Claude Opus 4.5 time-to-first-token across providers

Claude Opus 4.5 time to first token is the wall-clock delay between dispatching a request and rendering the first streamed token, and it dictates whether your application feels instantaneous or sluggish. That latency is not a fixed property of the weights; it is an emergent result of where the model runs, how the provider batches requests, and whether the prompt cache is warm. Measuring and comparing Claude Opus 4.5 time to first token across providers reveals infrastructure gaps that matter more than raw model speed.

What TTFT actually measures

Time-to-first-token strips out generation throughput and isolates the synchronous front of the request lifecycle. It includes DNS, TLS, request parsing, auth, scheduler queueing, model load or cache lookup, and the first forward pass of the prefill step. For a 200B-class model like Opus, prefill of a long context can dominate if the provider computes attention naively, but most mature stacks use flash-attention and paged KV caches to keep it sub-second under light load.

The number engineers should care about is not the median in a quiet test. It is the p95 or p99 under your real traffic shape. A provider that posts a snappy demo TTFT can still fall apart when your burst hits its global rate limit.

The prefill bottleneck under the hood

Opus-class models process the entire input prompt in a single forward pass before emitting any token. That prefill cost scales with sequence length and the batch your request shares a GPU step with. Providers that pack your request with others on the same accelerator inflate your TTFT linearly with their batch size. Some expose no visibility into this; you only see the latency.

If you send a 32K-token legal doc, expect prefill to dominate regardless of provider. The differentiator is whether the provider caches the KV state for that prefix. Anthropic’s ephemeral cache does exactly that. Bedrock’s caching is implicit and less controllable. Vertex may use model-garden optimizations you cannot tune. Understanding this separates the part of TTFT you can optimize from the part you can only route around.

Why Claude Opus 4.5 TTFT varies by provider

The model artifact is identical across authorized endpoints. The serving layer is not.

Direct Anthropic API

Anthropic runs its own inference fleet. Requests hit a centralized scheduler that scales with aggregate demand. Under normal conditions you get low queue delay because they provision for their own traffic. During launch spikes or regional incidents, you absorb the same contention as every other direct user. There is no isolation per customer unless you negotiate dedicated capacity.

Prompt caching is first-class: mark a prefix with cache_control and subsequent calls with the same prefix skip prefill entirely. That can turn a multi-second TTFT into tens of milliseconds. The tradeoff is cache scope—Anthropic’s cache is per-region and expires after a short TTL.

Bedrock and Vertex

Cloud marketplaces repackage the same model on their own accelerators. AWS Bedrock multiplexes Opus behind account-level quotas. If your AWS account is fresh or pinned to a low service quota, you will see throttling errors or long queue spins before the first token. Bedrock’s cross-region inference profiles help, but they add routing latency.

Vertex AI runs on GCP infrastructure. Their batch scheduler is optimized for throughput, not interactive latency, so a single low-traffic project can experience cold starts when a scaled-to-zero replica wakes. Claude Opus 4.5 time to first token on Vertex is therefore bimodal: fast if a warm replica exists, slow if not.

Aggregators and gateways

Resellers proxy to the above sources and add a hop. The extra network RTT is usually negligible compared to queue time, but the gateway’s own connection pooling and token accounting can either smooth or worsen TTFT. A gateway that opens a new upstream connection per request pays TLS handshake tax; one that keeps warm pools hides it.

An OpenRouter-class gateway such as n4n.ai exposes a single OpenAI-compatible endpoint for 240+ models and will honor your routing directives while forwarding provider cache-control hints, so you can pin Claude Opus 4.5 to a primary provider and fail over automatically when that provider degrades.

Measuring it correctly

You cannot trust a single curl from your laptop at 2 a.m. Write a probe that runs from the same region as your production caller, uses streaming, and records the timestamp at the first byte of content.

import time
from openai import OpenAI

client = OpenAI(base_url="https://your-endpoint/v1", api_key="sk-...")
start = time.perf_counter()
stream = client.chat.completions.create(
    model="claude-opus-4.5",
    messages=[{"role": "user", "content": "Summarize TTFT."}],
    stream=True,
)
for chunk in stream:
    if chunk.choices[0].delta.content:
        ttft_ms = (time.perf_counter() - start) * 1000
        print(f"first token after {ttft_ms:.0f}ms")
        break

Run this in a loop with concurrent workers that mimic your peak QPS. Capture p50, p95, p99. Repeat across providers with identical payloads and cache states.

Accounting for cache hits

Always label which run uses a cached prefix. With Anthropic’s native API:

client.messages.create(
    model="claude-opus-4.5",
    system=[{"type": "text", "text": "LONG_STATIC_CONTEXT",
             "cache_control": {"type": "ephemeral"}}],
    messages=[{"role": "user", "content": "Go."}],
)

If the gateway forwards cache_control, the same hint reaches the upstream provider. A miss forces full prefill; a hit bypasses it. Reporting TTFT without this split is misleading.

Reading provider status pages honestly

Status pages report “degraded performance” after the fact. They rarely show TTFT percentiles. Build your own synthetic canary that calls each provider every minute and logs TTFT to a timeseries. When Anthropic’s page goes green but your canary shows 3s p95, trust the canary. The same applies to Bedrock and Vertex, whose health dashboards aggregate across all models and hide Opus-specific queueing.

Tradeoffs: cost, locality, and queueing

Low TTFT is not free. Direct Anthropic gives predictable latency but ties you to one auth and one outage domain. Bedrock gives you IAM and possibly better compliance posture, but you fight AWS quota mechanics. Vertex offers GCP integration yet risks cold replicas.

Aggregators add resilience. Automatic fallback keeps p99 TTFT bounded when a provider rate-limits, but fallback itself costs a restarted request—you pay the prefill twice if the first attempt already streamed partial tokens. Design for idempotent retries: abort the slow stream at p95 of expected TTFT, fire a fresh one to the backup, and discard the stale partial.

Geographic locality matters. Routing Claude Opus 4.5 to a US-east endpoint from APAC doubles RTT before any compute. Some gateways let you specify region hints; use them.

Routing strategies that keep p99 sane

Client-side routing directives shift control from the provider to your code. A minimal JSON hint passed via gateway might look like:

{
  "model": "claude-opus-4.5",
  "route": {
    "prefer": ["anthropic", "bedrock"],
    "fallback_on": ["rate_limit", "timeout"],
    "max_ttft_ms": 1500
  }
}

The gateway tries the preferred provider, watches the stream, and if max_ttft_ms elapses with no token, cuts over. This requires the gateway to support speculative fallback; not all do.

Per-token usage metering lets you attribute cost when fallback happens. If you blindly retry on every slow request, you inflate spend. Set a circuit breaker: after N fallbacks in a window, shed load or degrade to a smaller model rather than hammering Opus.

Cache-control forwarding

Ensure your gateway forwards cache_control or its OpenAI-equivalent prefix extension. Otherwise your carefully warmed prefix goes cold at the proxy boundary, and Claude Opus 4.5 time to first token reverts to full prefill on every call. Test by sending the same long system prompt twice and comparing TTFT delta.

Decisive takeaway

Claude Opus 4.5 time to first token is a function of the serving path, not the model. Benchmark it from your production region with cache hits and misses separated, under realistic concurrency, across every provider you can legally access. Then put a routing layer in front that pins a primary, fails over on latency or throttle, and respects cache hints. Do that and your p99 TTFT stays under human perception thresholds even when a single provider wobbles—without rewriting your app for each vendor’s SDK.