whatmodel.sh / Step 1o Vision 32K High Resolution

Step 1o Vision 32K High Resolution

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Step 1o Vision 32K High Resolution is a closed-weights language model from StepFun. Licence: proprietary.

Catalogue identity notes

Exact Arena entry 'step-1o-vision-32k-highres' in the October 2026 pinned dataset. Comparison identity; no public run route inferred from leaderboard inclusion.

56.4±3.9comparison only: no current run option verifiedfrom 1 benchmark result across 1 source* thinly tested, treat the rank as provisional

$ how it compares

The LLMs ranked either side of Step 1o Vision 32K High Resolution, out of 1132 with a score, then more from StepFun.

#modelscorewhereRAMreleased
369Qwen3 4B Thinking 2507 Alibaba57±1local~3.9GB2025-08
370Hunyuan-Large (2025-02-10) Tencent57±3comparison only2025-02
371Step 1o Vision 32K High Resolution StepFun56±4comparison only
372Gemini 1.5 Pro Experimental (0827) Google56±1comparison only2024-08
373Gemini 1.5 Pro (002) Google56±1comparison only2024-09
more from StepFun
45Step 5 Preview StepFun75±1comparison only
130Step 3.5 Flash (196B-A11B) StepFun68±1api $0.15/M +local~118GB2026-02
151Step 3.7 Flash (198B-A11B) StepFun67±1api $0.44/M +local~104GB2026-05

see all LLMs · find the ones that fit your machine

$ what it's good at

vision & documents▁▁▃▃▂▃▅▅▇▇█▃ #142 of 199 * · 93% of its score · 1 test

Beats DeepSeek V4 Flash Vision (Exp), GPT-4o (2024-08-06) here, though they rank higher overall. Trails Mistral Small 3.1 24B (2503), Llama 4 Scout (109B-A17B), Qwen3-VL 8B Instruct here, though they rank lower overall.

testresultvs the others in its rundifficultycountswhy
Arena vision (style controlled)1124.2 ✎▁▁▂▂▂▂▄▅▅▆██ beats 37/145avg 1068▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 93%blunt

$ where to run it

No verified API or download recorded yet · add one

$ how the score was worked out · LMArena leaderboard dataset 93%

Each result implies a score on its own, after adjusting for how hard its test is and who ran it. The 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's added up by who ran the tests. How it works

sourceresultsshare of the scorelicence
LMArena leaderboard dataset1▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 93%CC BY 4.0
prior · pull toward the middle▇▇ 7%
weighted average= 56.4

$ improve this page

Update model informationAdd a place to run itAdd a test resultPropose a benchmarkCorrect a benchmark scoreEdit data on GitHub

Use a short GitHub form or edit the data through a pull request. Every number needs a source; here's which ones we can use.

data updated · sources and licences · json