GPT-4o vs Claude 3.5 Sonnet: vision latency compared

When you’re shipping a multimodal feature, the difference between a snappy response and a noticeable stall shapes the entire UX. This practical look at GPT-4o vs Claude 3.5 Sonnet vision latency cuts through vendor charts and focuses on request-level behavior engineers care about: time to first token, throughput, and cost per image.

Both models accept images alongside text, but they were built on different assumptions. GPT-4o is a natively unified model handling text, vision, and audio in one forward pass. Claude 3.5 Sonnet treats vision as a first-class input modality with a separate encoder front-end. That architectural split shows up in the latency profile.

Capabilities

Vision quality and input types

GPT-4o ingests images at arbitrary resolutions and converts them into a token grid scaled to content. It also accepts audio and can return audio, which neither Claude 3.5 Sonnet nor any other mainstream frontier model matches today. For pure image+text prompts, it handles screenshots, photos, and diagrams with strong OCR and spatial reasoning.

Claude 3.5 Sonnet is text-and-vision only. Its vision strength lies in dense documents, charts, and code repositories rendered as images. Public eval suites (e.g., MMMU, MathVista) show it trading wins with GPT-4o depending on the benchmark. For engineering use, both are competent; neither produces systematic hallucinations on clean diagrams.

Multimodal scope

If your product needs real-time voice with vision, GPT-4o is the only option. If you need to pipe a 30-page PDF as images, Claude’s 200K context window gives more headroom before you must chunk. GPT-4o’s 128K context is sufficient for most interactive sessions but forces tighter batching on large document sets.

Price and cost model

OpenAI prices GPT-4o at $5 per million input tokens and $15 per million output tokens. Anthropic prices Claude 3.5 Sonnet at $3 per million input and $15 per million output. Image tokens count toward input.

Tokenization differs:

• GPT-4o uses a variable scheme. A 512×512 image costs roughly 1,000–1,300 tokens; a 1080p frame can exceed 5,000.
• Claude 3.5 Sonnet charges per image based on resolution tiers. An image under 1 megapixel costs 1,152 tokens; larger images are split into tiles at additive cost.

A simple cost estimate for 10,000 small screenshots:

# GPT-4o approx
gpt4o_input = 10_000 * 1200 / 1e6 * 5  # $60
# Claude 3.5 Sonnet
claude_input = 10_000 * 1152 / 1e6 * 3  # $34.56

Output tokens dominate if you ask for long descriptions. Both charge equally on output side. If you cache repeated images, Claude’s prompt caching reads at $0.30/M input tokens skew the math further in its favor for iterative workloads.

Latency and throughput

The phrase GPT-4o vs Claude 3.5 Sonnet vision latency usually hides two distinct metrics: time to first token (TTFT) and tokens per second (TPS) once streaming starts.

Public latency aggregators consistently show GPT-4o with lower median TTFT on image-bearing requests. The unified architecture avoids a separate vision-encoder handoff, so the decoder starts generating sooner. Claude 3.5 Sonnet adds a small fixed overhead for image preprocessing, but its inter-token throughput is competitive on large outputs.

In practice:

• For a 512px image + 50-word question, GPT-4o often streams first token within a few hundred milliseconds on warm connections. Claude 3.5 Sonnet lands slightly later but within the same order of magnitude.
• For multi-image prompts (e.g., 5 screenshots), Claude’s tiling can increase TTFT linearly; GPT-4o’s grid scales similarly but with different constant factors.

Network and provider load matter more than model design at the tail. Routing both through a single OpenAI-compatible gateway such as n4n.ai lets you A/B latency without client changes; the endpoint honors routing directives and falls back when a provider is degraded, so you can measure both under identical conditions.

Measuring correctly requires controlling variables:

import time, requests

def ttft(model, url, api_key):
    start = time.perf_counter()
    r = requests.post("https://api.n4n.ai/v1/chat/completions",
        headers={"Authorization": f"Bearer {api_key}"},
        json={"model": model, "messages":[{"role":"user","content":[
            {"type":"image_url","image_url":{"url":url}},
            {"type":"text","text":"What is this?"}]}],"stream":True},
        stream=True)
    for chunk in r.iter_lines():
        if chunk:
            return time.perf_counter() - start  # seconds to first byte

Run from the same region, reuse TLS connections, and discard the first call (cold model load). Only then does the GPT-4o vs Claude 3.5 Sonnet vision latency gap become reproducible.

Ergonomics

API shape is the biggest friction point.

OpenAI-compatible request:

{
  "model": "gpt-4o",
  "messages": [
    {"role": "user", "content": [
      {"type": "image_url", "image_url": {"url": "data:image/png;base64,..."}},
      {"type": "text", "text": "Describe."}
    ]}
  ]
}

Anthropic Claude request:

{
  "model": "claude-3-5-sonnet-20240620",
  "messages": [
    {"role": "user", "content": [
      {"type": "image", "source": {"type": "base64", "media_type": "image/png", "data": "..."}},
      {"type": "text", "text": "Describe."}
    ]}
  ]
}

If you standardize on the OpenAI schema, a gateway that maps anthropic/claude-3.5-sonnet to the native format saves rewrite cost. Both support streaming, function calling, and system prompts. Python SDKs differ: openai exposes client.chat.completions, while anthropic uses client.messages.create with content blocks. The mental overhead is real when you maintain both.

Ecosystem

GPT-4o benefits from the OpenAI tooling avalanche: assistants, realtime API, and broad third-party integrations. Claude 3.5 Sonnet has first-class prompt caching (cache writes at $3.75/M, reads at $0.30/M) which dramatically cuts repeat-document costs. For vision, caching the image tokens across turns is a Claude advantage when you iterate on the same screenshot.

Both support structured outputs via JSON mode or tool schemas. Neither guarantees bounding boxes; if you need coordinate-level vision, you still post-process or use specialized models. Function calling latency is comparable, but GPT-4o’s tool parser is marginally faster on nested schemas in our proxy traces.

Limits

• Context: GPT-4o 128K, Claude 3.5 Sonnet 200K.
• Max images per call: both allow multiple; Claude documents a soft limit of 20 images per message, OpenAI does not specify but token cost bounds it.
• Resolution: GPT-4o downsamples large images internally; Claude tiles images larger than 1MP.
• Audio: GPT-4o native, Claude absent.
• Fine-tuning: Neither offers custom vision fine-tunes on these specific weights; you rely on prompt engineering.

Head-to-head table

Which to choose

Real-time camera or voice-vision apps: GPT-4o. The native audio stack and lower TTFT make it the only pragmatic choice for live multimodal interaction.

Document extraction at scale: Claude 3.5 Sonnet. Cheaper input tokens, larger context, and prompt caching on repeated templates offset the minor latency penalty.

Cost-sensitive batch labeling: Claude 3.5 Sonnet wins on input price if images are under 1MP. If you need audio or unified modality, GPT-4o justifies the premium.

Latency-critical single-image Q&A: GPT-4o edges out on first-token delay. If your UI shows a spinner, users feel the difference.

Mixed workloads with fallback needs: Use a routing layer that treats both as drop-in alternatives. That way a degraded provider doesn’t take down your feature, and you can shift traffic based on live GPT-4o vs Claude 3.5 Sonnet vision latency measurements.

Pick based on the dominant constraint: modality coverage, context size, or per-token budget. Both are production-grade; the gap is operational, not qualitative.