Comparing streaming latency: GPT-4o vs Claude vs Gemini

When you plot streaming latency GPT-4o Claude Gemini under real request patterns, the gaps are less about headline speed and more about tail behavior, time-to-first-token, and how each vendor packs the SSE stream. This comparison breaks down the three flagship APIs across the dimensions that actually affect product feel: capabilities, cost, throughput, and ergonomics.

Streaming mechanics

All three expose incremental token delivery over HTTP, but the wire formats differ enough to shape your client code.

GPT-4o

OpenAI uses Server-Sent Events with data: {json} frames and a terminal data: [DONE]. The OpenAI Python SDK hides this behind an iterator.

from openai import OpenAI
client = OpenAI()
stream = client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role":"user","content":"Explain Raft"}],
    stream=True,
)
for chunk in stream:
    if chunk.choices[0].delta.content:
        print(chunk.choices[0].delta.content, end="")

Claude

Anthropic emits named events: message_start, content_block_start, content_block_delta, content_block_stop, message_stop. The delta carries text or input_json_delta for tool use.

import anthropic
client = anthropic.Anthropic()
with client.messages.stream(
    model="claude-3-5-sonnet-20240620",
    max_tokens=1024,
    messages=[{"role":"user","content":"Explain Raft"}]
) as stream:
    for text in stream.text_stream:
        print(text, end="")

Gemini

Google’s SDK returns a generator of GenerateContentResponse chunks. Each chunk has a .text property that concatenates parts.

import google.generativeai as genai
model = genai.GenerativeModel("gemini-1.5-pro")
response = model.generate_content("Explain Raft", stream=True)
for chunk in response:
    print(chunk.text, end="")

Dimensions of comparison

Capabilities

GPT-4o accepts text and image inputs, supports function calling and JSON mode, and handles audio in preview forms. Claude 3.5 Sonnet focuses on long-context reasoning, vision, and precise tool use; its document comprehension is strong. Gemini 1.5 Pro ships a 1M-token context window, native multimodal input, and grounded tool calling via Vertex.

Price/cost model

All three bill per input and output token. Claude introduces explicit cache write/read pricing: you pay a premium to persist a prefix across requests. Gemini offers context caching with reduced token cost for repeated long prefixes. GPT-4o has no public cache discount outside the batch API (50% off, asynchronous). None of these models charge for stream setup; you pay only for tokens delivered.

Latency/throughput

This is where streaming latency GPT-4o Claude Gemini diverges most. GPT-4o generally posts the lowest time-to-first-token (TTFT) on small prompts—often sub-second—with token rates around 60–100 tokens/s. Claude shows higher TTFT (frequently 800ms–1.5s) but a remarkably flat inter-token interval; the stream feels mechanical and predictable. Gemini’s TTFT is competitive with GPT-4o when a cached context is warm, but cold starts on massive prompts add noticeable delay. Under concurrency, all three shift latency into queue time rather than jittery token gaps.

Ergonomics

OpenAI’s schema is the lingua franca; most proxy layers translate to it. Claude’s multi-event stream gives explicit block boundaries, which helps when you stream structured tool calls. Gemini’s iterator is the least code but couples you to Google’s response shape unless you wrap it.

Ecosystem

GPT-4o benefits from the largest third-party toolchain: LangChain, Vercel AI, and countless gateway presets. Claude has first-class SDKs and growing support in agent frameworks. Gemini lives inside Google Cloud; if you already run on GCP, the IAM and logging story is smoother.

Limits

GPT-4o: 128k context, default 4k output (max 16k). Claude: 200k context, max output 8k (some tiers 16k). Gemini: 1M context, output capped at 8k tokens per request. Rate limits are account-tier dependent and manifest as HTTP 429 or prolonged TTFT.

Comparison table

Measuring latency in your own stack

Vendor dashboards hide tail behavior. Instrument the client:

import time
start = time.time()
first_token = None
for chunk in stream:
    if first_token is None and hasattr(chunk, "text") and chunk.text:
        first_token = time.time() - start
    # forward chunk to UI
print(f"TTFT: {first_token*1000:.0f}ms, total: {(time.time()-start)*1000:.0f}ms")

Run this against each provider with identical prompts and concurrency. An inference gateway such as n4n.ai can aggregate these measurements across providers, honoring your routing directives and automatically failing over when a provider is rate-limited, so you can compare streaming latency GPT-4o Claude Gemini under identical load. It also forwards provider cache-control hints, so a single cache_control flag reaches Claude or Gemini without branching your code.

Tail latency and degradation

In practice, median latency is easy. The painful part is p99. GPT-4o’s TTFT can spike when OpenAI throttles new connections; the stream itself stays smooth. Claude tends to queue at the API edge, pushing TTFT out but not stalling mid-stream. Gemini’s streaming may pause if the backend recomputes a cache miss on a huge context. If you front these with a gateway that supports fallback, a degraded provider automatically reroutes to the next healthy one, preserving perceived streaming latency GPT-4o Claude Gemini continuity for the user.

Which to choose

Interactive chat where perceived speed wins

Use GPT-4o. Its low TTFT makes the first character appear almost immediately. Claude is viable if you need 200k context in the same session, but warn users about the initial pause.

Long-document analysis with cached context

Gemini 1.5 Pro with context caching gives the widest window and predictable stream once warm. Claude is the fallback when you require finer tool use or strict data handling.

Agentic loops with many sequential calls

Claude’s steady throughput and explicit block deltas simplify parsing of intermediate tool calls. GPT-4o remains attractive if you prioritize raw speed per call and can tolerate occasional 429s.

Cost-sensitive batch with live progress

Gemini or Claude cache discounts beat GPT-4o unless you can shift to OpenAI’s batch endpoint (which sacrifices real-time streaming). For user-facing progress bars, stream Claude or Gemini with cached prefixes.

Pick based on measured p95 TTFT in your region, not marketing sheets. The right answer is usually a router that keeps all three configured and shifts traffic by latency budget.