whatmodel.sh / Step-1V 32K

Step-1V 32K

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Step-1V 32K is a closed-weights language model from StepFun.

Catalogue identity notes

Comparison-only. Arena entry 'step-1v-32k' (vision). LMArena's leaderboard table (arena-leaderboard space @6f2eeeb) labels 'step-1v-32k' as 'Step-1V-32K' (StepFun, Proprietary), linking https://platform.stepfun.com/docs/llm/vision. Arena entry equals StepFun API id step-1v-32k (step-1v series, 32k context); no longer served.

49.6±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-1V 32K, out of 1132 with a score, then more from StepFun.

#modelscorewhereRAMreleased
586GLM-4 (0116) Z.ai50±1comparison only2024-01
587Llama 3 70B Instruct Meta50±0local~24GB2024-04
588Step-1V 32K StepFun50±4comparison only
589WizardLM-2 8x22B Microsoft50±1local~91GB2024-04
590Nova Pro Amazon50±1api $1.40/M2024-12
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▁▁▃▃▃▃▅▅▇▇█▃ #161 of 199 * · 93% of its score · 1 test

Beats DeepSeek V4 Flash Vision (Exp), Gemma 3 12B, Llama 3.2 90B Vision Instruct here, though they rank higher overall. Trails Qwen3.5 2B here, though they rank lower overall.

testresultvs the others in its rundifficultycountswhy
Arena vision (style controlled)1064.1 ✎▁▁▂▂▂▃▄▅▅▆██ beats 24/145avg 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= 49.6

$ 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