Amazon Nova Pro
Amazon Nova Pro is a multimodal model from Amazon, announced on December 3, 2024 and offered through Amazon Bedrock (model ID amazon.nova-pro-v1:0). It takes text, image and video input and returns text.
Superseded. Amazon’s Nova documentation says Amazon Nova 2 is now available with new models. This entry is the first-generation Nova Pro.
Released
December 3, 2024
Type
multimodal
License
proprietary
Context
300,000 tokens
Up to 10,000 output tokens.
Pricing
Input
$0.80 / 1M tokens
Output
$3.20 / 1M tokens
List price on the developer's own pages, last checked , more than 7 days ago: it may have changed since. Standard tier in US East (Ohio), as Amazon’s Nova pricing page lists it; other tiers and regions are priced differently (latency-optimized $1.00 / $4.00, Flex $0.40 / $1.60).
Capabilities
Sources
Every figure on this page was checked against these pages on . A figure we could not source is left out.
- AWS: Amazon Nova foundation models announced (opens in a new tab)
- Amazon Nova user guide: What is Amazon Nova? (opens in a new tab)
- Amazon Nova pricing (opens in a new tab)
Spotted something out of date? Tell us.
Links
In the news: Amazon Nova Pro and Amazon
Search Engine Roundtable
ChatGPT Ads With Learn More Arrow Link; Some Served Through Amazon DSP
OpenAI is testing a new ad format for ChatGPT that features a "Learn more" link with an upward-pointing arrow. This new ad unit is dotted and underlined. In some cases, these ChatGPT advertisements are being served through Amazon DSP, suggesting a partnership or integration for ad delivery.
9to5Google
Amazon launches two Bee AI recorders to remember everything you say
Amazon has launched two new AI wearables, the Bee AI recorders, a year after acquiring the AI company Bee. These devices are designed to record and remember spoken conversations. The Bee AI recorders are Amazon's first AI wearables, aimed at ensuring users never forget what has been said.
The Hacker News
Bring Your Own Agent: Your Employees' Assistants Are Already Logged In
The rollout of personal AI agents is encountering significant challenges, including privacy concerns and identification issues. Amazon has blocked Meta's Muse agent for failing to identify itself as a bot during browsing, and the Instinct agent has also raised privacy alarms. Despite these ongoing issues and accumulating negative experiences, the technology sector remains captivated by the capabilities and potential of these personal AI services. The article highlights that the personal AI agent rollout is proceeding with expected difficulties.
MIT Technology Review
Job titles of the future: Delivery drone air traffic controller
The burgeoning field of drone delivery is necessitating a new class of aviation professionals: Remote Pilots in Command (RPICs), who function as air traffic controllers for unmanned aerial vehicles. Trevor Wischnewsky, an RPIC at FlyTrex, exemplifies this emerging role. His responsibilities include overseeing fleets of hexacopter drones that transport goods within a three-mile radius to homes and businesses. Wischnewsky's role extends beyond managing FlyTrex's own drone traffic; he also coordinates with drones from competing companies such as Zipline, Amazon, and Alphabet's Wing, which are increasingly populating the skies over areas like Dallas. This coordination is crucial for ensuring the safe and efficient delivery of parcels. To qualify for such a position, significant aviation experience is required. When Wischnewsky applied to FlyTrex, he was undergoing training to become a commercial pilot and flight instructor and already held a drone pilot certification from the Federal Aviation Administration (FAA). FlyTrex mandates this FAA certification, coupled with an FAA-approved training program lasting several weeks, to equip pilots with the skills to intervene if necessary. Although Wischnewsky does not directly pilot the drones, he monitors their entire delivery process from FlyTrex's operations center. This monitoring relies on data from GPS, lasers, and various other sensors, providing a comprehensive view of the drone's status and environment. A key aspect of this role is a collaborative mindset, particularly concerning the integration of autonomous systems. Wischnewsky initially found it challenging to fully trust the automation, as traditional aviation training often advises against relying solely on automated systems. However, the Dallas-Fort Worth area serves as a critical testing ground for Unmanned Aircraft Systems Traffic Management (UTM). UTM is a sophisticated network designed to enable autonomous drones from different companies to share their real-time positional data. This shared information is vital for preventing mid-air collisions, even when drones operate beyond the visual line of sight of their controllers. Once an RPIC like Wischnewsky confirms that a drone is prepared for flight, its intended path is clear, and weather conditions are favorable, the UTM system largely manages the subsequent flight operations. The ability to remain calm under pressure is another essential attribute for RPICs, especially during peak operational periods when multiple drones are active. The increasing complexity and volume of drone traffic underscore the growing importance of these specialized air traffic control roles for the future of autonomous delivery systems.
MarkTechPost
Anthropic Releases Claude Haiku 5.5: A Small Model With 1M Context Priced at $0.10 per Million Input Tokens
Anthropic released Claude Haiku 5.5 this week, positioning it as its most affordable and rapid small-scale AI model to date. This new iteration is engineered for high-volume tasks such as text summarization, data compaction, classification, and the operation of sub-agents within larger AI systems. A key feature retained from previous versions is its substantial 1 million token context window, complemented by an output token capacity of up to 128,000 tokens. The pricing structure for Haiku 5.5 begins at $0.10 per million input tokens and $0.50 per million output tokens. Anthropic reports this represents a significant cost reduction, approximately 90% less than Claude Haiku 4.5 for prompts up to 100,000 tokens. The model is accessible via a hosted API and is generally available across multiple cloud platforms, including the Claude API, Amazon Bedrock, Google Cloud, Microsoft Foundry, and the Claude Platform on AWS. Claude Haiku 5.5 introduces an adjustable "effort" setting, a novel capability for the Haiku class of models. This "adaptive thinking" feature is enabled by default, with the effort parameter set to a medium level. The model processes both text and image inputs and generates text outputs, possessing a knowledge cutoff date of June 2026. For batch processing, the system supports up to 300,000 output tokens, currently in beta. Two important technical considerations for users migrating to Haiku 5.5 are noted: attempts to use non-default values for temperature, top_p, or top_k parameters will result in a 400 error. Additionally, the model's new tokenizer processes text such that it consumes approximately 30% more tokens compared to Haiku 4.5 for the same textual content; a migration guide is available to address these changes. The pricing model for Haiku 5.5 employs a two-tier structure based on prompt token volume. For prompts containing up to 100,000 tokens, input costs are $0.10 per million tokens, and output costs are $0.50 per million tokens. Cache reads are priced at $0.01, and 5-minute cache writes are $0.125. For prompts exceeding 100,000 tokens, the rates increase to $0.50 per million for input and $2.50 per million for output. This contrasts with Haiku 4.5's pricing of $1 per million input and $5 per million output. Anthropic estimates that approximately 90% of Haiku 4.5 requests fell within the 100,000 token threshold. Factoring in the tokenizer adjustments, Anthropic projects that Haiku 5.5 will be, on average, about 75% cheaper for users. Further cost reductions of an additional 50% are achievable through batch processing. In comparison, GPT-6 Luna, another model in the market, lists identical short-context rates but initiates its higher pricing tier above 272,000 input tokens, at $0.20 for input and $0.75 for output. For a 150,000 token prompt, Luna is listed as cheaper. Anthropic has also released benchmark performance data for Haiku 5.5, though these figures are self-reported. On the OSWorld 2.1 offline subset, Haiku 5.5 achieved a score of 72.4%. This performance is significantly higher than GPT-6 Luna's reported 48.9% and Haiku 4.5's 15.7%. In the Terminal-Bench 4.0 benchmark, Haiku 5.5 scored 39.2%, compared to Luna's 16.4% and Haiku 4.5's 0.0%. These benchmarks suggest a substantial leap in performance for Haiku 5.5, particularly in tasks represented by these specific evaluations, positioning it as a competitive option for developers seeking efficient and cost-effective AI solutions for large-scale text processing and analysis.