Skip to content

OpenAI Proprietary Released Mar 21, 2023 Updated

GPT-3.5 Turbo

ToolsGuide Scores, rankings, test results and price.

Model summary

  • Text #329 / 416

    136 / 1,000

    ToolsGuide Score· votes 141, tests 132

  • Price per 1M tokens

    In $0.50· Out $1.50

    $0.50

    Input · median text model $0.43

Summary

GPT-3.5 Turbo is OpenAI's model, released Mar 21, 2023. It's ranked in 1 category and does best at text: #329 of 416, with a ToolsGuide Score of 136 against a median of 235.

It costs $0.50 per million input tokens and $1.50 per million output tokens, close to the median text model ($0.43).

It has published results on 8 tests.

Specifications

Creator
OpenAI
Licence
Proprietary
Released
Mar 21, 2023
Context window
16K tokens
Input price
$0.50 / 1M
Output price
$1.50 / 1M
Tests reported
8
Settings ranked
gpt-3.5-turbo-0125

Model comparison

Against the ten best text models.

Text score

ToolsGuide Score out of 1,000 · Higher is better

Input price

USD per 1M input tokens · Lower is better

Leaderboards

Where GPT-3.5 Turbo sits among the top 20 in each category.

Text

#329 of 416 · ToolsGuide Score out of 1,000, where 1,000 is a flawless model

Full text leaderboard →

By task

Scored from votes on each kind of prompt alone, on the same 1,000-point scale.

Score against price

Text · the 10 priced models ranked closest · up and to the left is better value

Against its nearest rivals

Text prompts by kind · ToolsGuide Score from votes · hover for values

Text

gpt-3.5-turbo-0125 · hover a bar for its rank

Test results

Every test GPT-3.5 Turbo has a published result on, out of 100%, with its rank among the models that took it.

  • GPQA

    #243 / 256

    Graduate-level science questions written to be hard to look up.

    30.8%

    Self-reported

  • MMLU

    #85 / 103

    Massive Multitask Language Understanding benchmark testing knowledge across 57 diverse subjects including STEM, humanities, social sciences, and professional domains

    69.8%

    Self-reported

  • MATH

    #63 / 71

    MATH dataset contains 12,500 challenging competition mathematics problems from AMC 10, AMC 12, AIME, and other mathematics competitions. Each problem includes full step-by-step solutions and spans multiple difficulty levels (1-5) across seven mathematical subjects including Prealgebra, Algebra, Number Theory, Counting and Probability, Geometry, Intermediate Algebra, and Precalculus.

    43.1%

    Self-reported

  • HumanEval

    #58 / 66

    A benchmark that measures functional correctness for synthesizing programs from docstrings, consisting of 164 original programming problems assessing language comprehension, algorithms, and simple mathematics

    68.0%

    Self-reported

  • MMMU

    #66 / 66

    MMMU (Massive Multi-discipline Multimodal Understanding) is a benchmark designed to evaluate multimodal models on college-level subject knowledge and deliberate reasoning. Contains 11.5K meticulously collected multimodal questions from college exams, quizzes, and textbooks, covering six core disciplines: Art & Design, Business, Science, Health & Medicine, Humanities & Social Science, and Tech & Engineering across 30 subjects and 183 subfields.

    0.0%

    Self-reported

  • MathVista

    #40 / 40

    MathVista evaluates mathematical reasoning of foundation models in visual contexts. It consists of 6,141 examples derived from 28 existing multimodal datasets and 3 newly created datasets (IQTest, FunctionQA, and PaperQA), combining challenges from diverse mathematical and visual tasks to assess models' ability to understand complex figures and perform rigorous reasoning.

    0.0%

    Self-reported

  • MGSM

    #28 / 31

    MGSM (Multilingual Grade School Math) is a benchmark of grade-school math problems. Contains 250 grade-school math problems manually translated from the GSM8K dataset into ten typologically diverse languages: Spanish, French, German, Russian, Chinese, Japanese, Thai, Swahili, Bengali, and Telugu. Evaluates multilingual mathematical reasoning capabilities.

    56.3%

    Self-reported

  • DROP

    #21 / 30

    DROP (Discrete Reasoning Over Paragraphs) is a reading comprehension benchmark requiring discrete reasoning over paragraph content. It contains crowdsourced, adversarially-created questions that require resolving references and performing discrete operations like addition, counting, or sorting, demanding comprehensive paragraph understanding beyond paraphrase-and-entity-typing shortcuts.

    70.2%

    Self-reported