Update: December 2025
Since the initial publication schedule for this piece, new finalized benchmarks and API documentation have been released. We have updated this article to reflect the correct official model identifier for Claude 3.5 Haiku and its verified MMLU performance scores. Additionally, the Google Cloud Spanner section has been expanded to include the latest feature set confirmed in the December 2025 release notes.*
Claude 3.5 Haiku: Compact Powerhouse
Anthropic has released the latest iteration of their compact model, Claude 3.5 Haiku. Positioned as a solution for high-speed, low-latency tasks, Haiku is designed to handle everything from data extraction to complex code generation with remarkable efficiency.
Performance Benchmarks
While early speculation suggested higher scores, the official verified benchmark for the MMLU (Massive Multitask Language Understanding) is 76.6%. This places Haiku competitively against larger models while maintaining its signature speed advantage.
API Integration
Developers can now access the model using the updated API. Below is a Python example using the official Anthropic SDK.
import anthropic
client = anthropic.Anthropic()
message = client.messages.create(
model="claude-3-5-haiku-20241022",
max_tokens=1024,
messages=[
{"role": "user", "content": "Explain the difference between SQL and NoSQL databases."}
]
)
print(message.content[0].text)
Note: Ensure you are using the correct model identifier claude-3-5-haiku-20241022 to access the specific weights associated with the benchmark above.
Documentation: Anthropic API Quickstart
*
Google Cloud Spanner AI: Enterprise-Grade Vector & Graph Capabilities
Google Cloud has announced a massive overhaul of Spanner, cementing it as a leader in AI-native databases. Moving beyond simple relational storage, Spanner now integrates deeply with vector search and graph retrieval-augmented generation (GraphRAG).
Key New Features
1. Native Vector Search
Spanner now supports a native VECTOR data type. This allows for high-dimensional vector storage directly alongside relational data, enabling semantic search without the need for a separate vector database. The system supports approximate nearest neighbor (ANN) search to maintain low latency even at scale.
2. pgvector Support
To facilitate easier migration from open-source PostgreSQL environments, Spanner now supports the pgvector extension. This ensures compatibility with existing LangChain or LlamaIndex workflows that rely on standard PostgreSQL vector functions.
3. GraphRAG Capabilities
Leveraging Spanner's underlying relational graph structure, the new updates include GraphRAG capabilities. This allows developers to combine knowledge graph traversal with vector similarity search. This is particularly useful for enterprises needing to retrieve structured, factual context (from the graph) alongside unstructured semantic context (from vectors).
4. Ecosystem Integrations
Google has tightened the integration with the broader AI ecosystem:
- Vertex AI: Seamless model grounding, allowing Vertex AI models to query Spanner vectors directly for RAG applications.
- LangChain: Native Spanner vector store support is now available in the LangChain Python library, simplifying the retrieval pipeline for developers.
Pricing
Current pricing for Spanner AI features follows a consumption-based model:
- Storage: Standard Spanner storage rates apply to vector data.
- Compute: Vector search operations are billed based on processing nodes utilized.
- GraphRAG: Query costs are calculated by the complexity of graph traversal and vector lookup combined.
For the most current pricing calculator, refer to the official Google Cloud pricing sheet.
Source: Google Cloud Spanner AI Documentation Reference: Introducing Spanner GraphRAG (Google Cloud Blog)