[whatmodel.sh](../../README.md) / Nova Premier # Nova Premier Amazon · released 2025-04 · closed weights **score 55.2** ±1.5 · #275 of 732 · from 9 benchmark results across 2 sources · † no independently run results verified Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only. ## What it's good at _{● marks Nova Premier among every model on that task or test, worst to best.}

task                vs the others          rank  counts  tests
agent tasks         ▁▁▃▆█●▅▅▄▄▂▁   #168 of 313*     24%      2
coding              ▁▁▁▃▅▇●▇▅▄▃▁   #258 of 469*    5.9%      1
math                █··········●       #1 of 2*     23%      2
vision & documents  ▁▁▂▂▁●▄▂▆▇█▄   #114 of 153*     16%      2

agent tasks · #168 of 313 · 24% of its score

Beats [o1 (2024-12-17)](o1-2024-12-17.md), [o3-mini](o3-mini.md), [DeepSeek R1 0528](deepseek-r1-0528.md) here, though they rank higher overall. Trails [SmolLM3 3B](smollm3-3b.md), [Jamba 1.5 Mini](jamba-1-5-mini.md), [Jamba 1.5 Large](jamba-1-5-large.md) here, though they rank lower overall.

test                                result  vs the others in its run    difficulty           counts  why
MASSIVE-Agents AST (10k, zero-sho…   34.0%  █····███·██● beats 6/6      very hard · avg 15%     21%  sharp
BFCL v3 (maker)                      63.7%  ▁▁▁▁▁▃▅█●▅▄▁ beats 115/171  medium · avg 58%       2.8%  blunt · at its level · small run

coding · #258 of 469 · 5.9% 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), [InternLM2.5 20B Chat](internlm2-5-20b-chat.md) here, though they rank lower overall.

test                  result  vs the others in its run  difficulty      counts  why
BigCodeBench (maker)   28.1%  ▅····▃·●▃▅█▃ beats 3/12   hard · avg 26%    5.9%  at its level · small run

math · #1 of 2 · 23% of its score

test               result  vs the others in its run  difficulty           counts  why
AIME 2025 (maker)   16.0%  ▁·▁▁●▂▅▆█▇▅▁ beats 9/133  very hard · avg 12%     16%  sharp · small run
MATH-500 (maker)    82.0%  ▁▃▂▃▄●▅▇█▄▂▁ beats 23/85  easy · avg 79%         7.7%  small run

vision & documents · #114 of 153 · 16% of its score

Beats [DeepSeek V4 Flash Vision (Exp)](deepseek-v4-flash-vision-exp.md), [GPT-4 Turbo (2024-04-09)](gpt-4-turbo-2024-04-09.md), [Molmo 2 8B](molmo-2-8b.md) here, though they rank higher overall. Trails [Gemma 3 27B](gemma-3-27b.md), [Mistral Small 3.1 24B (2503)](mistral-small-3-1.md), [Llama 4 Scout (109B-A17B)](llama-4-scout.md) here, though they rank lower overall.

test                                result  vs the others in its run   difficulty        counts  why
CharXiv (reasoning questions) (ma…   48.8%  ▂▅▃▅●▆▆▅▆█▆▂ beats 27/101  medium · avg 38%    8.4%  at its level · small run
CharXiv descriptive questions (ma…   84.6%  ▂▄▄▅▇▄●█▇▂▁▂ beats 34/59   easy · avg 72%      7.2%  small run

other tests (2)

test                  result  vs the others in its run    difficulty      counts  why
GPQA Diamond (maker)   57.1%  ▁▁▁▁▂▄▅●███▄ beats 106/322  hard · avg 46%     19%  sharp · at its level · small run
MMLU (maker)           87.4%  ▁▁▁▁▁▂▂▅██▆● beats 199/214  easy · avg 81%     10%  small run

## 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
Amazon vendor-reported benchmark scores…        8   77% ▇▇▇▇▇▇▇▇▇
MASSIVE-Agents (submitted / maker-repor…        1   21% ▇▇▇
prior: pull toward the middle                        1% ▇
weighted average                                   = 55.2

_{Licences: MASSIVE-Agents ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/))} ## How to run it ### Through an API api (Amazon Bedrock) · $5.00/M blended · $2.5 in / $12.5 out per 1M tokens · [aws.amazon.com](https://aws.amazon.com/nova/pricing/) _{[price source](https://aws.amazon.com/nova/pricing): USD per 1M tokens, Amazon Bedrock Standard tier, geo cross-Region inference and in-Region, Amazon Nova Premier. Page table values are rendered from AWS's own price data file for the page (b0.p.awsstatic.com meteredUnitMaps, bedrock USD, published 2026-09-25T06:55:02Z); US East (N. Virginia) selected. Priority/Flex/Batch tiers, cached tokens and non-text modalities excluded. Currency: USD. AWS official Amazon Nova pricing page, observed 2026-09-25. · [context source](https://docs.aws.amazon.com/nova/latest/userguide/what-is-nova.html): Amazon Nova user guide model specifications: Nova Premier context window 1M (stated rounded; max output 10K tokens). Observed 2026-09-25.} [interactive version](https://whatmodel.sh/model/nova-premier) · [JSON](https://whatmodel.sh/api?format=json&all=1)