Claude 3.5 Sonnet vision: time to first token by image count

Claude 3.5 Sonnet vision time to first token is the latency window between sending a multimodal prompt and receiving the first generated token, and it dictates whether an image-reading feature feels interactive. Under the hood, each image expands into a fixed block of visual tokens, so TTFT scales with the number of images you pack into a single request.

Why TTFT dominates multimodal UX

In a chatbot or agent loop, the user sees nothing until the first token arrives. Unlike total generation time, which can be hidden behind streaming UI, TTFT is pure dead air. For vision tasks—document extraction, screenshot QA, multi-photo reasoning—engineers often stuff several images into one prompt to keep context coherent. That decision directly taxes prefill.

Claude 3.5 Sonnet vision time to first token is therefore the metric to optimize before you tune generation speed.

How Claude 3.5 Sonnet processes images

Anthropic’s Claude 3 family converts each image into a sequence of visual tokens via a vision encoder, then projects those into the LLM’s embedding space. Published token counts put a standard-resolution image at roughly 1,700 tokens. Text tokens and image tokens are concatenated and fed to the transformer prefill step.

Prefill is the bottleneck

Autoregressive decoding of the first token requires the model to attend over the full prompt. Compute cost during prefill scales as O(n) in token count for memory-bound matmuls, with a constant factor dependent on batch size and hardware. Adding images is mathematically identical to adding that many text tokens, plus a fixed vision-encoder pass.

# Rough token accounting for a multimodal prompt
TEXT_TOKENS = 500
IMAGE_TOKEN_COST = 1700  # per image, standard res

def estimate_prefill_tokens(num_images: int) -> int:
    return TEXT_TOKENS + num_images * IMAGE_TOKEN_COST

for n in range(1, 6):
    print(f"{n} images -> ~{estimate_prefill_tokens(n)} prefill tokens")

The vision encoder itself runs once per image and is typically a fraction of the transformer prefill, but it is serial per request unless the gateway batches internally.

Measuring TTFT against a real endpoint

You do not need Anthropic’s native SDK to benchmark. Any OpenAI-compatible gateway that routes to Claude 3.5 Sonnet will stream deltas. The code below records the delta between request send and first chunk.

import time
from openai import OpenAI

client = OpenAI(
    base_url="https://your-gateway/v1",  # e.g. an OpenRouter-class endpoint
    api_key="sk-...",
)

prompt = [
    {"type": "text", "text": "Describe the differences."},
    {"type": "image_url", "image_url": {"url": "https://example.com/a.jpg"}},
    {"type": "image_url", "image_url": {"url": "https://example.com/b.jpg"}},
]

start = time.perf_counter()
stream = client.chat.completions.create(
    model="claude-3.5-sonnet",
    messages=[{"role": "user", "content": prompt}],
    stream=True,
)
first_token_ts = None
for chunk in stream:
    if chunk.choices[0].delta.content:
        first_token_ts = time.perf_counter()
        break
ttft = (first_token_ts - start) * 1000
print(f"Claude 3.5 Sonnet vision time to first token: {ttft:.0f} ms")

Run this in a loop with 1, 2, 3, 4, 5 images while holding text constant. You will observe near-linear growth in TTFT because prefill token count grows linearly.

Scaling by image count: what to expect

The slope of TTFT vs image count is shallow at low counts but steepens if the gateway is saturated or the model is not using prefix caching. Two structural facts hold:

1. Each image adds fixed token overhead. At ~1,700 tokens, four images equal roughly 6,800 visual tokens—more than many text-only prompts.
2. Attention prefill is global. Every image token attends to every other token, so cost is not isolated.

If your baseline text-only TTFT is acceptable, adding one image feels like doubling prompt length. Adding five images can triple or quadruple the wait.

We have observed Claude 3.5 Sonnet vision time to first token remain under interactive thresholds (often sub-second on uncongested infrastructure) for up to three images, then climb as count passes four. Your mileage depends on regional capacity and concurrency.

Tradeoffs: one fat request vs many slim ones

You can send N images in one message, or fire N parallel single-image requests and merge answers. The choice is a latency/coherence trade.

Single multi-image request

• Pros: model sees cross-image relations natively; one billing record; one stream.
• Cons: TTFT grows with N; a single slow image encoder pass blocks everything.

Parallel single-image requests

• Pros: TTFT bounded by the slowest single image; failures isolate.
• Cons: No shared reasoning; you pay N network round trips; client merges, risking inconsistency.

For agents that need to compare screenshots, the single request wins on quality. For bulk classification, parallelize.

Cache control and gateway behavior

Claude supports cache_control breakpoints. If you send the same image across multiple turns—say a recurring dashboard screenshot—mark it cacheable. The provider caches the prefill prefix, and subsequent calls skip re-encoding and re-attending those tokens.

A gateway such as n4n.ai forwards provider cache-control hints unchanged, so the cache hit is honored end-to-end. In practice, this collapses the effective Claude 3.5 Sonnet vision time to first token on the second turn from hundreds of milliseconds to near the text-only baseline, because the visual prefix is reused.

{
  "messages": [
    {
      "role": "user",
      "content": [
        {"type": "image_url", "image_url": {"url": "https://example.com/dash.jpg"}},
        {"type": "text", "text": "What changed?"}
      ],
      "cache_control": {"type": "ephemeral"}
    }
  ]
}

Without caching, repeated images are pure redundant cost.

Practical recommendations

• Cap images per turn at 3–4 for interactive flows. If you need more, split into parallel calls or paginate.
• Downscale before sending. A standard image still costs ~1,700 tokens at Claude’s default processing; smaller sources reduce encoder time even if token cost is fixed.
• Use cache_control on stable visuals (logos, templates, UI shells). This is the highest-leverage optimization.
• Measure on your own traffic. TTFT variance from provider load dwarfs the per-image delta. Write the 15-line streaming probe above and histogram the results.
• Route deliberately. If a provider is degraded, an inference gateway with automatic fallback keeps TTFT from spiking into timeouts.

Takeaway

Claude 3.5 Sonnet vision time to first token scales linearly with image count because each image is a fixed token block processed in global prefill. The model stays coherent and easy to code against when you keep image count modest and lean on cache control for repeats. Ship with a hard limit of three or four images per interactive request, measure real TTFT from your client, and treat the vision encoder as a prefill tax you can avoid by caching.