Amazon

Amazon Nova Pro

Amazon Nova Pro is a multimodal foundation model developed by Amazon and made available through Amazon Bedrock. It accepts text and vision inputs and is designed to handle a wide range of tasks where accuracy, response speed, and cost-efficiency all need to be balanced together. It is part of the Amazon Nova family, which also includes Nova Lite and Nova Micro, each targeting different points on the capability-cost spectrum. Nova Pro was released in December 2024 and supports a 300,000-token context window. Nova Pro is particularly suited for agentic workflows and UI actuation, meaning it can be used to build systems that take sequences of actions or interact with interfaces. It supports fine-tuning on Amazon Bedrock, allowing developers to customize the model for specific domains or cost targets. Within the Nova family, Pro occupies the highest capability tier among the understanding models, making it the appropriate choice when tasks require processing both text and images at scale.

Dec 05, 2024 300,000 context 5,000 tokens output
Long Context Window Multimodal Input Agentic Task Execution Fine-Tuning Support Fast Response Speed Bedrock API Access

Model Overview

High-signal model metadata in a structured two-column overview table.

Provider

The entity that provides this model.

Amazon

Model ID

The routed model identifier exposed by upstream providers.

amazon/nova-pro-v1

Input Context Window

The number of tokens supported by the input context window.

300,000 tokens

Maximum Output Tokens

The number of tokens that can be generated by the model in a single request.

5,000 tokens tokens

Open Source

Whether the model's code is available for public use.

No

Release Date

When the model was first released.

Dec 05, 2024 1 year ago

Knowledge Cut-off Date

When the model's knowledge was last updated.

2024-10-31

API Providers

The providers that offer this model. This is not an exhaustive list.

Amazon Bedrock

Modalities

Types of data this model can process.

Text Image

What is Amazon Nova Pro

A fuller summary of positioning, capabilities, and source-specific details for Amazon Nova Pro.

Amazon Nova Pro is a multimodal foundation model developed by Amazon and made available through Amazon Bedrock. It accepts text and vision inputs and is designed to handle a wide range of tasks where accuracy, response speed, and cost-efficiency all need to be balanced together. It is part of the Amazon Nova family, which also includes Nova Lite and Nova Micro, each targeting different points on the capability-cost spectrum. Nova Pro was released in December 2024 and supports a 300,000-token context window.

Nova Pro is particularly suited for agentic workflows and UI actuation, meaning it can be used to build systems that take sequences of actions or interact with interfaces. It supports fine-tuning on Amazon Bedrock, allowing developers to customize the model for specific domains or cost targets. Within the Nova family, Pro occupies the highest capability tier among the understanding models, making it the appropriate choice when tasks require processing both text and images at scale.

Capabilities

What Amazon Nova Pro supports

CTX

Long Context Window

Processes up to 300,000 tokens in a single request, enabling analysis of lengthy documents, codebases, or multi-turn conversations without truncation.

MM

Multimodal Input

Accepts both text and image inputs, allowing the model to reason over visual content alongside written instructions or questions.

AG

Agentic Task Execution

Designed to support multi-step agentic workflows and UI actuation, enabling automated sequences of actions within larger systems.

AI

Fine-Tuning Support

Supports text and vision fine-tuning on Amazon Bedrock, allowing developers to adapt the model to specific use cases or optimize for cost and accuracy.

AI

Fast Response Speed

Tagged as FAST in the model catalog, reflecting that Nova Pro is designed to return responses quickly relative to its capability tier.

API

Bedrock API Access

Available via Amazon Bedrock, providing a managed API endpoint with no need to handle infrastructure or model hosting directly.

Pricing for Amazon Nova Pro

Primary API pricing shown in the same “quick compare” spirit as the reference page.

Price Comparison

Additional usage-cost dimensions synced into the project for this model.

maxTemperature 1
maxResponseSize 5,000 tokens

API Access & Providers

Places where this model is available, based on the synced detail-page metadata.

Amazon Bedrock

Provider Endpoints

Endpoint-level provider data currently available for this model.

Amazon Bedrock

Max output: 5,120 1d uptime: 100.0% Supported params: 6 Implicit caching: No

Amazon Bedrock

Max output: 5,120 1d uptime: 100.0% Supported params: 6 Implicit caching: No

Model Performance

Benchmark scores synced from the current model source and normalized into the local catalog.

Benchmark Score
AIME 2024
American math olympiad problems
10.7%
GPQA Diamond
PhD-level science questions (biology, physics, chemistry)
49.9%
HLE
Questions that challenge frontier models across many domains
3.4%
LiveCodeBench
Real-world coding tasks from recent competitions
23.3%
MATH-500
Undergraduate and competition-level math problems
78.6%
MMLU-Pro
Expert knowledge across 14 academic disciplines
69.1%
SciCode
Scientific research coding and numerical methods
20.8%

Resources & Documentation

Official model cards, release notes, docs, and other references synced from the source page.

Community discussion

What people think about Amazon Nova Pro

Amazon Nova Pro discussions are most active in r/lmarena, r/LocalLLaMA, r/OpenWebUI. The strongest match in this snapshot has 169 upvotes and 45 comments.

r/lmarena 12 upvotes 16 comments April 25, 2026
List of all models.

There are currently 481 models listed on the [arena.ai](http://arena.ai) website.

Here's the full list:

amazon.nova-pro-v1:0

anonymous-0410

anonymous-1111

anonymous-1218

anonymous-1221

anonymous-1800

anonymous-1815

anonymous-1825

anonymous-1835

apex-atlas

april26-chatbot1

april26-chatbot2

arastradero

atlas

autobear

badger

basalt-0303-1

basalt-0422-1

baseliner

beluga-0311-1

beluga-0413-1

blackhawk

blue-forge

botbot2

chatgpt-image-latest-high-fidelity (20251216)

chipmunk

chives

citrus

claude-3-5-sonnet-20241022

claude-3-7-sonnet-20250219

claude-3-7-sonnet-20250219-thinking-32k

claude-haiku-4-5-20251001

claude-opus-4-1-20250805

claude-opus-4-1-20250805-thinking-16k

claude-opus-4-1-search

claude-opus-4-20250514

claude-opus-4-20250514-thinking-16k

claude-opus-4-5-20251101

claude-opus-4-5-20251101-thinking-32k

claude-opus-4-5-search

claude-opus-4-6

claude-opus-4-6-search

claude-opus-4-6-thinking

claude-opus-4-7

claude-opus-4-7-search

claude-opus-4-7-thinking

claude-opus-4-search

claude-sonnet-4-20250514

claude-sonnet-4-20250514-thinking-32k

claude-sonnet-4-5-20250929

claude-sonnet-4-5-20250929-thinking-32k

claude-sonnet-4-5-search

claude-sonnet-4-6

claude-sonnet-4-6-search

clawl

clinkz

cloud-buddy

dall-e-3

dart-frog-0206

deep-octo

deepseek-v4-flash

deepseek-v4-flash-thinking

deepseek-v4-pro

deepseek-v4-pro-thinking

devstral-2

devstral-medium-2507

dialogue

dola-seed-2.0-preview-text

dola-seed-2.0-preview-vision

dola-seed-2.0-pro-text

dola-seed-2.0-pro-vision

dove

duomo-1-hero

EB45-turbo

EB45-vision

ember

emu

epilogue

ernie-5.0-0110

ernie-5.0-preview-1220

ernie-exp-251023

ernie-exp-251024

ernie-exp-251025

ernie-exp-251026

ernie-exp-251027

ernie-exp-vl-251016

ernie-image

eureka

february26-chatbot2

february26-chatbot3

february26-chatbot4

flashbrown-a

flashbrown-b

flow-state

flow-state-2

flow-state-3

flux-1-kontext-dev

flux-1-kontext-max

flux-1-kontext-pro

flux-2-dev

flux-2-flex

flux-2-klein-4b

flux-2-klein-9b

flux-2-max

flux-2-pro

flying-octopus

frenchfry

frieza

gallant

gallery

gcps-fast

gemini-2.0-flash-001

gemini-2.5-flash

gemini-2.5-flash-image-preview (nano-banana)

gemini-2.5-pro

gemini-2.5-pro-grounding

gemini-2.5-pro-grounding-exp

gemini-3-flash

gemini-3-flash (thinking-minimal)

gemini-3-flash-grounding

gemini-3-pro

gemini-3-pro-image-preview-2k (nano-banana-pro)

gemini-3.1-flash-image-preview (nano-banana-2) \[web-search\]

gemini-3.1-flash-lite-preview

gemini-3.1-pro

gemini-3.1-pro-grounding

gemini-3.1-pro-preview

gemma-3-27b-it

gemma-3n-e4b-it

glm-4.7

glm-4.7-flash

glm-5

glm-5.1

glm-5v-turbo

globe\_2

gpt-4.1-2025-04-14

gpt-4.1-mini-2025-04-14

gpt-5-chat

gpt-5-high

gpt-5-high-new-system-prompt

gpt-5-high-no-system-prompt

gpt-5-medium

gpt-5-mini-high

gpt-5-nano-high

gpt-5-search

gpt-5.1

gpt-5.1-codex

gpt-5.1-codex-max

gpt-5.1-codex-mini

gpt-5.1-high

gpt-5.1-medium

gpt-5.1-search

gpt-5.1-search-sp

gpt-5.2

gpt-5.2-chat-latest

gpt-5.2-codex

gpt-5.2-high

gpt-5.2-search

gpt-5.2-search-non-reasoning

gpt-5.3-chat-latest

gpt-5.3-codex

gpt-5.4

gpt-5.4-high

gpt-5.4-high-no-system-prompt

gpt-5.4-medium

gpt-5.4-mini-high

gpt-5.4-nano-high

gpt-5.4-no-system-prompt

gpt-5.4-search

gpt-5.5

gpt-5.5-high

gpt-5.5-search

gpt-image-1

gpt-image-1-high-fidelity

gpt-image-1-mini

gpt-image-1.5-high-fidelity

gpt-image-2 (medium)

gpt-oss-120b

gpt-oss-20b

grok-3-mini-beta

grok-3-mini-high

grok-4-0709

grok-4-1-fast-non-reasoning

grok-4-1-fast-reasoning

grok-4-1-fast-search

grok-4-fast-chat

grok-4-fast-reasoning

grok-4-fast-search

grok-4-search

grok-4.1

grok-4.1-thinking

grok-4.20-beta-0309-reasoning

grok-4.20-beta1

grok-4.20-multi-agent-beta-0309

grok-code-fast-1

grok-imagine-image

grok-imagine-image-pro

grok-imagine-video

hailuo-02-fast

hailuo-02-pro

hailuo-02-standard

hailuo-2.3

hailuo-2.3-fast

happy-friday-testing-1

happy-friday-testing-2

hearth

hidream-e1.1

hofburg\_2

hofburg\_2\_alt

hofburg\_3

hofburg\_4

hofburg\_5

hofburg\_5\_alt

hofburg-1

hunyuan-hy3-preview

hunyuan-image-2.1

hunyuan-image-3.0

hunyuan-image-3.0-fal

hunyuan-t1-20250711

hunyuan-video-1.5

hunyuan-vision-1.5-thinking

ibm-granite-h-small

ideogram-v3-quality

imagen-3.0-generate-002

imagen-4.0-fast-generate-001

imagen-4.0-generate-001

imagen-4.0-ultra-generate-001

intellect-3

jester

jumbo-dungeness

juniper

k2

kandinsky-5.0-i2v-pro

kandinsky-5.0-t2v-lite

kandinsky-5.0-t2v-pro

karyu

KAT-Coder-Pro-V1

ketchup-v2

kimi-k2-0711-preview

kimi-k2-0905-preview

kimi-k2-thinking-turbo

kimi-k2.5

kimi-k2.5-instant

kimi-k2.6

kiteki

kiwi-do

kiwire

kizen-alpha

kizen-beta

kling-2.5-turbo-1080p

kling-2.6-pro

kling-2.6-standard

kling-image-o1

kling-o1-pro

kling-o3-pro

kling-v2.1-master

kling-v2.1-standard

kling-v3

leepwal

left-bank

ling-1t

ling-1t-1031

ling-2.5-1t

ling-flash-2.0

llama-3.3-70b-instruct

longcat-flash-chat

ltx-2-19b

lucid-origin

mammoth-newt-0206

mammoth-newt-0226

march26-chatbot1

march26-chatbot1-public

march26-chatbot2

march26-chatbot3

markhor

Max

mercury

mercury-2

micro-mango

mimo-v2-flash

mimo-v2-flash (thinking)

mimo-v2-omni

mimo-v2-pro

mimo-v2.5

mimo-v2.5-pro

minicpm-sala

minimax-m1

minimax-m2

minimax-m2-preview

minimax-m2.1-preview

minimax-m2.5

mistral-large-3

mistral-medium-2505

mistral-medium-2508

mistral-small-2506

mistral-small-2603

mistral-small-3.1-24b-instruct-2503

mochi-v1

model-x

model-x-2

molmo-2-8b

monologue

monster

monterey

neon

nightride-on

nightride-on-v2

nova-2-lite

nvidia-nemotron-3-nano-30b-a3b-bf16

o3-2025-04-16

o3-mini

o3-search

o4-mini-2025-04-16

olmo-3-32b-think

olmo-3.1-32b-instruct

olmo-3.1-32b-think

orion

p-image

p-image-edit

paper-lantern

pebble-1

pebble-2

pepper

photon

pika-v2.2

pine

pisces-0226d

pisces-0309

pisces-0309-vision

pisces-0309b

pisces-0309c

pisces-0309d

pisces-0318-text

pisces-0318-vision

pisces-0320

pisces-llm-0130

pixel-parrot

pixverse-v5.6

ppl-sonar-reasoning-pro-high

prologue

pteronura

pulse

queen-bee

quiet\_sand

qwen-image-2.0

qwen-image-2.0-pro

qwen-image-2512

qwen-image-edit

qwen-image-edit-2511

qwen-image-prompt-extend

qwen-vl-max-2025-08-13

qwen3-235b-a22b

qwen3-235b-a22b-instruct-2507

qwen3-235b-a22b-no-thinking

qwen3-235b-a22b-thinking-2507

qwen3-30b-a3b

qwen3-30b-a3b-instruct-2507

qwen3-coder-480b-a35b-instruct

qwen3-max-2025-09-23

qwen3-max-2025-09-26

qwen3-max-2025-10-30

qwen3-max-preview

qwen3-max-thinking

qwen3-next-80b-a3b-instruct

qwen3-next-80b-a3b-thinking

qwen3-omni-flash

qwen3-vl-235b-a22b-instruct

qwen3-vl-235b-a22b-thinking

qwen3-vl-8b-instruct

qwen3-vl-8b-thinking

qwen3.5-122b-a10b

qwen3.5-122b-a10b-code

qwen3.5-27b

qwen3.5-27b-code

qwen3.5-35b-a3b

qwen3.5-35b-a3b-code

qwen3.5-397b-a17b

qwen3.5-flash

qwen3.6-plus

qwen3.6-plus-preview

qwq-32b

raptor-1.8-0120

raptor-1123

raptor-1124

ray-3

ray2

recraft-v3

recraft-v4

redwood

reve-v1.1

reve-v1.1-fast

ring-1t

ring-2.5-1t

ring-flash-2.0

rising-sun

robin

robin-high

rotten-apple

runway-gen-4.5

runway-gen4

runway-gen4-aleph

runway-gen4-turbo

scorch

seed-1.8

seedance-v1-lite

seedance-v1-pro

seedance-v1.5-pro

seededit-3.0

seedream-3

seedream-4-high-res-fal

seedream-4.5

seedream-5.0-lite

shakshouka

significant-otter

snowflake

soft-shell

solar-eclipse

sora

sora-2

sora-2-pro

spark

sphinx

spire

star-drift

steed-0217

step-3

step-3-mini-2511

step-3.5-flash

stephen-v2

stephen-vision-csfix

sungod

sunshine-ai

super-cara

super-gcp

tatertot

trinity-large

trinity-large-thinking

velo

veo-2

veo-3

veo-3-audio

veo-3-fast

veo-3-fast-audio

veo-3.1-audio

veo-3.1-audio-1080p

veo-3.1-audio-4k

veo-3.1-fast-audio

veo-3.1-fast-audio-1080p

veo-3.1-fast-audio-4k

vidu-q2-image

vierra

viper

vortex

vulcan

waffle

wan-v2.2-a14b

wan-vace

wan2.5-i2i-preview

wan2.5-i2v-preview

wan2.5-preview

wan2.5-t2i-preview

wan2.5-t2v-preview

wan2.6-i2v

wan2.6-image

wan2.6-t2i

wan2.6-t2v

wan2.7-i2v

wan2.7-image

wan2.7-image-pro

wan2.7-t2v

whisperfall

wild-bits

yivon-beta

yotta-nexus

z-image

zephyr

zero-prism

zeylu-alpha

zeylu-beta

zorik

Unfortunately, the list of models available for selection in direct and side-by-side mode is much smaller :(

Open Reddit thread
r/LocalLLaMA 169 upvotes 45 comments February 3, 2025
DeepSeek-R1 never ever relaxes...

So, I was testing DeepSeek-R1 with a math problem I found in a textbook for 9-year-olds **(yes, really)**, and the model managed to crack it.

The problem was:

`"Find two 3-digit palindromic numbers that add up to a 4-digit palindromic number. Note: the first digit of any of these numbers can't be 0."`

[R1 starts thinking...](https://preview.redd.it/ml5hnng3rwge1.jpg?width=1800&format=pjpg&auto=webp&s=1456610eeff8d8b9a122d86fbb44967f84f682d9)

Now, here’s where it gets interesting. R1 thought for a bit, found the correct answer in its `<think></think>` block, then went ahead to output it—but made a mistake.

[R1 makes a mistake...](https://preview.redd.it/77bke6q1swge1.jpg?width=1800&format=pjpg&auto=webp&s=d6eac07677fe576be9e699776a2134cba1d15c62)

Before even finishing its response, it caught its own error, backtracked, and corrected itself on the fly outside of the`<think></think>` block.

[R1 corrects itself...](https://preview.redd.it/yc3zjamsswge1.jpg?width=1800&format=pjpg&auto=webp&s=903d42998593e95a68ff32006b7bac6335df9f1e)

[R1's final answer.](https://preview.redd.it/j8vgvxn3twge1.jpg?width=1800&format=pjpg&auto=webp&s=b189fce4a099ed9182b315c2164a1071a4a32104)

[DeepSeek-R1 complete answer.](https://pastebin.com/0Ayv77LN)

Regarding the problem, **no other LLM solved it, except for** [**OpenAI o1**](https://pastebin.com/YCRR521W).

So now I’m wondering—**what's holding them back?** Is it the tokenizer's weaknesses? The sampling parameters (even when all where at the recommended settings they failed)? Or maybe, just maybe, non-thinking LLMs are really that bad at math?

Would love to hear thoughts on this.

Unsuccessful attemps by other models:

* [chatgpt-4o-latest-20241120](https://pastebin.com/r8VKHrcA)
* [claude-3-5-sonnet-20241022](https://pastebin.com/tXc7wGVz)
* [phi-4](https://pastebin.com/zGzQJ8B5)
* [amazon-nova-pro-v1.0](https://pastebin.com/vt54UFBe)
* [gemini-exp-1206](https://pastebin.com/eSN4y6E0)
* [llama-3.1-405b-instruct-bf16](https://pastebin.com/jVj1KcMF)
* [qwen-max-2025-01-25](https://pastebin.com/ZRLfhEfU)

Open Reddit thread
r/OpenWebUI 1 upvotes 3 comments May 19, 2026
Massive problems with 0.9.5 and Bedrock-Access-Gateway

I've decided to create a chat tool that is OpenWebUI in front of Bedrock Access Gateway.

I started with 0.9.1 and discovered that chat sharing was broken.

Then I noticed that 0.9.5 was released and downloaded it.

Now half of the Bedrock models streaming chat disappears after being visible (minimax m2.5, nova-pro-v1, glm-4.7-flash to name a few). And the "usage" toggle no longer pushes any token analytics into the postgres backend.

Does anyone have suggestions as to how to think through a stable, operational OWUI chat tool where the fundamental pieces like this will completely break with minor version changes??

Note: I upgraded BAG too. So apparently these "openai-compatible" bindings may not actually be as compatible as both BAG and OWUI make them out to be?

Open Reddit thread

That recent post about Carnegie Mellon's "AI disaster" https://www.reddit.com/r/singularity/comments/1k5s2iv/carnegie_mellon_staffed_a_fake_company_with_ai/

demonstrates perfectly how r/singularity rushes to embrace doomer narratives without actually reading the articles they're celebrating. If anyone bothered to look beyond the clickbait headline, they'd see that this study actually showcases how fucking close we are to fully automated employees and the recursive self improvement loop of automated machine learning research!!!!!

The important context being overlooked by everyone in the comments is that this study tested outdated models due to research and publishing delays.
Here were the models being tested:

- Claude-3.5-Sonnet(3.6)
- Gemini-2.0-Flash
- GPT-4o
- Gemini-1.5-Pro
- Amazon-Nova-Pro-v1
- Llama-3.1-405b
- Llama-3.3-70b
- Qwen-2.5-72b
- Llama-3.1-70b
- Qwen-2-72b

Of all models tested, Claude-3.5-Sonnet was the only one even approaching reasoning or agentic capabilities, and that was an early experimental version.

Despite these limitations, Claude still successfully completed 25% of its assigned tasks.

Think about the implications of a first-generation non-agentic, non-reasoning AI is already capable of handling a quarter of workplace responsibilities all within the context of what Anthropic announced yesterday that **fully AI employees are only a year away** (!!!):

https://www.axios.com/2025/04/22/ai-anthropic-virtual-employees-security

If anything this Carnegie Mellon study only further validates that what Anthropic is claiming is true and that we should utterly heed their company when their company announces that it expects "AI-powered virtual employees to begin roaming corporate networks in the next year" and take it fucking seriously when they say that these won't be simple task-focused agents but virtual employees with "their own 'memories,' their own roles in the company and even their own corporate accounts and passwords".

The r/singularity community seems more interested in celebrating perceived AI failures than understanding the actual trajectory of progress. What this study really shows is that even early non-reasoning, non-agentic models demonstrate significant capability, and, contrary to what the rabbid luddites in r/singularity would have you believe, only further substantiates rumours that soon these AI employees will have "a level of autonomy that far exceeds what agents have today" and will operate independently across company systems, making complex decisions without human oversight and completely revolutionize the world as we know it more or less overnight.

Open Reddit thread
r/GeminiAI 9 upvotes 9 comments December 9, 2025
List of all LMARENA models

# Text & Chat Models (LLMs)

# Google (Gemini & Gemma)

* **gemini-2.5-pro**
* gemini-2.5-pro-grounding-exp
* gemini-2.5-flash
* gemini-2.5-flash-preview-09-2025
* gemini-2.5-flash-lite-preview-09-2025-no-thinking
* gemini-2.5-flash-lite-preview-06-17-thinking
* gemini-3-pro
* gemini-2.0-flash-001
* gemma-3-27b-it
* gemma-3n-e4b-it

# OpenAI (GPT & O-Series)

* **gpt-5.1** / gpt-5.1-high
* **gpt-5-chat**
* gpt-5-high / gpt-5-high-new-system-prompt / gpt-5-high-no-system-prompt
* gpt-5-mini-high / gpt-5-nano-high
* **chatgpt-4o-latest-20250326**
* gpt-4.1-2025-04-14 / gpt-4.1-mini-2025-04-14
* gpt-oss-120b / gpt-oss-20b
* **o3-2025-04-16** / o3-mini
* **o4-mini-2025-04-16**

# Anthropic (Claude)

* **claude-3-7-sonnet-20250219** (+ thinking/thinking-32k)
* **claude-3-5-sonnet-20241022**
* claude-3-5-haiku-20241022
* **claude-opus-4-5-20251101** (+ thinking-32k)
* claude-sonnet-4-5-20250929 (+ thinking-32k)
* claude-haiku-4-5-20251001
* claude-opus-4-1-20250805 (+ thinking-16k)
* claude-opus-4-20250514 (+ thinking-16k)
* claude-sonnet-4-20250514 (+ thinking-32k)

# xAI (Grok)

* grok-3-mini-beta / grok-3-mini-high
* **grok-4.1** / grok-4.1-thinking
* grok-4-1-fast-reasoning / grok-4-1-fast-non-reasoning
* grok-4-0709
* grok-4-fast-chat / grok-4-fast-reasoning

# Alibaba (Qwen)

* qwen3-max-2025-09-23 / 09-26 / 10-20
* qwen3-max-preview / qwen3-max-thinking
* qwen3-next-80b-a3b-instruct / thinking
* qwen3-235b-a22b (+ instruct/thinking/no-thinking)
* qwen3-30b-a3b (+ instruct)
* qwen3-coder-480b-a35b-instruct
* qwen3-omni-flash
* qwq-32b
* Vision Understanding: qwen3-vl-235b-a22b (+instruct/thinking), qwen3-vl-8b (+instruct/thinking), qwen-vl-max-2025-08-13

# DeepSeek

* deepseek-v3.2
* deepseek-v3.2-thinking
* deepseek-v3-0324

# Meta (Llama)

* llama-3.3-70b-instruct
* llama-4-maverick-17b-128e-instruct

# Mistral

* mistral-large-3
* mistral-medium-2505 / 2508
* mistral-small-2506 / 3.1-24b-instruct-2503
* magistral-medium-2506

# Other Text Models

* **Baidu:** ernie-5.0-preview (1103/1120), ernie-exp (various dates)
* **Zhipu:** glm-4.5, glm-4.5-air, glm-4.5v, glm-4.6
* **MiniMax:** minimax-m1, minimax-m2, minimax-m2-preview
* **Tencent:** hunyuan-t1-20250711, hunyuan-vision-1.5-thinking
* **Amazon:** nova-2-lite, amazon-nova-experimental-chat, amazon.nova-pro-v1:0
* **Misc:** command-a-03-2025, ling-1t, ling-flash-2.0, step-3, ring-flash-2.0, intellect-3

# Image Generation Models

# Google (Imagen/Gemini)

* **gemini-3-pro-image-preview** (Standard, 2k, and 4k versions)
* gemini-2.5-flash-image-preview
* gemini-2.0-flash-preview-image-generation
* imagen-4.0-generate-001
* imagen-4.0-fast-generate-001
* imagen-4.0-ultra-generate-001
* imagen-3.0-generate-002

# Black Forest Labs (Flux)

* flux-2-pro
* flux-2-dev
* flux-2-flex
* flux-1-kontext-pro
* flux-1-kontext-dev
* flux-1-kontext-max

# OpenAI

* dall-e-3
* gpt-image-1
* gpt-image-1-mini
* gpt-image-1-high-fidelity

# Alibaba (Qwen Image)

* qwen-image-edit
* qwen-image-prompt-extend

# Tencent (Hunyuan)

* hunyuan-image-3.0
* hunyuan-image-3.0-fal
* hunyuan-image-2.1

# Wan / Video Models

* **wan2.5-preview**
* wan2.5-t2i-preview (Text to Image)
* wan2.5-i2i-preview (Image to Image)
* vidu-q2-image
* reve-v1 / reve-fast-edit

# Other Visual Models

* recraft-v3
* ideogram-v3-quality
* seedream-3 / seedream-4.5 / seedream-4-high-res-fal
* seededit-3.0
* mai-image-1
* photon
* lucid-origin
* hazel-gen-2 / 4
* hazel-edit-2 / 6
* hidream-e1.1
* tangerine
* ghost-pepper

# Hidden / Anonymous / Battle Models

These are internal codenames, blind test models, or obfuscated names specific to the Arena.

**The "Beluga/Phantom" Series:**

* beluga-1128-1
* phantom-1203-1
* phantom-mm-1125-1

**The "Raptor" Series:**

* raptor (base, 1110, 1119, 1123, 1124, 1202)
* raptor-llm (1017, 1024, 1125, 1205)
* raptor-vision (1015, 1107)

**The "EB / X1" Series:**

* EB45-turbo
* EB45-turbo-vl-0906
* EB45-vision
* x1-1-preview-0915
* x1-turbo-0906

**Anonymous IDs:**

* anonymous-1111, 1010, 915, 922, 925
* lmarena-internal-test-only
* not-a-new-model
* stephen-v2 / stephen-vision-csfix

**Abstract Codenames:**

* aegis-core, blackhawk, blitzphase, bridge-mind, dark-dragon, dashspark, evo-logic
* flashstride, flying-octopus, frame-flow, gauss, holo-scope, integrated-info
* leepwal, micro-mango, monster, monterey, neon, newton
* nightride-on / v2, rain-drop, redwood, route66, rushstream
* seahawk, silentnova, silvandra, skyhawk, sunshine-ai
* swiftflare, voltwhirl, viper, whisperfall, winter-wind

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FAQ

Common questions about Amazon Nova Pro

What is the context window for Amazon Nova Pro?

Amazon Nova Pro supports a context window of 300,000 tokens, which allows it to process large documents, long conversations, or extensive codebases in a single request.

What input types does Amazon Nova Pro support?

Nova Pro is a multimodal model that accepts both text and image inputs. It is distinct from Nova Micro, which is text-only.

When was Amazon Nova Pro released?

Amazon Nova Pro was released in December 2024, which also corresponds to its training data cutoff date as listed in the model metadata.

Can Amazon Nova Pro be fine-tuned?

Yes. Amazon Nova Pro supports text and vision fine-tuning through Amazon Bedrock, allowing developers to customize the model for specific tasks or cost requirements.

How does Nova Pro relate to the other Amazon Nova models?

Nova Pro is the highest-capability understanding model in the Amazon Nova family. Nova Lite is a lower-cost multimodal option, and Nova Micro is a text-only model optimized for low latency and minimal cost.

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