderboards ([Apache-2.0](https://www.apache.org/licenses/LICENSE-2.0)) · Aider code editing benchmark runs ([Apache-2.0](../../licenses/aider-polyglot-runs-Apache-2.0.txt)) · Epoch AI ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · It Ain't Over: A Multi-aspect Diverse Math Word Problem Dataset ([CC BY 4.0](../../licenses/acl-2023-emnlp-main-927-CC-BY-4-0.txt)) · Do Androids Laugh at Electric Sheep? Humor \`\`Understanding'' Benchmarks from The New Yorker Caption Contest ([CC BY 4.0](../../licenses/acl-2023-acl-long-41-CC-BY-4-0.txt)) · Unnatural Error Correction: GPT-4 Can Almost Perfectly Handle Unnatural Scrambled Text ([CC BY 4.0](../../licenses/acl-2023-emnlp-main-550-CC-BY-4-0.txt)) · LMArena leaderboard dataset ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · Automatic Model Selection with Large Language Models for Reasoning ([CC BY 4.0](../../licenses/acl-2023-findings-emnlp-55-CC-BY-4-0.txt)) · Polyrating: A Cost-Effective and Bias-Aware Rating System for LLM Evaluation ([CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/)) · Investigating Table-to-Text Generation Capabilities of Large Language Models in Real-World Information Seeking Scenarios ([CC BY 4.0](../../licenses/acl-2023-emnlp-industry-17-CC-BY-4-0.txt)) · Self-Consistency of Large Language Models under Ambiguity ([CC BY 4.0](../../licenses/acl-2023-blackboxnlp-1-7-CC-BY-4-0.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)) · ZeroSCROLLS: A Zero-Shot Benchmark for Long Text Understanding ([CC BY 4.0](../../licenses/acl-2023-findings-emnlp-536-CC-BY-4-0.txt)) · EQ-Bench 3 canonical leaderboard results ([MIT](../../licenses/eqbench3-MIT.txt)) · clembench: Using Game Play to Evaluate Chat-Optimized Language Models as Conversational Agents ([CC BY 4.0](../../licenses/acl-2023-emnlp-main-689-CC-BY-4-0.txt)) · EQ-Bench: An Emotional Intelligence Benchmark for Large Language Models ([CC BY 4.0](../../licenses/paper-2312.06281v2-CC-BY-4.0.txt)) · Have LLMs Advanced Enough? A Challenging Problem Solving Benchmark For Large Language Models ([CC BY 4.0](../../licenses/acl-2023-emnlp-main-468-CC-BY-4-0.txt)) · Conic10K: A Challenging Math Problem Understanding and Reasoning Dataset ([CC BY 4.0](../../licenses/acl-2023-findings-emnlp-427-CC-BY-4-0.txt)) · Large Language Model Is Not a Good Few-shot Information Extractor ([CC BY 4.0](../../licenses/acl-2023-findings-emnlp-710-CC-BY-4-0.txt)) · Is ChatGPT a Good Causal Reasoner? A Comprehensive Evaluation ([CC BY 4.0](../../licenses/acl-2023-findings-emnlp-743-CC-BY-4-0.txt)) · Exploring the Boundaries of GPT-4 in Radiology ([CC BY 4.0](../../licenses/acl-2023-emnlp-main-891-CC-BY-4-0.txt)) · Do Large Language Models Know What They Don't Know? ([CC BY 4.0](../../licenses/acl-2023-findings-acl-551-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)) · An Investigation of LLMs' Inefficacy in Understanding Converse Relations ([CC BY 4.0](../../licenses/acl-2023-emnlp-main-429-CC-BY-4-0.txt)) · A Comprehensive Evaluation of Large Language Models on Legal Judgment Prediction ([CC BY 4.0](../../licenses/acl-2023-findings-emnlp-490-CC-BY-4-0.txt))
## How to run it
No verified API or local download yet.
[interactive version](https://whatmodel.sh/model/gpt-4-0314) · [JSON](https://whatmodel.sh/api?format=json&all=1)