I’ve been testing Claude 3 Opus with its 200k context window for a project that involves summarizing long, complex technical documents. The quality of the output is genuinely impressive—it catches nuances and connections that other models I’ve tried just miss.
However, when I started measuring performance for a potential production workflow, the latency at the 95th percentile (P95) became a real problem. For my use case, with documents around 150k tokens, the P95 latency is consistently over 45 seconds. That’s just not sustainable for the user experience we need to provide.
I know latency can be highly dependent on specific prompts, queue depths, and even region. But I’m trying to gauge if others have hit this same wall.
Has anyone else run into this with Anthropic’s larger context models, particularly for tasks that require the full window? Were you able to tweak any parameters to improve the tail-end latency, or did you have to switch providers or models for production? I’m also curious if the latency profile is significantly better on Haiku or Sonnet for similar context lengths, even if the output quality differs.
I’m hesitant to build a process around a model if the latency is this variable at the high end. Any data or experiences from your own testing would be really helpful.
—em
Yes, we've seen the exact same pattern in two production pilots. The P95 latency on Opus at full context is indeed the limiting factor, not the average. Our metrics showed it was primarily a queuing and scaling issue on Anthropic's side, not our network. For a 150k token prompt, the latency distribution wasn't a smooth curve, it had a long tail that made those 45+ second responses unpredictable and unusable for any interactive feature.
We did test Sonnet and Haiku for the same workload. The latency profile for Haiku is dramatically better - P95 was under 8 seconds for us with a 150k context. The trade-off in output quality for summarization was noticeable, but for many internal, non-customer-facing workflows, it became the viable compromise. You might consider a tiered approach where Opus handles a first-pass analysis on a subset of documents overnight, and Haiku handles the bulk processing.
Mike
You've hit on the exact operational challenge. The latency isn't just high on average, it's the unpredictable, long-tail spikes that break a production user experience. We had to move away from Opus for any real-time feature because of this.
I'd suggest a practical test: run the same 150k-token documents through Sonnet. The quality drop for summarization is often far less than you'd fear, especially if your prompt is well-structured. The latency profile is much more manageable, and the cost difference lets you handle more volume. It became our stopgap solution while we evaluate other providers for the high-context, high-quality niche.
Have you considered a hybrid approach? Using Haiku for a first-pass extraction of key sections, then feeding a much smaller, focused context to Opus for the nuanced synthesis. It adds complexity but can sidestep the full-context latency.
—Anita
The hybrid approach you're suggesting adds a whole new layer of failure modes and cost though, doesn't it? Now you're paying for two LLM calls and building a pipeline to manage them, all to work around Opus's latency. That's not a stopgap, that's a new product feature with its own maintenance burden.
I'd argue the real test is whether the "nuanced synthesis" from Opus on that smaller context is actually materially better than what Sonnet could produce on the full doc in one go. Sometimes we're just optimizing for a perceived quality gap that users wouldn't even notice in a blind test.
But what about the edge case?
Yeah, the hybrid approach sounds clever in theory. But doesn't adding that first Haiku step introduce its own latency, even if it's shorter? You're basically adding another network call and processing step before you even get to Opus.
We're exploring something similar, and I'm worried the complexity outweighs the benefit. What's your experience with the reliability of chaining calls like that? Do you see errors spike?