[whatmodel.sh](../../README.md) / Nova Pro
# Nova Pro
Amazon · released 2024-12 · closed weights
**score 50.6** ±1.1 · #354 of 732 · from 17 benchmark results across 6 sources
Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only.
## What it's good at
● marks Nova Pro among every model on that task or test, worst to best.
task vs the others rank counts tests
agent tasks ▁▁▃▆●▄▅▅▄▄▂▁ #226 of 313 14% 4
coding ▁▁▁▃▅▇●▇▅▄▃▁ #272 of 469* 28% 2
chat & writing ▁▁▁▃▆▄▆●▆▇▇▃ #241 of 402* 13% 1
math ●··········█ #2 of 2* 12% 2
vision & documents ▁▁▂●▁▃▄▂▆▇█▄ #134 of 153 17% 3
agent tasks · #226 of 313 · 14% of its score
Beats [o1 (2024-12-17)](o1-2024-12-17.md), [o3-mini](o3-mini.md), [DeepSeek R1 (Jan '25)](deepseek-r1.md) here, though they rank higher overall. Trails [SmolLM3 3B](smollm3-3b.md), [Jamba 1.5 Mini](jamba-1-5-mini.md), [Mellum2 12B-A2.5B Instruct](mellum2-12b-a2-5b-instruct.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
BFCL V4 (December 2025 archive) 25.0% ▁▁▁▂●█▅▅▆▂▂▂ beats 14/77 hard · avg 31% 5.4% at its level
BFCL v3 60.8% ▁▁▁▁▁▃▅█●▄▁▁ beats 83/114 medium · avg 51% 4.4% blunt · at its level · several runs
Galileo Agent Leaderboard v1 (too… 86.8% ▃▃▃▃▆▅●▅█▂▅▂ beats 17/31 easy · avg 78% 2.5% typical
Galileo Agent Leaderboard v2 (act… 29.0% ▅▃·▃●▅▅█▃█▅▅ beats 4/19 hard · avg 26% 2.1% at its level
coding · #272 of 469 · 28% of its score
Beats [Qwen3-VL 32B Instruct](qwen3-vl-32b-instruct.md), [Solar Open 100B (102B-A12B)](solar-open-100b.md), [GLM-4.7-Flash](glm-4-7-flash.md) here, though they rank higher overall. Trails [DeepSeek LLM 67B Chat](deepseek-llm-67b-chat.md), [QwQ-32B-Preview](qwq-32b-preview.md), [Phi-3 Medium 128K Instruct](phi-3-medium-128k-instruct.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
Aider code editing (133 Exercism … 54.1% ▁·▁▂▄▁●▄█▆▂▂ beats 19/62 medium · avg 51% 25% sharp · at its level
BigCodeBench (maker) 22.3% ▅····●·█▃▅█▃ beats 2/12 hard · avg 26% 2.8% at its level · small run
chat & writing · #241 of 402 · 13% of its score
Beats [Mistral Small 4 (119B-A6.5B)](mistral-small-4.md), [Qwen2.5 Coder 32B Instruct](qwen2-5-coder-32b.md), [Ministral 3 14B Reasoning 2512](ministral-3-14b-reasoning.md) here, though they rank higher overall. Trails [Flan-T5 XXL](flan-t5-xxl.md), [LFM2.5 1.2B Instruct](lfm2-5-1-2b-instruct.md), [Gemma 3 4B](gemma-3-4b.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
LMArena (text) 1290.1 ▁▁▁▂▃▃▃●▆▇█▃ beats 124/385 avg 1260 13% blunt
math · #2 of 2 · 12% of its score
test result vs the others in its run difficulty counts why
AIME 2025 (maker) 5.3% ▁·▁●▁▂▅▆█▇▅▁ beats 4/133 very hard · avg 12% 7.1% sharp · small run
MATH-500 (maker) 76.6% ▁▃▂▃●▇▅▇█▄▂▁ beats 20/85 easy · avg 79% 4.5% small run
vision & documents · #134 of 153 · 17% of its score
Trails [Qwen2-VL 7B Instruct](qwen2-vl-7b.md), [Qwen3.5 2B](qwen3-5-2b.md), [InternVL2-26B](internvl2-26b.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
Arena vision (style controlled) 1018.7 ▁▁▂●▂▂▃▄▅▅█▇ beats 14/152 avg 1061 8.5% blunt
CharXiv (reasoning questions) (ma… 40.6% ▂▅▃●▅▆▆▅▆█▆▂ beats 19/101 medium · avg 38% 4.2% at its level · small run
CharXiv descriptive questions (ma… 70.5% ▂▄▄●▇▄██▇▂▁▂ beats 13/59 easy · avg 72% 4.1% at its level · small run
other tests (4)
test result vs the others in its run difficulty counts why
GPQA Diamond (maker) 50.0% ▁▁▁▁▂▄●▅███▄ beats 87/322 hard · avg 46% 10% sharp · at its level · small run
MMLU (maker) 85.9% ▁▁▁▁▁▂▂▅██●▄ beats 192/214 easy · avg 81% 5.9% small run
Vectara hallucination leaderboard… 98.2% ▁▁▂▃▄▄█▆▃●▂▁ beats 124/140 very easy · avg 94% 0.8% blunt · maxed out · noisy
Vectara hallucination leaderboard… 94.9% ▁▂▁▂▅█▂▃▄●▁▁ beats 91/98 very easy · avg 90% 0.0% blunt
source results share of the score
Amazon vendor-reported benchmark scores… 8 40% ▇▇▇▇▇
Aider code editing benchmark runs 1 25% ▇▇▇
LMArena leaderboard dataset 2 21% ▇▇▇
Berkeley Function Calling Leaderboard r… 2 8% ▇
Galileo Agent Leaderboard 2 5% ▇
Vectara hallucination leaderboard 2 1% ▇
prior: pull toward the middle 1% ▇
weighted average = 50.6
Licences: Aider code editing benchmark runs ([Apache-2.0](../../licenses/aider-polyglot-runs-Apache-2.0.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)) · 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) · $1.40/M blended · $0.8 in / $3.2 out per 1M tokens · [docs.aws.amazon.com](https://docs.aws.amazon.com/bedrock/latest/userguide/model-card-amazon-nova-pro.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). Amazon Nova Pro (amazon.nova-pro-v1:0). Observed 2026-09-28 (page price data $0.0008/$0.0032 per 1K tokens). · [context source](https://docs.aws.amazon.com/nova/latest/userguide/what-is-nova.html): Context limit from the maker's documentation, observed 2026-10-06.
[interactive version](https://whatmodel.sh/model/nova-pro) · [JSON](https://whatmodel.sh/api?format=json&all=1)