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Claude Opus & Sonnet on Elevence AI
Anthropic's Claude 4.7 Opus and Sonnet 4.6 — on Elevence AI's OpenAI-compatible API.
Applies to web, iOS and Android
PROVIDER
Anthropic
CONTEXT
200K tokens
MODALITY
text
PRICING
Sonnet 4.6: $3 / $15 per million in/out · Opus 4.7: $15 / $75
Claude is Anthropic's flagship product family. Opus 4.7 is the highest-quality reasoning model in the lineup; Sonnet 4.6 is the production workhorse with excellent quality at a much lower price point; Haiku 4.5 is the fast, cheap model for high-throughput tasks. All three run on Elevence AI through the same OpenAI-compatible API.
Claude is particularly strong at long-context work, careful nuanced reasoning, and code generation. If your prompts are document-heavy, ambiguous, or require careful judgement, Claude often gives more reliable answers than GPT-5 at lower cost.
What Claude Opus & Sonnet is great at
Best-in-class long-context comprehension
Claude Sonnet 4.6 and Opus 4.7 maintain accuracy on retrieval and synthesis across the full 200K-token window. Pass a multi-chapter document and ask for a structured summary — Claude tracks references across the entire input without losing the thread.
Extended thinking for hard problems
Opus and Sonnet support extended thinking — the model produces a hidden reasoning trace before the visible answer. On Elevence AI you can enable this via the standard OpenAI-compatible reasoning_effort parameter; the trace is included in usage but hidden from the response by default.
Excellent code generation
Claude Sonnet 4.6 consistently ranks at the top of independent code-generation benchmarks (HumanEval, SWE-bench). Pair it with Elevence AI's code-execution tool to run generated snippets in a sandboxed Node.js environment.
Best for
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Long-context document reasoning and summarisation
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Code generation and refactoring
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Nuanced writing tasks where tone and judgement matter
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High-quality answers at moderate price (Sonnet 4.6 sweet spot)
Limitations
Opus is expensive at scale
Opus 4.7 at $15/$75 per million in/out tokens is among the most expensive frontier models. For most production use, Sonnet 4.6 delivers comparable quality at one-fifth the price.
Conservative on edge-case prompts
Claude's safety tuning sometimes refuses prompts that GPT-5 would handle — typically not a problem for normal product work but worth knowing if you're testing edge cases.
Code example
Drop-in OpenAI-compatible API. Swap the base URL and key, change the model field:
import OpenAI from 'openai';
const client = new OpenAI({
baseURL: 'https://api.elevence.ai/v1',
apiKey: process.env.ELEVENCE_API_KEY,
});
const response = await client.chat.completions.create({
model: 'claude-4-sonnet',
messages: [{ role: 'user', content: 'Refactor this function for clarity:\n\n' + code }],
stream: true,
});
Frequently asked questions
Which Claude model should I use?
Default to Sonnet 4.6 for production — best quality-per-dollar ratio. Upgrade to Opus 4.7 for the hardest reasoning tasks where quality is worth 5x the cost. Drop to Haiku 4.5 for high-throughput simple tasks (classification, extraction).
Does Claude on Elevence AI have the 200K context window?
Yes — Elevence AI passes the request to Anthropic with the full 200K window available. You pay per token used, so longer prompts cost more, but you can fill the window when you need to.
Can I use Claude with Elevence AI agents?
Yes. Custom agents can be configured with Claude (any variant) as the underlying model and combined with tools like web search, code execution, and file search.
Is Claude on Elevence AI the same as Claude.ai's Claude?
Yes. The models are identical — Elevence AI calls Anthropic's official API. The differences are billing (pay-as-you-go vs subscription), wrapper (one key for 60+ models), and feature surface (branching, sharing, OpenAI-compatible API).
How do I switch from Claude to GPT-5 in the same conversation?
In the studio: tap the model picker on any message and choose a different model — the conversation continues with the new model on the next response. Via the API: change the `model` field in subsequent requests within the same thread.
Related models
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