The decision between models often comes down to how they feel in a live stream, not benchmark scores. When you put GPT-4o vs Claude streaming latency under real load, the differences show up in time-to-first-token (TTFT), inter-token jitter, and how the API behaves when a provider is degraded. This is a head-to-head across the dimensions that actually move the needle in production.
How we measured
We eliminated client-side variables by sending identical prompts to both models through a single OpenAI-compatible endpoint. Using a gateway like n4n.ai, which fronts 240+ models and honors provider cache-control hints, means the only moving part is the backend model itself. The same stream=True call shape was used for both, from the same compute zone, over a persistent connection pool.
from openai import OpenAI
import time
client = OpenAI(base_url="https://api.your-gateway.com/v1", api_key="KEY")
def measure_ttft(model: str, prompt: str) -> float:
start = time.perf_counter()
for chunk in client.chat.completions.create(
model=model,
messages=[{"role": "user", "content": prompt}],
stream=True,
):
if chunk.choices[0].delta.content:
return (time.perf_counter() - start) * 1000.0
return float("nan")We ran prompts from 200 to 8K input tokens, mirroring production traffic: short chat turns, RAG retrieval contexts, and multi-file code blocks. No synthetic load was injected; we observed natural provider load across a week.
What we tracked
- TTFT: wall clock from request send to first content delta.
- Inter-token interval (ITI) variance: standard deviation of gaps between deltas after the first.
- Stream completion rate: fraction of calls that closed cleanly vs 429/timeout.
- Tail latency: p95 and p99 TTFT, not just averages.
Capabilities
GPT-4o is a natively multimodal model: text, vision, and audio in a single weight set. That matters for streaming because audio tokens and text tokens share the same decode path, so you can get low-latency voice without a separate ASR/TTS hop. It handles 128K context windows.
Claude Sonnet 4.5 keeps the family strength in long-context reasoning and code. It accepts up to 200K tokens and holds coherence on multi-file refactors better than most. Input is text and image; no native audio. For agents that ingest entire repos, that extra context headroom is decisive.
Price and cost model
OpenAI lists GPT-4o at $5 per million input tokens and $15 per million output tokens. Claude Sonnet 4.5 sits in the same bracket as its predecessor: roughly $3–5 per MTok input, $15 per MTok output depending on volume and cache usage.
Caching changes the equation. Both providers support prompt caching, but the hints must be forwarded. A gateway that forwards cache_control blocks lets you pin long system prompts and cut repeat-input cost by 50–90% on Claude, and similar with OpenAI’s cached tokens. If your stream repeats a 4K system prompt across turns, that discount dwarfs the per-token model difference.
Latency and throughput
This is the core of GPT-4o vs Claude streaming latency. GPT-4o is tuned for responsiveness: its TTFT is consistently lower, especially on short prompts. You feel it in chat UIs where the cursor starts blinking almost immediately. On 200-token inputs we see it routinely beat Claude to first character by a meaningful margin.
Claude Sonnet 4.5 trades a little TTFT for steadier mid-stream throughput on long generations. On 2K+ output spans, its tokens-per-second curve is flat; GPT-4o can burst early then throttle under provider load. Neither model streams at a fixed rate. Both exhibit jitter from speculative decoding and load shedding.
Tail behavior
The average lies. Under provider congestion, GPT-4o’s p99 TTFT can stretch because it prioritizes burst capacity; Claude’s p99 is less spiky but starts higher. If your SLA is “first token under 800ms at p95,” Claude is easier to guarantee. If it’s “feel instant in the common case,” GPT-4o wins.
Ergonomics
Both are reachable via the OpenAI Chat Completions schema if you use a compatible gateway. Native Anthropic API uses a different message shape and SSE event format, but the delta-streaming concept is identical. Tool calling streams differently: GPT-4o emits tool_calls deltas inline; Claude streams partial JSON inside a single block.
{
"model": "gpt-4o",
"stream": true,
"tools": [{"type": "function", "function": {"name": "search"}}]
}If you parse streams generically, budget for both shapes. A thin normalizer that emits a unified delta.type event saves weeks of frontend pain.
Ecosystem
GPT-4o rides the OpenAI ecosystem: Assistants, structured outputs, realtime API. Claude Sonnet 4.5 plugs into Anthropic’s MCP and a growing set of agent frameworks that assume block-based streaming. If you are already on a unified gateway, the ecosystem gap shrinks. You call one endpoint, pass model= and optional routing headers, and the gateway handles fallback when a provider is rate-limited.
Limits
GPT-4o caps at 128K context for most deployments. Claude Sonnet 4.5 holds 200K. Rate limits are account-tier based; both return 429 with retry-after under burst. Streaming-specific limit: OpenAI closes the stream on timeout if no token in ~10s; Claude keeps longer idle but will truncate on max tokens. Handle both by implementing client-side resume with a cursor offset.
Head-to-head comparison
| Dimension | GPT-4o | Claude Sonnet 4.5 |
|---|---|---|
| Capabilities | Native text/vision/audio, 128K ctx | Text/vision, 200K ctx, strong code |
| Cost model | $5/$15 per MTok, cached tokens | ~$3–5/$15 per MTok, cache_control |
| TTFT | Lower, consistent on short prompts | Slightly higher, stable under load |
| Throughput | Bursty, may throttle mid-stream | Steady on long outputs |
| Ergonomics | OpenAI schema, inline tool deltas | Anthropic native or compatible gateway |
| Ecosystem | Assistants, realtime API | MCP, agent frameworks |
| Limits | 128K ctx, 10s stream idle timeout | 200K ctx, longer idle tolerance |
Which to choose
Real-time chat or voice: Pick GPT-4o. The lower time-to-first-token makes the difference between a snappy copilot and a sluggish one. This is where GPT-4o vs Claude streaming latency is most decisive.
Long-document analysis: Claude Sonnet 4.5. The 200K window and flat throughput keep multi-page extraction from stalling.
Cost-sensitive batch with caching: Either, but lean Claude if your prompts are long and reusable; its cache discount is aggressive.
Fallback-critical production: Use a gateway that routes both and honors client directives. You get GPT-4o’s speed with Claude as the overflow target when OpenAI is degraded.
Multimodal audio: Only GPT-4o today. Don’t bolt on separate ASR/TTS if you can avoid it.
Streaming latency is not a single number; it’s a distribution. Measure it on your own traffic, keep the API shape identical, and choose based on the tail, not the average.