whatmodel.sh / Step 1o Turbo (2025-06)
Step 1o Turbo (2025-06)
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Step 1o Turbo (2025-06) is a closed-weights language model from StepFun. Licence: proprietary.
Catalogue identity notes
Exact Arena entry 'step-1o-turbo-202506' in the October 2026 pinned dataset. Comparison identity; no public run route inferred from leaderboard inclusion.
$ how it compares
The LLMs ranked either side of Step 1o Turbo (2025-06), out of 1132 with a score, then more from StepFun.
| # | model | score | where | ||
|---|---|---|---|---|---|
| 335 | calme-2.1-qwen2.5-72b | 58±4 | local | ||
| 336 |  Magistral Small 1.2 | 58±1 | api $0.75/M +local | ||
| 337 | Step 1o Turbo (2025-06) | 58±3 | comparison only | ||
| 338 |  Nemotron-Cascade-8B-Thinking | 58±2 | local | ||
| 339 |  Qwen3-VL 30B-A3B Instruct | 58±1 | api $0.35/M +local | ||
| more from StepFun | |||||
| 45 | Step 5 Preview | 75±1 | comparison only | ||
| 130 | Step 3.5 Flash (196B-A11B) | 68±1 | api $0.15/M +local | ||
| 151 | Step 3.7 Flash (198B-A11B) | 67±1 | api $0.44/M +local | ||
$ what it's good at
chat & writing▁▁▁▃▅▅▆█▆▆▆▂ #271 of 579 * · 57% of its score · 1 test
Beats Granite 4.2 8B, Qwen3-VL 30B-A3B Thinking, Mistral Small 4 (119B-A6.5B) here, though they rank higher overall. Trails LFM2.5 1.2B Instruct, LFM2.5 1.2B Thinking, Gemma 3 12B here, though they rank lower overall.
| test | result | vs the others in its run | counts |
|---|---|---|---|
| LMArena (text) | 1320.0 ✎ | ▁▁▁▂▃▃▃▆▆▇█▃ beats 153/379 | 57% |
vision & documents▁▁▃▃▂▃▅▅▇▇█▃ #120 of 199 * · 40% of its score · 1 test
Beats DeepSeek V4 Flash Vision (Exp), GPT-5 nano, Step 3 here, though they rank higher overall. Trails GPT-4o (2024-11-20) here, though they rank lower overall.
| test | result | vs the others in its run | counts |
|---|---|---|---|
| Arena vision (style controlled) | 1155.8 ✎ | ▁▁▂▂▂▂▄▅▅▆██ beats 52/145 | 40% |
$ where to run it
No verified API or download recorded yet · add one
$ how the score was worked out · LMArena leaderboard dataset 97%
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
| source | results | share of the score |
|---|---|---|
 LMArena leaderboard dataset | 2 | 97% |
| prior · pull toward the middle | 3% | |
| weighted average | = 57.8 |
$ improve this page
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