whatmodel.sh / Nova 2 Pro (Preview)
Nova 2 Pro (Preview)
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Nova 2 Pro (Preview) is a closed-weights language model from Amazon, released November 2025.
$ how it compares
The LLMs ranked either side of Nova 2 Pro (Preview), out of 828 with a score, then more from Amazon.
| # | model | score | where | ||
|---|---|---|---|---|---|
| 182 | 64±2 | local | |||
| 183 | 64±1 | local | |||
| 184 | 64±2 | api $3.78/M | |||
| 185 | 64±1 | comparison only | |||
| 186 | 64±1 | local | |||
| more from Amazon | |||||
| 269 | 58±2 | api $0.94/M | |||
| 339 | 54±2 | api $5.00/M | |||
| 423 | 50±1 | api $1.40/M | |||
$ what it's good at
agent tasks▁▁▃▆█▆▅▅▅▄▃▁ #94 of 300 * · 8.7% of its score · 1 test
Beats Grok 4.20 (0309 reasoning), Grok 4.3, o3-pro here, though they rank higher overall.
| test | result | vs the others in its run | counts |
|---|---|---|---|
| τ²-Bench-Verified (GPT-5.1 user) lab | 78.5% ✎ | ▂··▄···▂▄▄█▄ beats 8/13 | 8.7% |
other tests2 not in a task · 89% of its score
| test | result | vs the others in its run | counts |
|---|---|---|---|
| KMMLU-HARD (Daekeun Kim, zero-shot) | 54.1% ✎ | █···▁▁▁▂▃·▆▂ beats 14/23 | 49% |
| CLIcK Korean (Daekeun Kim) | 73.1% ✎ | ▆▄▄▄█▄▂·▄█·▄ beats 12/23 | 41% |
$ where to run it
Through an API
api · Amazon Bedrock · $3.78/M · $1.375 in / $11 out per 1M tokens · 1M context · aws.amazon.com ↗ · price sourceUSD per 1M text tokens, Amazon Bedrock Standard tier, geo cross-Region inference and in-Region, Amazon Nova 2 Pro (Preview). Global cross-Region inference is listed lower. 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.
$ how the score was worked out · Daekeun Kim Korean LLM benchm… 89%, Amazon AGI — τ²-Bench-Verified 9%
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
| source | results | share of the score |
|---|---|---|
| 2 | 89% | |
| 1 | 9% | |
| prior · pull toward the middle | 2% | |
| weighted average | = 63.5 |
$ improve this page
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