whatmodel.sh / Mercury 2

Mercury 2

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Mercury 2 is a closed-weights language model from Inception. Licence: proprietary.

Catalogue identity notes

Exact Arena entry 'mercury-2' in the October 2026 pinned dataset. Comparison identity; no public run route inferred from leaderboard inclusion.

58.5±2.0comparison only: no current run option verifiedfrom 3 benchmark results across 2 sources

$ how it compares

The LLMs ranked either side of Mercury 2, out of 828 with a score.

#modelscorewhereRAMreleased
253Nemotron-Cascade-8B NVIDIA59±1local~6.3GB2025-12
254Qwen3 14B Alibaba59±1api $0.61/M +local~10GB2025-04
255Mercury 2 Inception59±2comparison only
256DiffusionGemma 26B-A4B Google59±1comparison only2026-06
257gpt-oss-20b OpenAI58±1api $0.13/M +local~14GB2025-08

see all LLMs · find the ones that fit your machine

$ what it's good at

coding▁▁▁▃▅▇█▇▅▄▃▁ #232 of 524 * · 63% of its score · 1 test

Beats Qwen3.5 4B, Qwen3-VL 32B Instruct, Falcon-H1R 7B here, though they rank higher overall. Trails QwQ-32B-Preview, Ministral 3 3B Reasoning 2512, DeepSeek-Coder-V2 Instruct (0724) here, though they rank lower overall.

testresultvs the others in its rundifficultycountswhy
Arena WebDev (Code Arena)1170.0 ✎▁▃▄▃▅█▄▇▆▄▁▁ beats 3/119avg 981▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 63%sharp
chat & writing▁▁▁▃▅▄▆█▇▇▇▃ #197 of 463 * · 35% of its score · 1 test

Beats MiniCPM5 2B, Granite 4.2 30B, Seed-OSS 36B Instruct here, though they rank higher overall. Trails LFM2.5 1.2B Instruct, LFM2.5 1.2B Thinking, LFM2.5 8B-A1B here, though they rank lower overall.

testresultvs the others in its rundifficultycountswhy
LMArena (text)1343.3 ✎▁▁▁▂▃▃▃▆▆▇█▃ beats 183/379avg 1268▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 35%blunt
other tests1 not in a task · 0.0% of its score
testresultvs the others in its rundifficultycountswhy
Vectara hallucination leaderboard (HHEM-2.3, factual consistency rate)87.7% ✎▁▂▁▂▅█▂▃▄▃▁▁ beats 20/98very easy · avg 90%▇ 0.0%blunt

$ where to run it

No verified API or download recorded yet · add one

$ how the score was worked out · LMArena leaderboard dataset 98%, Vectara hallucination leaderb… 0%

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 dataset2▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 98%CC BY 4.0
Vectara hallucination leaderboard (vectara/hallucination-leaderboard repository)1▇ 0%Apache-2.0
prior · pull toward the middle▇ 2%
weighted average= 58.5

$ 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