# Claude Sonnet 4.6

Claude Sonnet 4.6 pairs a context window of 1M tokens with adaptive thinking and MCP support, advancing agentic coding, code review, frontend UI, and computer use accuracy in a single release at Sonnet pricing.

- **Model ID:** `anthropic/claude-sonnet-4.6`
- **Type:** chat
- **Providers:** anthropic, vertexAnthropic, bedrock, claudeaws
- **Context window:** 1,000,000
- **Maximum output tokens:** 128,000
- **Pricing:** $3/1M input tokens, $15/1M output tokens
- **Canonical page:** https://vercel.com/ai-gateway/models/claude-sonnet-4.6

## Supported parameters

Detailed capability metadata has not been reported for this model.

## Example

```ts
import { streamText } from 'ai'

const result = streamText({
  model: 'anthropic/claude-sonnet-4.6',
  prompt: 'Why is the sky blue?'
})
```

## About

Claude Sonnet 4.6 launched on AI Gateway on February 17, 2026. Anthropic described it as approaching Opus-level intelligence. Claude Sonnet 4.6 advances multiple capabilities the Sonnet tier developed across the 4.x generation: stronger agentic coding, improved code review, better frontend UI quality, and higher computer use accuracy.

Two architecture features define Claude Sonnet 4.6. First, adaptive thinking: the model decides when and how much to reason, rather than requiring a fixed extended thinking budget. Simpler requests get a proportionate response. Complex ones receive deeper reasoning automatically. You don't need to categorize each request in advance. This evolves the thinking mode introduced in Claude 3.7 Sonnet into a smarter, model-directed form.

Second, a 1M-token context window at Sonnet pricing. Claude Sonnet 4.5 was the first Sonnet-tier model to reach 1M tokens; Claude Sonnet 4.6 keeps that as standard. For teams working with large codebases, long document collections, or extended agent histories, Opus-approaching quality at 1M tokens and Sonnet pricing changes what's economically feasible.

MCP (Model Context Protocol) support for scaled tool use means Claude Sonnet 4.6 can participate in large tool ecosystems without specifying individual tools for each interaction. The model proactively executes tasks, delegates to subagents, and parallelizes tool calls.

## What to consider

Adaptive thinking calibrates token usage automatically. Pair it with the `effort` parameter to manage cost on mixed workloads where some requests benefit from deep reasoning and others don't.

## When to use

### Best For

- **Agentic coding and code review** requiring strong intelligence with efficient reasoning, the primary capability improvements in this release
- **Frontend UI development** where quality of visual output matters, highlighted as an area of improvement over previous Sonnet versions
- **Computer use workflows** where higher accuracy on GUI automation ships as a 4.6 improvement at Sonnet pricing
- **Large-context agentic tasks** where the context window of 1M tokens enables processing entire codebases or document sets at Sonnet pricing
- **MCP-based tool environments** where the model needs to interact with large, diverse tool ecosystems

### Consider Alternatives When

- You need the maximum intelligence ceiling regardless of cost. Claude Opus 4.6 provides full Opus depth with the same 1M context window
- Simple tasks with tight latency budgets are the primary workload. Haiku 4.5 is faster and cheaper for well-bounded high-throughput requests
- Your use case doesn't benefit from adaptive thinking and you prefer to manage thinking budgets explicitly
- Computer use accuracy is critical to your outcome; verify benchmark results that match your workload against published scores at evaluation time

## Best for

- **Agentic coding and code review:** requiring strong intelligence with efficient reasoning, the primary capability improvements in this release
- **Frontend UI development:** Quality of visual output matters, highlighted as an area of improvement over previous Sonnet versions
- **Computer use workflows:** Higher accuracy on GUI automation and screen-driven agents ships as a 4.6 improvement, at Sonnet pricing rather than the Opus tier
- **Large-context agentic tasks:** The context window of 1M tokens enables processing entire codebases or document sets at Sonnet pricing
- **MCP-based tool environments:** The model needs to interact with large, diverse tool ecosystems

## Consider alternatives

- **Maximum intelligence ceiling:** Claude Opus 4.6 provides full Opus depth with the same 1M context window
- **Tight latency budgets:** Haiku 4.5 is faster and cheaper for well-bounded high-throughput requests
- **Explicit thinking budgets:** Earlier models like Claude 3.7 Sonnet accept a fixed `thinking` token budget instead of adaptive mode

## Frequently asked questions

### How does adaptive thinking differ from the fixed thinking budgets in earlier models?

The model decides when and how much to reason based on the request, rather than applying a developer-specified token budget. For mixed workloads with varying complexity, this avoids over-spending thinking tokens on simple requests while still applying deep reasoning to hard ones.

### Does Claude Sonnet 4.6 have the context window of 1M tokens?

Yes. The context window of 1M tokens is part of Sonnet 4.6's standard specification.

### What does MCP support mean for tool use in Sonnet 4.6?

MCP (Model Context Protocol) support lets the model interact with larger, standardized tool ecosystems without requiring individual tool specification per interaction. The model can proactively execute tasks, delegate to subagents, and parallelize tool calls.

### Can Sonnet 4.6 interleave thinking and tool calls?

Yes. Sonnet 4.6 can interleave thinking and tool calls within a single response, reasoning about a problem, calling a tool, reasoning about the result, and calling another tool, all in one turn.

### How do I configure adaptive thinking and effort for Sonnet 4.6 in the AI SDK?

Set the model to `anthropic/claude-sonnet-4.6`. Under `providerOptions.anthropic`, pass an `effort` level (for example, `medium`) and `thinking.type` set to `adaptive`.

### What does "Opus-approaching intelligence" mean practically for my use cases?

Sonnet 4.6 narrows the gap between the Sonnet and Opus tiers. For tasks where Opus previously justified its higher cost, benchmark Sonnet 4.6 as a potentially equivalent alternative at lower per-token pricing.

### What were the specific capability improvements in Sonnet 4.6 over previous Sonnet models?

Agentic coding, code review, frontend UI generation, and higher-fidelity instruction following all improved. The model approaches Opus-level intelligence while maintaining Sonnet-tier cost and latency.

## Links

- [Model page](https://vercel.com/ai-gateway/models/claude-sonnet-4.6)
- [AI Gateway documentation](https://vercel.com/docs/ai-gateway)
- [Provider model documentation](https://docs.anthropic.com/claude/docs/models-overview)
- [Provider pricing](https://www.anthropic.com/pricing#anthropic-api)


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