whatmodel.sh / Amazon Nova Experimental Chat (26-02-10)

Amazon Nova Experimental Chat (26-02-10)

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Amazon Nova Experimental Chat (26-02-10) is a closed-weights language model from Amazon. Licence: proprietary.

Catalogue identity notes

Exact Arena entry 'amazon-nova-experimental-chat-26-02-10' in the October 2026 pinned dataset. Comparison identity; no public run route inferred from leaderboard inclusion.

68.1±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 (26-02-10), out of 1132 with a score, then more from Amazon.

#modelscorewhereRAMreleased
132MiMo-V2-Omni Xiaomi68±2comparison only2026-03
133Gemini 2.5 Pro Experimental (03-25) Google68±3comparison only2025-03
134Amazon Nova Experimental Chat (26-02-10) Amazon68±3comparison only
135Grok 4 (0709) SpaceXAI (xAI)68±1comparison only2025-07
136MiniMax-M2.7 MiniMax68±1api $0.53/M +local~75GB2026-03
more from Amazon
162Amazon Nova Experimental Chat (26-01-10) Amazon67±3comparison only
167Nova 2 Omni (Preview) Amazon66±7api $0.93/M2025-11
206Amazon Nova Experimental Chat (12-10) Amazon64±3comparison only

see all LLMs · find the ones that fit your machine

$ what it's good at

chat & writing▁▁▁▃▅▅▆█▆▆▆▂ #102 of 579 * · 95% of its score · 1 test

Beats LongCat 2.0 (1.6T-A48B), Inkling Small (266B-A12B), Muse Glimmer 30B here, though they rank higher overall. Trails Gemma 4 12B, Gemma 4 26B-A4B, Mistral Medium 3.5 (128B) here, though they rank lower overall.

testresultvs the others in its rundifficultycountswhy
LMArena (text)1426.6 ✎▁▁▁▂▃▃▃▆▆▇█▃ beats 280/379avg 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= 68.1

$ 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