[whatmodel.sh](../../README.md) / Nova 2 Lite # Nova 2 Lite Amazon · released 2025-10 · closed weights **score 57.6** ±1.4 · #232 of 732 · from 7 benchmark results across 5 sources Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only. ## What it's good at ● marks Nova 2 Lite among every model on that task or test, worst to best. 
task            vs the others          rank  counts  tests
agent tasks     ▁▁▃●█▄▅▅▄▄▂▁   #241 of 313*     14%      2
chat & writing  ▁▁▁▃▆▄▆●▆▇▇▃   #182 of 402*     20%      1
agent tasks · #241 of 313 · 14% of its score Beats [o3-mini](o3-mini.md) here, though they rank higher overall. Trails [SmolLM3 3B](smollm3-3b.md), [Phi-3.5-mini-instruct](phi-3-5-mini-instruct.md), [Jamba 1.5 Mini](jamba-1-5-mini.md) here, though they rank lower overall. 
test                                result  vs the others in its run  difficulty        counts  why
BFCL V4 (December 2025 archive)      27.1%  ▁▁▁▂●█▅▅▆▂▂▂ beats 21/77  hard · avg 31%      9.5%  at its level
τ²-Bench-Verified (GPT-5.1 user) …   72.4%  ▂··▄···▂●▄█▄ beats 4/13   medium · avg 45%    4.1%  at its level
chat & writing · #182 of 402 · 20% of its score Beats [Seed-OSS 36B Instruct](seed-oss-36b.md), [Gemma 2 9B IT SimPO](gemma-2-9b-simpo.md), [Nemotron-Cascade-14B-Thinking](nemotron-cascade-14b-thinking.md) here, though they rank higher overall. Trails [LFM2.5 1.2B Instruct](lfm2-5-1-2b-instruct.md), [LFM2.5 1.2B Thinking](lfm2-5-1-2b-thinking.md), [LFM2.5 8B-A1B](lfm2-5-8b-a1b.md) here, though they rank lower overall. 
test            result  vs the others in its run    difficulty  counts  why
LMArena (text)  1335.0  ▁▁▁▂▃▃▃●▆▇█▃ beats 170/385  avg 1260       20%  blunt
other tests (4) 
test                                result  vs the others in its run  difficulty           counts  why
CLIcK Korean (Daekeun Kim)           67.2%  ▆▄▄●█▄▂·▄█·▄ beats 7/23   medium · avg 56%        28%  at its level
HAE-RAE 1.0 (Daekeun Kim)            73.9%  ▄▄▄▂●█▂▄▂█▂▄ beats 7/22   medium · avg 61%        23%  typical
KMMLU-HARD (Daekeun Kim, zero-sho…   48.2%  █···▁▁▁●▃·▆▂ beats 12/23  very hard · avg 29%     15%  sharp
Vectara hallucination leaderboard…   94.9%  ▁▂▁▂▅█▂▃▄●▁▁ beats 90/98  very easy · avg 90%    0.0%  blunt
## How the score was worked out Each result implies a score on its own, after adjusting for how hard that test is and who ran it. The model's 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 is added up by who ran the tests. [Method](../method.md) 
source                                    results  share of the score
Daekeun Kim Korean LLM benchmark runs           3   66% ▇▇▇▇▇▇▇▇
LMArena leaderboard dataset                     1   20% ▇▇
Berkeley Function Calling Leaderboard r…        1    9% ▇
Amazon AGI (submitted / maker-reported)         1    4% ▇
Vectara hallucination leaderboard               1    0% ▇
prior: pull toward the middle                        1% ▇
weighted average                                   = 57.6
Licences: Daekeun Kim Korean LLM benchmark runs ([MIT](../../licenses/korean-personal-benchmark-runs-MIT.txt)) · LMArena leaderboard dataset ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · Berkeley Function Calling Leaderboard result archive ([Apache-2.0](../../licenses/bfcl-result-archive-v4-Apache-2.0.txt)) · Amazon AGI ([MIT](../../licenses/tau2-verified-MIT.txt)) · Vectara hallucination leaderboard ([Apache-2.0](https://www.apache.org/licenses/LICENSE-2.0)) ## How to run it ### Through an API api (Amazon Bedrock) · $0.94/M blended · $0.33 in / $2.75 out per 1M tokens · [docs.aws.amazon.com](https://docs.aws.amazon.com/bedrock/latest/userguide/models-supported.html) [price source](https://aws.amazon.com/nova/pricing): USD per 1M tokens, Standard tier, geo cross-Region inference and in-Region, US East (N. Virginia); global cross-Region inference $0.30/$2.50. Observed 2026-09-25. · [context source](https://github.com/anomalyco/models.dev/blob/a668908cfe7073faa0363699486fc41a3ff52412/models/amazon/nova-2-lite.toml) [interactive version](https://whatmodel.sh/model/nova-2-lite) · [JSON](https://whatmodel.sh/api?format=json&all=1)