[whatmodel.sh](../../README.md) / GPT-5.3-Codex # GPT-5.3-Codex OpenAI · released 2026-02 · closed weights **score 72.7** ±1.3 · #52 of 732 · from 8 benchmark results across 4 sources Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only. ## What it's good at ● marks GPT-5.3-Codex among every model on that task or test, worst to best. 
task         vs the others        rank  counts  tests
agent tasks  ▁▁▃▆█▄▅▅●▄▂▁   #48 of 313     99%      8
coding       ▁▁▁▃▅▇█▇▅●▃▁   #55 of 469     46%      3
web search   ▁▁▁▅▄●▃▄·▁·▁   #37 of 84*     17%      1
agent tasks · #48 of 313 · 99% of its score Beats [Gemini 3.1 Pro (Preview)](gemini-3-1-pro.md), [Qwen3.8-Omni-Flash](qwen3-8-omni-flash.md), [Qwen3.8-Flash-Next](qwen3-8-flash-next.md) here, though they rank higher overall. Trails [Claude Sonnet 4.6](claude-sonnet-4-6.md), [GLM-5.2](glm-5-2.md), [GLM-5.3-Flash](glm-5-3-flash.md) here, though they rank lower overall. 
test                                result  vs the others in its run  difficulty            counts  why
SWE-bench Verified                   74.8%  ▁··▁·▁▂▁▄●▄▁ beats 17/29  hard · avg 19%           33%  sharp
BrowseComp (maker)                   77.3%  ●··█▄··▄·▄█▄ beats 0/8    medium · avg 38%         17%  typical
Toolathlon (Tool Decathlon) (make…   51.9%  ▃··▃··▃··●█▅ beats 3/8    very hard · avg 13%      12%  at its level
Terminal-Bench 2.0 (maker)           77.3%  ██···██··█●█ beats 5/6    hard · avg 15%           11%  at its level
Vibe Code Bench (test split, 50 w…   61.8%  ▃··▃·▃██▃▃▃● beats 14/14  very hard · avg 2.8%    8.5%  sharp · at its level · noisy
OSWorld-Verified (maker)             64.7%  █▄·····●·▄▄▄ beats 3/6    hard · avg 16%          8.2%  at its level
KlinikeBench strict pass@1 (333 i…    3.6%  ▁▁·▁▁●▁▅▃█▅█ beats 4/27   very hard · avg 2.4%    4.9%  typical
SWE-bench Pro (maker)                56.8%  ▄·····▄·▄▄●█ beats 4/6    hard · avg 29%          4.8%  blunt · at its level
coding · #55 of 469 · 46% of its score Beats [GPT-5.4](gpt-5-4.md), [Gemini 3.1 Pro (Preview)](gemini-3-1-pro.md), [DeepSeek V4 Flash Vision (Exp)](deepseek-v4-flash-vision-exp.md) here, though they rank higher overall. Trails [Qwen3.7 Plus](qwen3-7-plus.md), [Claude Sonnet 4.6](claude-sonnet-4-6.md), [Kimi K2.6](kimi-k2-6.md) here, though they rank lower overall. 
test                                result  vs the others in its run  difficulty            counts  why
SWE-bench Verified                   74.8%  ▁··▁·▁▂▁▄●▄▁ beats 17/29  hard · avg 19%           33%  sharp
Vibe Code Bench (test split, 50 w…   61.8%  ▃··▃·▃██▃▃▃● beats 14/14  very hard · avg 2.8%    8.5%  sharp · at its level · noisy
SWE-bench Pro (maker)                56.8%  ▄·····▄·▄▄●█ beats 4/6    hard · avg 29%          4.8%  blunt · at its level
web search · #37 of 84 · 17% of its score Beats [Gemini 3.1 Pro (Preview)](gemini-3-1-pro.md) here, though they rank higher overall. Trails [GPT-4o mini (2024-07-18)](gpt-4o-mini-2024-07-18.md), [Mistral Small 3.1 24B (2503)](mistral-small-3-1.md), [Llama 4 Scout (109B-A17B)](llama-4-scout.md) here, though they rank lower overall. 
test                result  vs the others in its run  difficulty        counts  why
BrowseComp (maker)   77.3%  ●··█▄··▄·▄█▄ beats 0/8    medium · avg 38%     17%  typical
## 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
OpenAI vendor-reported benchmark scores…        5   53% ▇▇▇▇▇▇
Epoch AI                                        1   33% ▇▇▇▇
Vibe Code Bench                                 1    9% ▇
KlinikeBench: Evaluating Language Model…        1    5% ▇
prior: pull toward the middle                        1% ▇
weighted average                                   = 72.7
Licences: Epoch AI ([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/)) · KlinikeBench: Evaluating Language Models Beyond Diagnostic Accuracy ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) ## How to run it ### Through an API api (OpenAI) · $4.81/M blended · $1.75 in / $14 out per 1M tokens · [platform.openai.com](https://platform.openai.com/docs/models) [price source](https://github.com/anomalyco/models.dev/blob/a668908cfe7073faa0363699486fc41a3ff52412/providers/openai/models/gpt-5.3-codex.toml): Standard base token rate from models.dev. Provider-specific tiers, long-context surcharges, caching and promotions may differ. · [context source](https://github.com/anomalyco/models.dev/blob/a668908cfe7073faa0363699486fc41a3ff52412/models/openai/gpt-5.3-codex.toml) [interactive version](https://whatmodel.sh/model/gpt-5-3-codex) · [JSON](https://whatmodel.sh/api?format=json&all=1)