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Model details

Claude Opus 4.8 Fast

Claude Opus 4.8 Fast is a latency-optimized sibling within Anthropic's Opus family, designed to preserve the reasoning depth of the standard Opus 4.8 while answering more quickly. The model carries the same multimodal foundation as the broader Claude line, accepting text and images as input and producing text responses, and it exposes a full set of developer-oriented features including tool calling, structured outputs, and an explicit reasoning mode. The "Fast" designation, per Anthropic's own fast-mode documentation, trades cost for speed, charging roughly double the price of the regular Opus 4.8 in exchange for higher output tokens-per-second throughput, which positions it for interactive applications where response time matters as much as answer quality.

Because Fast variants in the Claude family are explicitly described as reusing the underlying Opus 4.8 weights and training lineage, this release inherits the same large-scale pretraining, alignment, and post-training work that shaped the flagship, with the speed profile layered on top rather than a separately cultivated smaller model. Practical strengths therefore mirror Opus 4.8: long-context document and codebase analysis, visual reasoning over images, agentic tool use, and chain-of-thought responses steered by the reasoning parameter. Its fit is forward-looking for high-traffic agent loops, real-time copilots, and batch pipelines where every saved second of latency compounds, and the prompt-caching layer reported on hosted deployments can push effective input costs well below the headline rate for workloads with repeated context.

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Provider
Venice AI
Model key
claude-opus-4-8-fast
Release date
May 28, 2026
Last updated
Jun 11, 2026
Knowledge cutoff
2026-01
Input modalities
Output modalities
Capabilities

Cost

Input token cost
$12.00
Output token cost
$60.00

Limits

Output tokens
128,000 tokens
Context window
1,000,000 tokens

Transparent token rates

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Rates are shown per one million tokens. Combined means one million input plus one million output tokens.

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