OpenAI Proprietary Released Apr 9, 2024 Updated
GPT-4 Turbo
ToolsGuide Scores, rankings, test results and price.
Model summary
Summary
GPT-4 Turbo is OpenAI's model, released Apr 9, 2024. It's ranked in 2 categories and does best at text: #256 of 416, with a ToolsGuide Score of 192 against a median of 235.
It also ranks #131 of 167 for vision (219).
Its vision score uses votes only, because it has published fewer than two of the tests used for that category.
It costs $10.00 per million input tokens and $30.00 per million output tokens, about 23× the median text model ($0.43).
It has published results on 6 tests.
Specifications
- Creator
OpenAI- Licence
- Proprietary
- Released
- Apr 9, 2024
- Context window
- 128K tokens
- Input price
- $10.00 / 1M
- Output price
- $30.00 / 1M
- Tests reported
- 6
- Settings ranked
- gpt-4-turbo-2024-04-09, GPT-4 Turbo (2024-04-09)
Model comparison
Against the ten best text models.
Text score
ToolsGuide Score out of 1,000 · Higher is better
Vision score
ToolsGuide Score out of 1,000 · Higher is better
Input price
USD per 1M input tokens · Lower is better
Leaderboards
Where GPT-4 Turbo sits among the top 20 in each category.
Text
#256 of 416 · ToolsGuide Score out of 1,000, where 1,000 is a flawless model
Vision
#131 of 167 · ToolsGuide Score out of 1,000, where 1,000 is a flawless model
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
-
GPT-4 Turbo -
claude-3-opus-20240229 -
deepseek-v2.5
Text
gpt-4-turbo-2024-04-09 · hover a bar for its rank
Vision
GPT-4 Turbo (2024-04-09) · hover a bar for its rank
Test results
Every test GPT-4 Turbo has a published result on, out of 100%, with its rank among the models that took it.
GPQA
#206 / 256Graduate-level science questions written to be hard to look up.
48.0%
Self-reported
MMLU
#30 / 103Massive Multitask Language Understanding benchmark testing knowledge across 57 diverse subjects including STEM, humanities, social sciences, and professional domains
86.5%
Self-reported
MATH
#33 / 71MATH 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.
72.6%
Self-reported
HumanEval
#30 / 66A benchmark that measures functional correctness for synthesizing programs from docstrings, consisting of 164 original programming problems assessing language comprehension, algorithms, and simple mathematics
87.1%
Self-reported
MGSM
#11 / 31MGSM (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.
88.5%
Self-reported
DROP
#5 / 30DROP (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.
86.0%
Self-reported