[whatmodel.sh](../../README.md) / GPT-5.2-Codex # GPT-5.2-Codex OpenAI · released 2026-01 · closed weights **score 67.1** ±1.7 · comparison only: no current run option verified · from 5 benchmark results across 3 sources Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only. ## What it's good at ● marks GPT-5.2-Codex among every model on that task or test, worst to best. 
task         vs the others         rank  counts  tests
agent tasks  ▁▁▂▆█▄▅▄●▃▁▁    #72 of 354     48%      4
coding       ▁▁▁▃▄▆█▇●▄▃▁   #138 of 539     68%      3
agent tasks · #72 of 354 · 48% of its score Beats [LongCat 2.0 (1.6T-A48B)](longcat-2-0.md), [GPT-5 Pro](gpt-5-pro.md), [GPT-5](gpt-5.md) here, though they rank higher overall. 
test                                result  vs the others in its run  difficulty            counts  why
Terminal-Bench 2.0 (maker)           64.0%  ██···█●··███ beats 3/6    hard · avg 15%           18%  at its level
OSWorld-Verified (maker)             38.2%  ●▄·····▄·▄▄▄ beats 0/6    hard · avg 16%           13%  at its level
Vibe Code Bench (test split, 50 w…   37.9%  ▃··▃·▃██▃●▃█ beats 10/14  very hard · avg 2.8%     10%  sharp · noisy
SWE-bench Pro (maker)                56.4%  ▄·····▄·▄●▄█ beats 3/6    hard · avg 29%          7.4%  blunt · at its level
coding · #138 of 539 · 68% of its score Beats [Qwen3.5 Plus](qwen3-5-plus.md) here, though they rank higher overall. Trails [Llama 3.1 Nemotron Ultra 253B v1](llama-3-1-nemotron-ultra-253b.md), [Solar Pro 4](solar-pro-4.md), [Laguna XS 2.1 (33B-A3B)](laguna-xs-2-1.md) here, though they rank lower overall. 
test                                result  vs the others in its run   difficulty            counts  why
Arena WebDev (Code Arena)           1338.7  ▁▁▃▂●█▃▆▅▅▁▂ beats 25/129  avg 981                  51%  sharp
Vibe Code Bench (test split, 50 w…   37.9%  ▃··▃·▃██▃●▃█ beats 10/14   very hard · avg 2.8%     10%  sharp · noisy
SWE-bench Pro (maker)                56.4%  ▄·····▄·▄●▄█ beats 3/6     hard · avg 29%          7.4%  blunt · at its level
## How the score was worked out Each result implies a score on its own, after adjusting for how hard that test is and who ran it. The model's score is their weighted average, with a small pull toward the middle for thinly tested models. Each test's weight is its *counts* column above; here it is added up by who ran the tests. [Method](../method.md) 
source                                    results  share of the score
LMArena leaderboard dataset                     1   51% ▇▇▇▇▇▇
OpenAI vendor-reported benchmark scores…        3   38% ▇▇▇▇▇
Vibe Code Bench                                 1   10% ▇
prior: pull toward the middle                        1% ▇
weighted average                                   = 67.1
Licences: LMArena leaderboard dataset ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · Vibe Code Bench ([CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/)) ## How to run it No verified API or local download yet. [interactive version](https://whatmodel.sh/model/gpt-5-2-codex) · [JSON](https://whatmodel.sh/api?format=json&all=1)