whatmodel.sh / Amazon Nova Experimental Chat (10-09)

Amazon Nova Experimental Chat (10-09)

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Amazon Nova Experimental Chat (10-09) is a closed-weights language model from Amazon. Licence: proprietary.

Catalogue identity notes

Exact Arena entry 'amazon-nova-experimental-chat-10-09' in the October 2026 pinned dataset. Comparison identity; no public run route inferred from leaderboard inclusion.

59.0±3.3comparison 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 Amazon Nova Experimental Chat (10-09), out of 1132 with a score, then more from Amazon.

#modelscorewhereRAMreleased
309Gemma 2 9B IT DPO (Princeton) Princeton NLP59±1local~7.6GB2024-07
310Ling-flash-2.0 inclusionAI (Ant Group)59±2local~40GB2025-09
311Amazon Nova Experimental Chat (10-09) Amazon59±3comparison only
312Trinity-Large-Preview Arcee AI59±2local~155GB2026-01
313GLM-4.7-Flash Z.ai59±1api $0.0000/M +local~19GB2026-01
more from Amazon
134Amazon Nova Experimental Chat (26-02-10) Amazon68±3comparison only
162Amazon Nova Experimental Chat (26-01-10) Amazon67±3comparison only
167Nova 2 Omni (Preview) Amazon66±7api $0.93/M2025-11

see all LLMs · find the ones that fit your machine

$ what it's good at

chat & writing▁▁▁▃▅▅▆█▆▆▆▂ #218 of 579 * · 95% of its score · 1 test

Beats MiniMax-M2, MiniCPM5 2B, Granite 4.2 30B here, though they rank higher overall. Trails LFM2.5 1.2B Thinking, LFM2.5 8B-A1B, LFM2.5 2.6B here, though they rank lower overall.

testresultvs the others in its rundifficultycountswhy
LMArena (text)1347.0 ✎▁▁▁▂▃▃▃▆▆▇█▃ beats 189/379avg 1268▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 95%blunt

$ where to run it

No verified API or download recorded yet · add one

$ how the score was worked out · LMArena leaderboard dataset 95%

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▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 95%CC BY 4.0
prior · pull toward the middle▇ 5%
weighted average= 59.0

$ 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