GPT-4o vs Claude 3.5 Sonnet: tokens per second compared

When you’re picking a default model for a production LLM feature, GPT-4o vs Claude 3.5 Sonnet speed is usually the second thing debated after accuracy. Both ship from major labs, both are multimodal, and both sit in the same price band, but their throughput profiles and latency tails behave differently under real load.

Capabilities

Modality and reasoning

GPT-4o is OpenAI’s omni-model: text, vision, and an audio path that requires separate transcription/synthesis endpoints. Claude 3.5 Sonnet is Anthropic’s mid-tier model in the 3.5 family, strong on coding and long-context reasoning, with vision but no audio. For pure text agentic workflows, both clear the bar; Sonnet edges out on structured code generation, GPT-4o holds a slight lead on open-ended creative mixing of text and image inputs.

Tool use and structured output

Both support JSON mode or tool schemas. OpenAI exposes response_format={"type":"json_object"} and parallel tool calls. Anthropic uses tool_use blocks in the message stream. In practice, Claude 3.5 Sonnet’s tool parsing is stricter; GPT-4o is more forgiving with malformed schemas. Neither replaces downstream validation—parse everything.

Price and cost model

Token pricing

Public list prices as of late 2024: GPT-4o charges $5 per 1M input tokens and $15 per 1M output tokens. Claude 3.5 Sonnet lists $3 per 1M input and $15 per 1M output. On input-heavy RAG pipelines, Sonnet is roughly 40% cheaper per query. Output cost is identical, so generation-heavy tasks cost the same across both.

Hidden cost: latency and retries

Speed translates directly to infrastructure cost. A slow model holds a worker thread or websocket open longer. If your service pays per concurrent connection or per GB-hour of memory, the GPT-4o vs Claude 3.5 Sonnet speed difference affects your compute bill. Retries on timeout multiply token spend and skew your metering.

Latency and throughput

This is the core of the comparison. Headline vendor numbers are marketing; you need tail latency under your own traffic shape.

First token latency

Time-to-first-token (TTFT) is dominated by prompt processing. Claude 3.5 Sonnet tends to return first token faster on prompts under 2k tokens. GPT-4o catches up on longer contexts because of its optimized KV-cache handling and aggressive prefix caching.

Tokens per second under load

Sustained generation speed (tokens/sec) lands in a similar band for single-stream requests. Under batch or high concurrency, provider scheduling matters more than model architecture. We’ve observed both degrade to 30–50% of baseline throughput when a region is saturated. The GPT-4o vs Claude 3.5 Sonnet speed gap narrows to noise when you control for prompt size and concurrency.

Measuring it yourself

Don’t trust vendor dashboards. Run a streaming loop against each:

import time
from openai import OpenAI

client = OpenAI()
start = time.time()
first = None
n = 0
for chunk in client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role":"user","content":"Write a 300-word RFC for a cache."}],
    stream=True,
):
    if chunk.choices[0].delta.content:
        if first is None:
            first = time.time()
        n += 1
ttft = first - start
gen = time.time() - first
print(f"TTFT {ttft:.2f}s, {n/gen:.1f} tok/s (approx)")

For Sonnet, the Anthropic SDK is just as terse:

from anthropic import Anthropic
client = Anthropic()
start = time.time()
first = None
chars = 0
with client.messages.stream(
    model="claude-3-5-sonnet-20240620",
    max_tokens=400,
    messages=[{"role":"user","content":"Write a 300-word RFC for a cache."}],
) as stream:
    for text in stream.text_stream:
        if first is None:
            first = time.time()
        chars += len(text)
ttft = first - start
gen = time.time() - first
print(f"TTFT {ttft:.2f}s, {chars/4/gen:.1f} tok/s (approx)")

Count tokens with the provider’s tokenizer for accuracy; character/4 is a rough proxy.

Ergonomics

API shape

OpenAI’s SDK is ubiquitous; every gateway, langchain fork, and curl snippet assumes it. Anthropic’s SDK forces you to pass system as a top-level param and handle tool_use/tool_result blocks explicitly. If you already run OpenAI-compatible middleware, Claude requires an adaptation layer unless you sit behind a translation gateway.

Streaming and cancellation

Both support SSE streaming. GPT-4o’s stream includes finish_reason promptly; Claude’s stream sends a final message_stop. Cancelling mid-stream works on both, but Anthropic’s API returns a 200 with partial body whereas OpenAI kills the connection. Your client must handle both shapes or you’ll leak connections.

Ecosystem

Model availability and routing

OpenAI locks GPT-4o to its platform. Anthropic serves Claude 3.5 Sonnet via its API, AWS Bedrock, and Google Vertex. If you need multi-cloud redundancy, Sonnet wins. A gateway like n4n.ai exposes both behind one OpenAI-compatible endpoint and automatically falls back when a provider is rate-limited, which reduces the ergonomic gap to zero and lets you A/B latency without client rewrites.

Tooling and observability

LangSmith, Helicone, and most eval harnesses treat OpenAI as the default. Claude support is first-class in newer tools but expect occasional field mapping. For self-hosted tracing, both emit usage metadata per request; forward it to your meter.

Limits

Context windows

GPT-4o: 128k context. Claude 3.5 Sonnet: 200k context. If your app ingests entire codebases or long legal docs, Sonnet’s headroom matters and avoids chunking artifacts.

Rate limits and degradation

OpenAI tier-3 accounts get ~10k RPM on GPT-4o; Anthropic’s standard tier allows 1k RPM on Sonnet by default, raisable. Both throttle abruptly under abuse patterns. Provider degradation during peak hours is real; build fallback or use a routing directive:

{
  "model": "claude-3-5-sonnet",
  "route": {"prefer": "anthropic", "fallback": ["openai"]},
  "cache_control": {"type": "ephemeral"}
}

Head-to-head summary

Which to choose

Real-time chat with short context

Pick Claude 3.5 Sonnet. Its lower TTFT on short prompts makes the UI feel snappier. The $3 input price helps if you re-send system prompts often.

Long-document analysis

Sonnet’s 200k context and strong retrieval grounding win. GPT-4o forces chunking earlier and may lose cross-chunk references.

High-concurrency batch jobs

Either works. Measure against your own load. If you already use OpenAI infra, GPT-4o’s tier limits are easier to raise. Use a fallback gateway to mask provider outages and keep p99 stable.

Multimodal voice or vision

GPT-4o is the only one with a native audio path. For vision-only, both are competent; Sonnet’s vision is slightly more precise on diagrams and screenshots.

Cost-sensitive RAG

Sonnet’s cheaper inputs make it the default for embedding-heavy pipelines where you pass large retrieved contexts each call.

Bottom line: the GPT-4o vs Claude 3.5 Sonnet speed gap is smaller than the ergonomic and context differences. Benchmark with your own prompts, then route by use case rather than chasing a single tokens-per-second number.