LLM Reference

Claude 3 Sonnet

Released
2024-03-04
Last refreshed
2026-05-11
Status
Researched 46d ago
MultimodalCodingRAGAgentsLong contextVisionJSON / Tool use

Claude 3 Sonnet is worth evaluating for coding, rag, and agents when its provider route and context window match the workload.

Use it for

  • Teams evaluating coding, rag, and agents
  • Workloads that can use a 200k context window
  • Buyers comparing 2 tracked provider routes

Do not use it for

  • Workloads where another current model has stronger sourced task evidence
Specifications
Family
Claude 3
Released
2024-03-04
Context
200k
Parameters
70B
Architecture
Decoder Only
Knowledge cutoff
2023-08
Specialization
general
Training
finetuned
Created by

Developing safe and ethical AI systems.

San Francisco, California, United States
Founded 2021
Website
Pricing
Output / 1M
$15.00
Input / 1M
$3.00

Cheapest of 3 routes · AWS Bedrock · cache read $0.300

About

Claude 3 Sonnet by Anthropic is a versatile large language AI model, balancing intelligence and speed for diverse enterprise use cases. It is part of the Claude 3 family, positioned between the powerful Opus and the faster Haiku models. Sonnet excels in nuanced content creation, accurate summarization, and complex scientific query handling while also showcasing proficiency in non-English languages and coding tasks. Additionally, it enhances vision capabilities with exceptional skills in visual reasoning, such as interpreting charts, graphs, and transcribing text from imperfect images, which benefits industries like retail, logistics, and finance. Operated at twice the speed of Claude 3 Opus, Sonnet is efficient in context-sensitive customer support and multi-step workflows. It has achieved AI Safety Level 2 (ASL-2) and is accessible through multiple platforms, including Claude.ai, the Claude iOS app, the Anthropic API, Amazon Bedrock, and Google Cloud's Vertex AI.

Claude 3 Sonnet is a model in the Claude 3 family. The structured metadata tracks a 200k-token context window, multimodal input, reasoning, structured outputs, and code execution. This page tracks provider routes through AWS Bedrock, GCP Vertex AI, and Anthropic, with the cheapest tracked route listed at $3 input and $15 output per 1M tokens. Headline tracked benchmarks include Massive Multi-discipline Multimodal Understanding 53.1.

Top use-case fit: coding, agents, and build tasks

Coding

Included by capability and metadata signals in the decision map.

RAG

Included by capability and metadata signals in the decision map.

Agents

Included by capability and metadata signals in the decision map.

Provider price ladder

Compare all 3

Compare API pricing across 2 providers for input and output tokens, batch, and cached reads when available.

ProviderInput / 1MOutput / 1MCacheRoute
AWS Bedrock$3.00$15.00read $0.300
Serverless
GCP Vertex AI$3.00$15.00-
Serverless

Capabilities

VisionMultimodalReasoningStructured OutputsCode Execution

Benchmark peer barsfor Vision

Benchmark scores(1)

Scores are benchmark-specific and are direction-aware: the same numeric gap can mean very different outcomes across suites. Use the leaderboard context and this model's provider route to decide whether the winning margin is meaningful for your workload.
BenchmarkScoreVersionSource
Massive Multi-discipline Multimodal Understanding53.1https://mmmu-benchmark.github.io/

Migration checks

No linked migration route is available for this model yet.

API versions

claude-3-sonnet-20240229

Rankings & picks(3)