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Anthropic: Claude Opus 4.7Active

anthropic/claude-opus-4.7
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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.

By:AnthropicInput Type:Output Type:Publish time:2026-04-16

Providers

Route requests across multiple providers. Copy a provider slug to set your preference.

ProviderCache Hit RateContext
Anthropic
anthropic
58.8%$5/ M tokens$25/ M tokensRead:0.5/ M tokensWrite:6.25-10/ M tokens1M0.95s6.49tps
Google Vertex
google-vertex
79.1%$5/ M tokens$25/ M tokensRead:0.5/ M tokensWrite:6.25-10/ M tokens1M1.07s36.9tps
Amazon Bedrock
amazon-bedrock
-$5/ M tokens$25/ M tokensRead:0.5/ M tokensWrite:6.25-10/ M tokens1M--

Uptime

Direct request success rate on AI Gateway and per-provider.

Oct 26, 19:59
Anthropic100.00%
Google Vertex100.00%

Throughput

P50 throughput on live AI Gateway traffic, in tokens per second (TPS).

Oct 26, 19:00
Anthropic6.49 tps
Google Vertex36.9 tps

Latency

P50 time to first token (TTFT) on live AI Gateway traffic, in milliseconds.

Oct 26, 19:00
Anthropic0.95s
Google Vertex1.07s

Activity

Token volume and request traffic to this model over time.

Token Consumption
Oct 25
Anthropic4.9M
Google Vertex5.4M
Amazon Bedrock-

Token volume broken down by the provider that served each request. Larger segments indicate providers handling more of this model's traffic.

Benchmarks

Scores on standardized evaluations. Higher percentages are better — and rank percentile shows

Metrics sourced fromArtificial Analysis

Apps

Public apps that send the most traffic to this model. Good signal for what real production workloads look like — and a hint at which use cases this model is best suited for. View All

Token Ranking

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