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Anthropic's adaptive-reasoning Sonnet-class model with a 1M-token context window, strong Design Arena coding and UI results, and always-on reasoning selectable from low to max effort.
Overview. Claude Sonnet 5 is Anthropic's reasoning-tuned, Sonnet-class model, released June 30, 2026, and positioned between Anthropic's lighter-weight and flagship Claude offerings. It ships a 1,000,000-token context window and up to 128K tokens of output, with adaptive reasoning built in rather than offered as an optional toggle. Developers building on long documents, extended sessions, or code-heavy workflows that need consistent step-by-step reasoning are the natural fit.
Key strengths. Sonnet 5 posts a strong coding index score from Artificial Analysis (71.5) and performs well across Design Arena's structured-generation categories, including UI-component generation, game development, and 3D tasks. Its adaptive thinking runs by default with selectable effort levels from low through max, giving it consistent reasoning depth on complex, multi-step problems rather than requiring a separate extended-thinking flag. The 1M-token context window means large codebases or lengthy document sets can be handled in a single pass without chunking.
When to use it. Reach for Sonnet 5 when a task benefits from deep, always-on reasoning over long inputs — large-document analysis, multi-file code review, or structured UI/game-asset generation are good matches given its Design Arena results. Its batch-oriented latency profile makes it better suited to asynchronous or longer-running Inference jobs than to latency-sensitive interactive chat. It also accepts image and document input alongside text, so it can handle mixed-media prompts where a document or screenshot is part of the context.
Limitations. Sonnet 5 no longer accepts custom sampling settings: setting temperature, top_p, or top_k to a non-default value now returns an error rather than being silently applied, so behavior tuning through sampling is not available. Anthropic has also removed the manual extended-thinking toggle in favor of always-on adaptive reasoning, which means there's no way to force a faster, non-reasoning response from this model. Anthropic has not disclosed parameter count or a training data cutoff for this model.
Sovereignty note. Anthropic is a United States-based developer subject to CLOUD Act jurisdiction, and Sonnet 5 is closed-weight — it cannot be self-hosted or run air-gapped, so all Inference routes through Anthropic-controlled infrastructure. Teams with strict data-residency requirements should confirm whether a Sovereign deployment path is available before routing regulated workloads to this model.
| Inference Provider | Sovereignty | Runs in | In / out per 1M | Status | Pin |
|---|---|---|---|---|---|
| L0 | €1.81 / 9.07+2 rates | Online | |||
| L0 | €2.00 / 9.98+1 rate | Active | |||
| L0 | €2.00 / 9.98+1 rate | Active |
17 use cases classified by the curator. The note says why.
71.5 coding index plus tool-calling and extended-thinking support make this a strong fit for autonomous multi-file coding agents.
Consistent Design Arena results across UI, 3D, and game-dev coding tasks indicate strong generative code completion performance.
Reasoning-tuned inference with selectable effort levels suits step-by-step code explanation, backed by the 71.5 coding index.
1M-token context window allows full-repository review, and the 71.5 coding index reflects strong code comprehension.
1M-token context window enables Q&A over very large documents without chunking.
Explicit tool-calling capability makes this a direct fit for structured function-calling applications.
Extended-thinking mode with selectable reasoning effort supports diagnostic reasoning, though no bug-specific benchmark is cited.
Vision capability enables document image processing, but no OCR-specific benchmark is documented to confirm strong-tier performance.
Vision capability is present, enabling image-based Q&A, though no vision-specific benchmark is cited.
1M context supports long-document generation, though curated strengths emphasize coding over prose quality.
Vision support combined with strong UI-focused Design Arena results suggests reasonable screenshot/UI interpretation ability.
Large context window supports summarizing lengthy inputs, though no summarization-specific benchmark is documented.
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, aa-lcr.
curl https://api.stav.ai/v1/chat/completions \
-H "Authorization: Bearer $STAV_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "anthropic/claude-sonnet-5",
"reasoning_effort": "high",
"messages": [{"role": "user", "content": "Hello!"}]
}'→ X-Stav-Request-Id · X-Stav-Model · X-Stav-Provider → X-Stav-Sovereignty-Level — the rung the request was served at