LLManthropicPlan: Pro and up

Anthropic: Claude Opus 4.7 (Fast)

Fast-mode variant of [Opus 4.7](/anthropic/claude-opus-4.7) - identical capabilities with higher output speed at premium 6x pricing. Learn more in Anthropic's docs: https://platform.claude.com/docs/en/build-with-claude/fast-mode

Anyone in the Space can @-mention Anthropic: Claude Opus 4.7 (Fast) with the team's shared context — pooled credits, one chat, one memory.

All models

Verdict

Claude Opus 4.7 (Fast) delivers Anthropic's highest-tier reasoning in a speed-optimized package. The million-token context window handles entire codebases or document sets in one pass, while multimodal support covers text, images, and file uploads. At $30/$150 per Mtok, you're paying premium rates for premium capability — reach for this when latency matters more than cost and the task demands deep analysis across massive context.

Best for

Low-latency analysis of large codebases
Real-time document Q&A with file uploads
Multimodal reasoning on screenshots and diagrams
Complex research synthesis under time pressure
Interactive debugging sessions with full context

Strengths

The million-token window eliminates chunking overhead for repository-scale code review or multi-document synthesis. Multimodal input handles mixed media workflows — paste a screenshot, upload a PDF, add text context in one request. The 'Fast' designation suggests optimized inference without sacrificing Opus-tier reasoning depth, making it viable for interactive use cases where Claude 3.5 Sonnet's speed wasn't enough but full Opus latency was prohibitive.

Trade-offs

Output pricing at $150/Mtok makes verbose responses expensive fast — a 10k-token summary costs $1.50 in output alone. Without public benchmarks yet, performance relative to Claude 3.5 Sonnet or GPT-4o remains unverified in head-to-head tests. The 'Fast' variant likely trades some accuracy for speed compared to standard Opus 4.7, though Anthropic hasn't published specifics. For cost-sensitive batch work, cheaper models will deliver better ROI.

Specifications

Provider: anthropic
Category: llm
Context length: 1,000,000 tokens
Max output: 128,000 tokens
Modalities: text, image, file
License: proprietary
Released: 2026-05-12

Pricing

Input: $30.00/Mtok
Output: $150.00/Mtok
Model ID: anthropic/claude-opus-4.7-fast

Per-token prices show what the model costs upstream. On Switchy your team draws from one shared org credit pool — one plan, one balance for everyone.

Team cost calculator

Seats5 peopleMessages / seat / day80Avg turn size2 ktokOutput share30 %

Estimated monthly spend

$1161.60

17.6M tokens / month
5 seats · 80 msgs/day

Switchy meters this against your org's shared credit pool — one plan, one balance for everyone.

Providers

Provider-level routing data is not available yet for this model.

Performance

Performance snapshots are collected daily. Check back after the next ingestion run.

Benchmarks

Public benchmark scores are not available yet for this model. Check back after the next ingestion run.

Works well with

Top MCPs

Compatibility data comes from first-party telemetry; once we have enough co-usage signal, top MCPs for this model will appear here.

How Switchy teams use it

Not enough Spaces have used this model yet to share anonymised team stats. We wait for at least 50 distinct Spaces per week before publishing any aggregate.

Starter prompts

Codebase Architecture Review

Review this codebase for architectural patterns, coupling issues, and technical debt. Highlight the three highest-impact refactoring opportunities and explain the current design trade-offs.

Open in a Space →

Multi-Document Synthesis

Synthesize key findings from these documents. Identify consensus points, contradictions, and gaps in the research. Prioritize actionable insights for product strategy.

Open in a Space →

Screenshot Debugging

Here's a screenshot of the bug and the relevant component code. Explain what's causing the rendering issue and provide a fix with minimal changes to existing logic.

Open in a Space →

Contract Clause Analysis

Review this contract for liability clauses, termination conditions, and non-standard terms. Flag anything that deviates from typical SaaS agreements and assess risk level.

Open in a Space →

Real-Time Research Assistant

Using the full dataset I uploaded, answer this question with specific citations: [your question]. If the data doesn't support a conclusion, say so and explain what's missing.

Open in a Space →