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Claude Sonnet 4.5

claude-sonnet-4-5

Researched 29d ago

Last refreshed 2026-05-16. Next refresh: weekly.

MultimodalRAGAgentsLong contextVisionClassificationJSON / Tool useHighlight

Claude Sonnet 4.5 is worth evaluating for rag, agents, and long context when its provider route and context window match the workload.

Decision context: Vision task fit, 7 tracked provider routes, and research from 2026-04-19.

Use it for

  • Teams evaluating rag, agents, and long context
  • Workloads that can use a 200K context window
  • Buyers comparing 4 tracked provider routes

Do not use it for

  • Workloads where another current model has stronger sourced task evidence

Cheapest output

$15.00

Anthropic per 1M tokens

Provider routes

7

Tracked API hosts

Quality / dollar

Grade D

Ranked by benchmark score divided by cheapest output price

Freshness

2026-04-19

Researched 29d ago

fresh

Top use-case fit

RAG

Included by capability and metadata signals in the decision map.

Agents

Included by capability and metadata signals in the decision map.

Long context

Included by capability and metadata signals in the decision map.

Provider price ladder

Compare all 7
ProviderInput / 1MOutput / 1MBatch in / outCacheRoute
Anthropic$3.00$15.00$1.50 / $7.50-
Serverless
GCP Vertex AI$3.00$15.00--
Serverless
Microsoft Foundry$3.00$15.00-read $0.300 / 5m $3.75 / 1h $6.00
ServerlessProvisioned
OpenRouter$3.00$15.00--
Serverless

Benchmark peer barsfor Vision

Migration checks

No linked migration route is available for this model yet.

About

Claude Sonnet 4.5 available on AWS Bedrock

Claude Sonnet 4.5 has a 200K-token context window.

Claude Sonnet 4.5 input tokens at $3/1M, output at $15/1M.

Capabilities

VisionMultimodalReasoningFunction CallingTool UseStructured Outputs

Benchmark Scores(3)

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 Understanding77.8https://mmmu-benchmark.github.io/
BFCL73.2https://gorilla.cs.berkeley.edu/leaderboard.html
MMLU PRO86.0https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro

Rankings

Specifications

Released2025-09-29
Context200K
ArchitectureDecoder Only
Knowledge cutoff2025-12
Specializationgeneral
LicenseProprietary
Trainingfinetuned

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Founded 2021
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