[whatmodel.sh](../../README.md) / GPT-5.5 Instant # GPT-5.5 Instant OpenAI · released 2026-05 · closed weights **score 67.9** ±0.9 · comparison only: no current run option verified · from 9 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-5.5 Instant among every model on that task or test, worst to best. 
task                vs the others         rank  counts  tests
chat & writing      ▁▁▁▃▅▄▇█▇▇●▃   #34 of 474*    9.4%      1
math                ▁▁▃▆██▅●▃▁▁▁   #145 of 530     59%      3
vision & documents  ▁▁▂▃▂▃▅▄▆█●▄    #29 of 189     15%      3
chat & writing · #34 of 474 · 9.4% of its score Beats [GPT-6 Sol](gpt-6-sol.md), [GPT-5.6 Terra](gpt-5-6-terra.md), [MiMo-V2.6-Flash (309B-A15B)](mimo-v2-6-flash.md) here, though they rank higher overall. 
test            result  vs the others in its run    difficulty  counts  why
LMArena (text)  1473.2  ▁▁▁▂▃▃▃▆▆▇●▃ beats 350/385  avg 1260      9.4%  blunt
math · #145 of 530 · 59% of its score Beats [Claude Sonnet 4.6](claude-sonnet-4-6.md), [MiniMax-M3](minimax-m3.md), [MiMo-V2.5 (310B-A15B)](mimo-v2-5.md) here, though they rank higher overall. Trails [Olmo 3 32B Think](olmo-3-32b-think.md), [Olmo 3.1 32B Think](olmo-3-1-32b-think.md), [Qwen3-VL 30B-A3B Thinking](qwen3-vl-30b-a3b-thinking.md) here, though they rank lower overall. 
test                       result  vs the others in its run   difficulty            counts  why
FrontierMath tiers 1–3 v2   26.3%  ▂··▁▁▃●▄█▆▄▃ beats 18/86   very hard · avg 1.2%     41%  sharp · at its level
FrontierMath tier 4 v2       2.4%  ▁●▁▂█▅▂▃▂▁▁▁ beats 5/67    very hard · avg 0.1%     14%  sharp
OTIS mock AIME 2024–2025    68.1%  ▃▃▄▃▃▃●▇█▃▂▅ beats 98/197  very hard · avg 7.2%    4.3%  sharp · noisy
vision & documents · #29 of 189 · 15% of its score Beats [Claude Sonnet 5.5](claude-sonnet-5-5.md), [GPT-6 Sol](gpt-6-sol.md), [MiMo-V2.6-Pro](mimo-v2-6-pro.md) here, though they rank higher overall. 
test                                result  vs the others in its run    difficulty      counts  why
Arena vision (style controlled)     1276.1  ▁▁▂▂▂▂▃▄▅▅█● beats 127/152  avg 1061          6.3%  blunt
CharXiv (reasoning questions) (ma…   81.6%  ▄··▂█··▂···● beats 8/9      hard · avg 32%    5.3%  at its level
Arena document (style controlled)   1460.3  ▁▁▃▃▄▃▇●▅▇▄▅ beats 17/37    avg 1331          3.2%  blunt
other tests (2) 
test                   result  vs the others in its run    difficulty            counts  why
GPQA Diamond            82.5%  ▁▁▁▁▂▄▆▄▅●█▅ beats 135/211  hard · avg 39%           12%  sharp
Chess Puzzles (Epoch)   12.0%  █▁▁▁▁●▃▃▃▂▁▁ beats 56/137   very hard · avg 5.8%    4.8%  sharp · noisy
## 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
Epoch AI                                        5   75% ▇▇▇▇▇▇▇▇▇
LMArena leaderboard dataset                     3   19% ▇▇
OpenAI vendor-reported benchmark scores…        1    5% ▇
prior: pull toward the middle                        0% ▇
weighted average                                   = 67.9
Licences: Epoch AI ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · LMArena leaderboard dataset ([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-5-5-instant) · [JSON](https://whatmodel.sh/api?format=json&all=1)