GPT-4o vs Claude vs Gemini: cost per million tokens

The first line item in any LLM budget is the model itself, and GPT-4o vs Claude vs Gemini pricing drives most early architecture decisions. But token rates alone don’t determine spend—capabilities, latency, and ecosystem fit change the equation fast once you ship.

Capabilities

GPT-4o is OpenAI’s omni-model: strong general reasoning, native vision, and audio I/O through a single weight set. It handles structured extraction and tool calling with high reliability, which is why it remains the default for mixed-modality product surfaces.

Claude (specifically Claude 3.5 Sonnet as the current flagship) wins on long-form comprehension, code generation, and agentic workflows. Its strength is following nuanced instructions inside large prompts without drifting, making it the pick for document-heavy pipelines.

Gemini 1.5 Pro ships with a 1M-token context (2M in private preview) and native multimodal ingestion of text, image, audio, and video. It lags slightly on complex instruction adherence but excels when you need to ground a query in massive corpora without external chunking.

Price and cost model

Published list prices (Q4 2024) for the flagships:

• GPT-4o: $2.50 / 1M input tokens, $10 / 1M output tokens.
• Claude 3.5 Sonnet: $3 / 1M input, $15 / 1M output.
• Gemini 1.5 Pro: $3.50 / 1M input, $10.50 / 1M output for prompts over 128k; $1.25 / $5 below that tier.

The GPT-4o vs Claude vs Gemini pricing gap narrows when you factor prompt caching. Anthropic and Google both offer cache write/read discounts; OpenAI added prompt caching at $1.25 / 1M cached input for GPT-4o. If your traffic repeats system prompts, effective cost can drop 50–90%.

# Cost estimate for a 10k-token repeated system prompt + 2k new tokens, 100k calls/mo
models = {
    "gpt-4o": {"in": 2.50, "out": 10.0, "cache": 1.25},
    "claude-3-5-sonnet": {"in": 3.0, "out": 15.0, "cache": 0.30},
    "gemini-1.5-pro": {"in": 3.50, "out": 10.50, "cache": 0.875},
}
for name, p in models.items():
    cached_in = 10_000 * p["cache"] / 1e6
    new_in = 2_000 * p["in"] / 1e6
    out = 1_000 * p["out"] / 1e6  # assume 1k output
    print(name, (cached_in + new_in + out) * 100_000)

Latency and throughput

Raw benchmark numbers shift weekly, so treat these as operational ranges from production traffic:

• GPT-4o: lowest time-to-first-token (TTFT) among the three at ~300–500ms for small prompts; sustained throughput ~100–150 tok/s per stream.
• Claude 3.5 Sonnet: TTFT ~600–900ms; throughput similar but more sensitive to prompt length past 32k tokens.
• Gemini 1.5 Pro: TTFT highly variable by region; can be 400ms on Vertex but degrades when context exceeds 500k tokens.

If you need interactive UX, GPT-4o leads. For batch extraction over night, all three are acceptable.

Ergonomics

OpenAI’s API is the de facto standard. The Anthropic SDK differs (separate messages shape, system as top-level), but a gateway such as n4n.ai collapses these three into one OpenAI-compatible endpoint covering 240+ models, so the same client code swaps models by string.

from openai import OpenAI
client = OpenAI(base_url="https://api.n4n.ai/v1", api_key="sk-...")

for model in ["gpt-4o", "claude-3-5-sonnet", "gemini-1.5-pro"]:
    r = client.chat.completions.create(
        model=model,
        messages=[{"role": "user", "content": "Summarize: ..."}],
        max_tokens=512,
    )
    print(model, r.usage.total_tokens)

Gemini exposes cache-control via systemInstruction and cachedContent objects in Google’s SDK; through an OpenAI-compatible proxy, those map to extra_body routing hints. The gateway honors client routing directives and forwards provider cache-control hints, which matters when you want Claude’s prompt caching without leaving the OpenAI SDK.

Ecosystem

• GPT-4o: largest third-party tooling, fine-tuning available, Azure OpenAI mirroring.
• Claude: Anthropic-first, but available on AWS Bedrock and GCP Vertex; no self-serve fine-tune on 3.5 yet.
• Gemini: native to Google Cloud, tight with BigQuery and Vertex AI; open-weight variants (Gemma) for local.

If you’re already on GCP, Gemini’s egress zero-cost is a real line item. On AWS, Bedrock’s Claude integration avoids cross-cloud fees.

Limits

Routing and fallback

In production you rarely lock to one model. Automatic fallback when a provider is rate-limited or degraded keeps p99 latency bounded. With per-token usage metering you can attribute cost to features without building your own accounting layer.

{
  "route": {
    "primary": "gpt-4o",
    "fallback": ["claude-3-5-sonnet", "gemini-1.5-pro"],
    "on_error": [429, 503]
  },
  "cache_control": {"type": "ephemeral", "ttl": 3600}
}

Which to choose

High-volume chat with low latency: GPT-4o. Best TTFT and cheapest cached input among the three at scale.

Long-document analysis or agentic coding: Claude 3.5 Sonnet. The 200k window and instruction fidelity offset the higher output price.

Massive context or Google-cloud-native: Gemini 1.5 Pro. The 1M window removes your chunking layer; sub-128k pricing undercuts both rivals.

Cost-sensitive MVP: Start on Gemini 1.5 Pro (sub-128k tier) or GPT-4o mini if quality permits, then promote to flagship only for low-frequency complex calls.

Resilient production: Use all three behind a routing layer with fallback. Keep GPT-4o primary, Claude for long prompts, Gemini for overflow. Meter per token and revisit the GPT-4o vs Claude vs Gemini pricing quarterly—the list prices move every few months.