whatmodel.sh / Amazon Nova Experimental Chat (11-10)

Amazon Nova Experimental Chat (11-10)

+ contribute
Update model informationAdd a place to run itAdd a test resultPropose a benchmarkCorrect a benchmark scoreEdit data on GitHub

Amazon Nova Experimental Chat (11-10) is a closed-weights language model from Amazon. Licence: proprietary.

Catalogue identity notes

Exact Arena entry 'amazon-nova-experimental-chat-11-10' in the October 2026 pinned dataset. Comparison identity; no public run route inferred from leaderboard inclusion.

61.0±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 (11-10), out of 1132 with a score, then more from Amazon.

#modelscorewhereRAMreleased
267GLM-4.5V Z.ai61±2api $0.90/M +local~69GB2025-08
268Llama 3.1 Nemotron Ultra 253B v1 NVIDIA61±2local~82GB2025-04
269Amazon Nova Experimental Chat (11-10) Amazon61±3comparison only
270HyperCLOVA X SEED 32B Think NAVER Cloud61±2comparison only2025-12
271Devstral Small 2 24B Mistral61±3local~17GB2025-12
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▁▁▁▃▅▅▆█▆▆▆▂ #189 of 579 * · 95% of its score · 1 test

Beats INTELLECT-3, Nemotron 3 Super 120B-A12B, MiniMax-M2 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)1364.3 ✎▁▁▁▂▃▃▃▆▆▇█▃ beats 208/379avg 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= 61.0

$ 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