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.
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.
High-signal model metadata in a structured two-column overview table.
The entity that provides this model.
The routed model identifier exposed by upstream providers.
The number of tokens supported by the input context window.
The number of tokens that can be generated by the model in a single request.
Whether the model's code is available for public use.
When the model was first released.
When the model's knowledge was last updated.
The providers that offer this model. This is not an exhaustive list.
Types of data this model can process.
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.
Processes up to 300,000 tokens in a single request, enabling analysis of lengthy documents, codebases, or multi-turn conversations without truncation.
Accepts both text and image inputs, allowing the model to reason over visual content alongside written instructions or questions.
Designed to support multi-step agentic workflows and UI actuation, enabling automated sequences of actions within larger systems.
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.
Tagged as FAST in the model catalog, reflecting that Nova Pro is designed to return responses quickly relative to its capability tier.
Available via Amazon Bedrock, providing a managed API endpoint with no need to handle infrastructure or model hosting directly.
Primary API pricing shown in the same “quick compare” spirit as the reference page.
Additional usage-cost dimensions synced into the project for this model.
Places where this model is available, based on the synced detail-page metadata.
Endpoint-level provider data currently available for this model.
Benchmark scores synced from the current model source and normalized into the local catalog.
| Benchmark | Score |
|---|---|
|
AIME 2024
American math olympiad problems
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GPQA Diamond
PhD-level science questions (biology, physics, chemistry)
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HLE
Questions that challenge frontier models across many domains
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LiveCodeBench
Real-world coding tasks from recent competitions
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MATH-500
Undergraduate and competition-level math problems
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MMLU-Pro
Expert knowledge across 14 academic disciplines
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SciCode
Scientific research coding and numerical methods
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Official model cards, release notes, docs, and other references synced from the source page.
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.
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 :(
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)
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?
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.
# 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
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.
Nova Pro is a multimodal model that accepts both text and image inputs. It is distinct from Nova Micro, which is text-only.
Amazon Nova Pro was released in December 2024, which also corresponds to its training data cutoff date as listed in the model metadata.
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.
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.
Continue browsing adjacent models from the same provider.