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Google LLC (Vertex AI)

claude-opus-4-7

Opus 4.7 is the next generation of Anthropic's Opus family, built for long-running, asynchronous agents. Building on the coding and agentic strengths of Opus 4.6, it delivers stronger performance on complex, multi-step tasks and more reliable agentic execution across extended workflows. It is especially effective for asynchronous agent pipelines where tasks unfold over time - large codebases, multi-stage debugging, and end-to-end project orchestration. Beyond coding, Opus 4.7 brings improved knowledge work capabilities - from drafting documents and building presentations to analyzing data. It maintains coherence across very long outputs and extended sessions, making it a strong default for tasks that require persistence, judgment, and follow-through.

πŸ‘Vision🧠ReasoningπŸ”§Tool calling⚑CachingπŸ–₯Computer use
Try claude-opus-4-7CompareDocs

Specifications

Context window1M tokens
Max output128K tokens
API typechat
AddedFeb 5, 2026
Model IDvertex/claude-opus-4-7
Data retentionNo
Used for trainingNo
Provider locationπŸ‡ΊπŸ‡Έ US / πŸ‡ͺπŸ‡Ί EU
Privacy policyVertex AI Data Governance β†’

Benchmarks

Released 2026-03
SWE-Bench Verifiedcoding
78.6%

Resolving real GitHub issues from 12 popular Python repositories.

GPQA Diamondreasoning
83.4%

Graduate-level physics, chemistry & biology questions designed to resist Googling.

MMLU Proknowledge
89.1%

Massive Multitask Language Understanding across 57 academic subjects.

Scores are sourced from official model cards, Artificial Analysis, and public leaderboards. Benchmarks measure specific skills and do not capture every aspect of model quality β€” always test on your own workload.

Pricing

Input / 1M
$5.00
Output / 1M
$25.00
Cache write / 1M
$6.25
Cache read / 1M
$0.50
Estimated cost
100K input + 10K output$0.75
1M input + 100K output$7.50
10M input + 1M output$75.00

Requesty charges exactly what the upstream provider charges β€” no markup, no per-request fees. Prompt caching and smart routing can reduce effective cost by 30-80%.

Quickstart

Drop-in compatible with the OpenAI SDK. Change the base URL, swap in your Requesty API key, and set the model to vertex/claude-opus-4-7.

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from openai import OpenAI

client = OpenAI(
    api_key="YOUR_REQUESTY_API_KEY",
    base_url="https://router.requesty.ai/v1",
)

response = client.chat.completions.create(
    model="vertex/claude-opus-4-7",
    messages=[
        {"role": "user", "content": "Explain quantum computing in one paragraph."},
    ],
)

print(response.choices[0].message.content)

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Frequently asked questions

How much does claude-opus-4-7 cost?
claude-opus-4-7 is priced at $5.00 per million input tokens and $25.00 per million output tokens when accessed via Requesty. Prompt caching is supported, which can cut effective input cost by up to 90% on repeated context. Requesty charges exactly what the upstream provider charges β€” we don't add markup.
What is the context window of claude-opus-4-7?
claude-opus-4-7 has a context window of 1M tokens, with a maximum output of 128K tokens per response. That's roughly 1,333 words of input you can fit in a single prompt.
How does claude-opus-4-7 perform on benchmarks?
claude-opus-4-7 scores 95.2% on HumanEval, 94.8% on MATH, 91.5% on AIME 2024. See the full benchmark chart above for results across MMLU Pro, GPQA Diamond, SWE-Bench Verified, HumanEval, MATH, AIME, MMMU, and LiveBench.
What can claude-opus-4-7 do?
claude-opus-4-7 supports vision input, tool calling, extended reasoning, prompt caching, computer use. You can call it through any OpenAI-compatible client by pointing base_url to Requesty.
How do I use claude-opus-4-7 with the OpenAI SDK?
Install the OpenAI SDK, set base_url to "https://router.requesty.ai/v1", set your API key to your Requesty key, and set the model to "vertex/claude-opus-4-7". The Quickstart above shows Python, JavaScript and cURL snippets.

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One API key, 400+ models, OpenAI-compatible. No markup on provider prices, automatic failover, and smart caching built-in.

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