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A hybrid-reasoning Claude model from Anthropic with a 1M-token context window, selectable reasoning effort, and tool calling for coding, research, and agentic work.
Overview. Claude Sonnet 4.6 is a proprietary hybrid-reasoning model built by Anthropic, released in February 2026. It pairs a 1M-token context window with up to 128K tokens of output and lets you dial reasoning effort up or down per request. Developers reach for it when a task needs either a large context window, an agentic tool-calling loop, or an adjustable depth of reasoning within a single model.
Key strengths. The model's hybrid-reasoning design exposes selectable effort levels (low through max), so you can trade latency for depth of thinking on a per-call basis rather than switching models. Its 1M-token context window supports long documents, multi-file codebases, and extended agentic sessions without chunking. Native tool calling, optional parallel tool calls, and prompt/context caching make it well suited to coding and research workflows that lean on repeated context or multi-step tool use.
When to use it. Use Claude Sonnet 4.6 for coding, research, or agentic tasks that benefit from extended reasoning traces and a large working context, such as reviewing an entire codebase, synthesizing long research documents, or running multi-step tool-calling agents. Set a lower reasoning effort for interactive, latency-sensitive requests, and a higher effort for tasks that need deeper deliberation before answering. Its vision input also makes it a fit for tasks that combine text and images, such as reviewing diagrams alongside code or documents.
Limitations. Anthropic now positions this model as a legacy Sonnet release: Claude Sonnet 5 has replaced it as the default Sonnet model as of June 2026, though Sonnet 4.6 remains active with no retirement scheduled before February 2027. It has no native audio input or output, and Anthropic does not disclose its underlying architecture or parameter count. Its training data cutoff is January 2026, so it won't have knowledge of events after that point.
Sovereignty note. Claude Sonnet 4.6 is built by Anthropic, a US-based creator, which means Inference through this model can be subject to CLOUD Act access requests rather than qualifying as Sovereign under EU-residency terms.
| Inference Provider | Sovereignty | Runs in | In / out per 1M | Status | Pin |
|---|---|---|---|---|---|
| L0 | €2.72 / 13.61+2 rates | Online |
20 use cases classified by the curator. The note says why.
1M-token context plus tool-calling and selectable extended-thinking effort make this the family's top pick for large-repo agentic coding workflows that need to hold full codebases in context.
Extended-thinking mode plus full-repo context window supports tracing bugs across large, interdependent codebases.
1M-token context enables reviewing entire repositories in one pass rather than chunked diffs, a differentiator versus smaller-context family members.
1M-token context is five times larger than other family members, ideal for Q&A over very long single documents or document sets.
Native tool-calling support and a 1M-token window let function-calling apps maintain large state/schema context across many calls.
Massive context window plus extended-thinking supports ingesting and cross-referencing large literature sets without chunking loss.
1M-token context helps hold entire audit trails/documents in scope, but closed-weights US-jurisdiction status is a consideration for regulated audit workflows.
Tool-calling and extended-thinking support browser-automation agent loops, though no browser-specific benchmark is documented for this variant.
Vision support enables chart-extraction tasks, but lack of benchmark evidence caps this at acceptable rather than strong.
General coding capability and large context support explaining code in context, competent but not a differentiated strength area.
Extended-thinking reasoning aids structured analysis tasks, though no data-analysis-specific benchmark is documented.
Vision capability supports OCR-style document reading, though no OCR-specific benchmark is cited.
Sourced numbers, not our own evaluations. Percentiles are against the whole Stav catalogue.
Computed from aa-intelligence-index, gpqa-diamond, aa-engineering-index, aa-coding-index, livecodebench, terminal-bench-v4, ifbench, aa-multilingual-index, aa-lcr.
curl https://api.stav.ai/v1/chat/completions \
-H "Authorization: Bearer $STAV_API_KEY" \
-H "Content-Type: application/json" \
-d '{
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"messages": [{"role": "user", "content": "Hello!"}]
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