whatmodel.sh / Sonar Pro

Sonar Pro

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Sonar Pro is a closed-weights language model from Perplexity, released January 2025.

Catalogue identity notes

Comparison-only. Arena entry 'ppl-sonar-pro-high' (search). Perplexity API system (search-augmented). Arena entry 'ppl-sonar-pro-high' = sonar-pro with high search context size (Perplexity pricing bills Sonar requests by search context size low/medium/high). Perplexity docs: 'Sonar will be supported until September 27, 2026'.

Model information sources

Release date ↗

63.0±4.9comparison 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 Sonar Pro, out of 1132 with a score, then more from Perplexity.

#modelscorewhereRAMreleased
227Granite 4.2 30B IBM63±1api $0.28/M +local~19GB2026-08
228Hunyuan-Vision-1.5-Thinking Tencent63±3api price unknown2025-09
229Sonar Pro Perplexity63±5comparison only2025-01
230o1-preview OpenAI63±1comparison only2024-09
231Gemini 2.5 Flash Preview (04-17) Google63±1comparison only2025-04
more from Perplexity
243Sonar Reasoning Pro Perplexity62±5comparison only

see all LLMs · find the ones that fit your machine

$ what it's good at

web search▂▁▂▇▅█▃▄▁▂▁▁ #68 of 96 * · 89% of its score · 1 test

Beats DeepSeek V4 Flash (Preview, Apr 2026), GPT-5.4 mini, Qwen3.5 397B-A17B here, though they rank higher overall. Trails GPT-4o mini (2024-07-18), Mistral Small 3.1 24B (2503), Llama 4 Scout (109B-A17B) here, though they rank lower overall.

testresultvs the others in its rundifficultycountswhy
Arena search systems (style controlled)1142.9 ✎▁·▁▁▃▂▂█▅▂▁▁ beats 4/30avg 1055▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 89%blunt

$ where to run it

No verified API or download recorded yet · add one

$ how the score was worked out · LMArena leaderboard dataset 89%

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▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 89%CC BY 4.0
prior · pull toward the middle▇▇▇ 11%
weighted average= 63.0

$ 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