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OpenAI Proprietary Released Jan 14, 2026 Updated

GPT-5.2 Codex

ToolsGuide Scores, rankings, test results and price.

Model summary

  • Coding #104 / 147

    265 / 1,000

    ToolsGuide Score· votes 243, tests 301

  • Price per 1M tokens

    In $1.75· Out $14.00

    $1.75

    Input · median coding model $0.75

Summary

GPT-5.2 Codex is OpenAI's model, released Jan 14, 2026. It's ranked in 1 category and does best at coding: #104 of 147, with a ToolsGuide Score of 265 against a median of 316.

It costs $1.75 per million input tokens and $14.00 per million output tokens, about 2.3× the median coding model ($0.75).

It has published results on 3 tests.

Specifications

Creator
OpenAI
Licence
Proprietary
Released
Jan 14, 2026
Context window
400K tokens
Input price
$1.75 / 1M
Output price
$14.00 / 1M
Tests reported
3
Settings ranked
GPT-5.2-Codex

Model comparison

Against the ten best coding models.

Coding score

ToolsGuide Score out of 1,000 · Higher is better

Input price

USD per 1M input tokens · Lower is better

Leaderboards

Where GPT-5.2 Codex sits among the top 20 in each category.

Coding

#104 of 147 · ToolsGuide Score out of 1,000, where 1,000 is a flawless model

Full coding leaderboard →

By task

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

Score against price

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

Coding

GPT-5.2-Codex · hover a bar for its rank

Test results

Every test GPT-5.2 Codex has a published result on, out of 100%, with its rank among the models that took it.

  • SWE-Bench Pro

    #36 / 61

    Harder, longer software engineering tasks from real codebases.

    56.4%

    Self-reported · In the coding score

  • Terminal-Bench 2.0

    #18 / 54

    Getting real work done in a command-line terminal.

    64.0%

    Self-reported

  • LiveBench

    #17 / 38

    LiveBench is a challenging, contamination-limited LLM benchmark that addresses test set contamination by releasing new questions monthly based on recently-released datasets, arXiv papers, news articles, and IMDb movie synopses. It comprises tasks across math, coding, reasoning, language, instruction following, and data analysis with verifiable, objective ground-truth answers.

    74.3%

    Self-reported · In the text score