whatmodel.sh / Solar Pro 4

Solar Pro 4

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Solar Pro 4 is a closed-weights language model from Upstage, released August 2026.

64.6±1.5#148 of 719 LLMsfrom 3 benchmark results across 1 source

$ how it compares

The LLMs ranked either side of Solar Pro 4, out of 828 with a score, then more from Upstage.

#modelscorewhereRAMreleased
168Nemotron 3 Super 120B-A12B NVIDIA65±1api $0.28/M +local~73GB2026-03
169GLM-4.5 Z.ai65±1api $1.00/M +local~58GB2025-07
170Solar Pro 4 Upstage65±2api $0.53/M2026-08
171MiniMax-M2 MiniMax65±1api $0.53/M +local~75GB2025-10
172Laguna XS.2 Poolside65±2local~21GB2026-04
more from Upstage
75Solar Open 2 (250B-A15B) Upstage72±1local~527GB2026-07
240Solar Open 100B (102B-A12B) Upstage60±1local~41GB2025-12
662SOLAR 10.7B Instruct v1.0 Upstage37±1local~8.3GB2023-12

see all LLMs · find the ones that fit your machine

$ what it's good at

agent tasks▁▁▃▆█▆▅▅▅▄▃▁ #118 of 300 * · 42% of its score · 1 test

Beats o3-pro, o3, GPT-5 mini here, though they rank higher overall. Trails SmolLM3 3B, Jamba 1.5 Large, Nova 2 Pro (Preview) here, though they rank lower overall.

testresultvs the others in its rundifficultycountswhy
Arena Agent (October 2026 aggregate IPS)-0.1590 IPS ✎▃▁▁▄▂▃▇█▂▂▁▃ beats 0/49avg -0.312 IPS▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 42%sharp
coding▁▁▁▃▆██▇▆▄▃▁ #101 of 458 * · 37% of its score · 1 test

Beats Grok 4.3, Muse Glimmer 30B, Qwen3.5 Plus here, though they rank higher overall. Trails Llama 3.1 Nemotron Ultra 253B v1 here, though they rank lower overall.

testresultvs the others in its rundifficultycountswhy
Arena WebDev (Code Arena)1370.2 ✎▁▂▄▃▅█▄▇▆▄▁▁ beats 36/119avg 981▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 37%sharp
chat & writing▁▁▁▃▅▄▅█▆▆▇▃ #123 of 393 * · 21% of its score · 1 test

Beats LongCat 2.0 (1.6T-A48B), MiMo-V2-Flash (309B-A15B), MiniMax-M2.1 here, though they rank higher overall. Trails LFM2.5 2.6B, Nemotron-Cascade-8B, DeepSeek V3 0324 here, though they rank lower overall.

testresultvs the others in its rundifficultycountswhy
LMArena (text)1386.2 ✎▁▁▁▂▃▃▃▆▆▇█▃ beats 225/379avg 1268▇▇▇▇▇▇▇▇ 21%blunt

$ where to run it

Through an API

api · $0.52/M · $0.3 in / $1.2 out per 1M tokens · 524k context · developers.upstage.ai ↗ · price sourceStandard base token rate from models.dev. Provider-specific tiers, long-context surcharges, caching and promotions may differ.

$ how the score was worked out · LMArena leaderboard dataset 99%

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 dataset3▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 99%CC BY 4.0
prior · pull toward the middle▇ 1%
weighted average= 64.6

$ 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