[whatmodel.sh](../../README.md) / Nova Micro
# Nova Micro
Amazon · released 2024-12 · closed weights
**score 47.2** ±2.0 · #425 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 Micro among every model on that task or test, worst to best.
task vs the others rank counts tests
agent tasks ▁▁▃●█▄▅▅▄▄▂▁ #264 of 313 55% 4
chat & writing ▁▁▁▃▆▄●█▆▇▇▃ #288 of 402* 40% 1
agent tasks · #264 of 313 · 55% of its score
Beats [DeepSeek R1 0528 Qwen3 8B](deepseek-r1-0528-qwen3-8b.md), [Magistral Small 1.0 (2506)](magistral-small-2506.md), [Llama 4 Scout (109B-A17B)](llama-4-scout.md) here, though they rank higher overall. Trails [Granite 4.0 H 1B](granite-4-0-h-1b.md), [Granite 4.0 1B](granite-4-0-1b.md), [SmolLM3 3B](smollm3-3b.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
MASSIVE-Agents AST (10k, zero-sho… 5.5% █····█●█·███ beats 2/6 very hard · avg 15% 22% sharp
BFCL V4 (December 2025 archive) 22.3% ▁▁▁▂●█▅▅▆▂▂▂ beats 10/77 hard · avg 31% 16% at its level
BFCL v3 51.8% ▁▁▁▁▁▃▅●▅▄▁▁ beats 43/114 medium · avg 51% 9.0% blunt · at its level
Galileo Agent Leaderboard v1 (too… 82.9% ▃▃▃▃▆●▆▅█▂▅▂ beats 12/31 easy · avg 78% 8.7% at its level
chat & writing · #288 of 402 · 40% of its score
Beats [Ministral 3 14B Reasoning 2512](ministral-3-14b-reasoning.md), [Ministral 3 8B](ministral-3-8b.md), [Gemma 4 E4B](gemma-4-e4b.md) here, though they rank higher overall. Trails [EXAONE 3.5 2.4B Instruct](exaone-3-5-2-4b.md), [LFM2 700M](lfm2-700m.md), [Flan-T5 XXL](flan-t5-xxl.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
LMArena (text) 1240.7 ▁▁▁▂▃▃●▆▆▇█▃ beats 89/385 avg 1260 40% blunt
other tests (2)
test result vs the others in its run difficulty counts why
Vectara hallucination leaderboard… 98.4% ▁▁▂▃▄▄█▆▃●▂▁ beats 129/140 very easy · avg 94% 2.9% blunt · maxed out · noisy
Vectara hallucination leaderboard… 94.5% ▁▂▁▂▅█▂▃●▃▁▁ beats 83/98 very easy · avg 90% 0.0% blunt
source results share of the score
LMArena leaderboard dataset 1 40% ▇▇▇▇▇
Berkeley Function Calling Leaderboard r… 2 25% ▇▇▇
MASSIVE-Agents (submitted / maker-repor… 1 22% ▇▇▇
Galileo Agent Leaderboard 1 9% ▇
Vectara hallucination leaderboard 2 3% ▇
prior: pull toward the middle 2% ▇
weighted average = 47.2
Licences: 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)) · MASSIVE-Agents ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · Galileo Agent Leaderboard ([Apache-2.0](../../licenses/galileo-agent-leaderboard-Apache-2.0.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) · $0.061/M blended · $0.035 in / $0.14 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). Observed 2026-09-25. · [context source](https://github.com/anomalyco/models.dev/blob/a668908cfe7073faa0363699486fc41a3ff52412/models/amazon/nova-micro.toml)
[interactive version](https://whatmodel.sh/model/nova-micro) · [JSON](https://whatmodel.sh/api?format=json&all=1)