whatmodel.sh / Amazon Nova Experimental Chat (26-01-10)

Amazon Nova Experimental Chat (26-01-10)

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Amazon Nova Experimental Chat (26-01-10) is a closed-weights language model from Amazon. Licence: proprietary.

Catalogue identity notes

Exact Arena entry 'amazon-nova-experimental-chat-26-01-10' in the October 2026 pinned dataset. Comparison identity; no public run route inferred from leaderboard inclusion.

66.5±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 (26-01-10), out of 1132 with a score, then more from Amazon.

#modelscorewhereRAMreleased
160DeepSeek V3.1 Terminus DeepSeek67±1local~230GB2025-09
161DeepSeek V3.2 Exp DeepSeek67±1local~726GB2025-09
162Amazon Nova Experimental Chat (26-01-10) Amazon67±3comparison only
163Qwen3 235B-A22B Thinking 2507 Alibaba66±1api $0.97/M +local~76GB2025-07
164Grok 4.1 Fast (Reasoning) SpaceXAI (xAI)66±1api $0.28/M2025-11
more from Amazon
134Amazon Nova Experimental Chat (26-02-10) Amazon68±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▁▁▁▃▅▅▆█▆▆▆▂ #121 of 579 * · 95% of its score · 1 test

Beats LongCat 2.0 (1.6T-A48B), Inkling Small (266B-A12B), GPT-5.4 nano here, though they rank higher overall. Trails Gemma 4 12B, Kimi K2 Instruct (0905), Gemma 4 26B-A4B here, though they rank lower overall.

testresultvs the others in its rundifficultycountswhy
LMArena (text)1413.1 ✎▁▁▁▂▃▃▃▆▆▇█▃ beats 259/379avg 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= 66.5

$ 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