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OpenAI Proprietary Released Mar 4, 2026 Updated

GPT-5.3 Chat

ToolsGuide Scores, rankings, test results and price.

Model summary

  • Text #78 / 416

    418 / 1,000

    ToolsGuide Score· votes only

  • Price per 1M tokens

    In $1.75· Out $14.00

    $1.75

    Input · median text model $0.43

Summary

GPT-5.3 Chat is OpenAI's model, released Mar 4, 2026. It's ranked in 1 category and does best at text: #78 of 416, with a ToolsGuide Score of 418 against a median of 235.

Its text score uses votes only, because it has published fewer than two of the tests used for that category.

It costs $1.75 per million input tokens and $14.00 per million output tokens, about 4.0× the median text model ($0.43).

It has published results on 2 tests.

Specifications

Creator
OpenAI
Licence
Proprietary
Released
Mar 4, 2026
Context window
128K tokens
Input price
$1.75 / 1M
Output price
$14.00 / 1M
Tests reported
2
Settings ranked
gpt-5.3-chat-latest

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-5.3 Chat sits among the top 20 in each category.

Text

#78 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-5.3-chat-latest · hover a bar for its rank

Test results

Every test GPT-5.3 Chat has a published result on, out of 100%, with its rank among the models that took it.

  • HealthBench Hard

    #9 / 24

    A challenging variation of HealthBench that evaluates large language models' performance and safety in healthcare through 5,000 multi-turn conversations with particularly rigorous evaluation criteria validated by 262 physicians from 60 countries

    25.9%

    Self-reported

  • HealthBench

    #11 / 14

    An open-source benchmark for measuring performance and safety of large language models in healthcare, consisting of 5,000 multi-turn conversations evaluated by 262 physicians using 48,562 unique rubric criteria across health contexts and behavioral dimensions

    54.1%

    Self-reported