Cost per request: GPT-4o vs Claude vs Llama pricing

The real cost per request GPT-4o Claude Llama isn’t just the sticker price per token—it’s a function of context size, output length, self-hosting overhead, and failure rates. Engineers optimizing a production pipeline need a head-to-head that goes beyond marketing sheets. This breakdown covers capabilities, pricing structure, latency, ergonomics, ecosystem, and hard limits so you can map model choice to actual spend.

Capabilities

Where each model earns its keep

GPT-4o is OpenAI’s flagship omni-model: text, vision, and audio in one endpoint, strong reasoning, low hallucination on structured extraction. Claude (specifically Claude 3.5 Sonnet, the default “Claude” in most API routes) excels at long-document synthesis, agentic tool use, and code generation inside a 200K context window. Llama (Llama 3 70B/8B) is Meta’s open-weight family; you trade some frontier quality for full weight access, custom fine-tunes, and no per-token toll if you own the hardware.

For a RAG pipeline over 100-page PDFs, Claude’s context and citation behavior often beat GPT-4o on recall. For multimodal intake (receipt photos + text), GPT-4o is the only one of the three with native vision in the same call. Llama needs a separate vision adapter or a smaller dedicated model such as Llama 3.2-Vision.

Reasoning and code

On HumanEval-style sweeps, GPT-4o and Claude 3.5 sit within a few points of each other; Llama 70B trails by mid-single digits but closes the gap after quantization-aware fine-tuning. None of these are static—point releases shift the curve every quarter.

Price / Cost Model

OpenAI and Anthropic sell tokens. Meta sells weights.

GPT-4o: $5.00 per 1M input tokens, $15.00 per 1M output tokens. Claude 3.5 Sonnet: $3.00 per 1M input, $15.00 per 1M output. Llama 3 70B is free to download; your cost is GPU time. A single A100-80GB at ~$1.50/hr running vLLM can push ~2,000 output tokens/sec at batch 16, which puts a 500-token response at roughly $0.0002 in compute—if you have steady utilization.

The cost per request GPT-4o Claude Llama diverges hard at scale. At 10M output tokens/month:

• GPT-4o: $150
• Claude: $150
• Llama self-hosted: ~$30–50 in GPU, plus ops time.

But self-hosting hides a fixed cost: you pay whether you serve 1 request or 1M. Below ~5M tokens/month, API is usually cheaper unless privacy mandates local inference.

Here’s a minimal Python snippet to compute blended cost for a request mix:

def cost_per_request(model, in_tok, out_tok):
    pricing = {
        "gpt-4o": (5/1e6, 15/1e6),
        "claude-3.5": (3/1e6, 15/1e6),
    }
    if model in pricing:
        i, o = pricing[model]
        return i*in_tok + o*out_tok
    # llama: assume $0.0002 per 500 out tok flat for example
    if model == "llama-70b":
        return 0.0002 * (out_tok/500) + 0.00005 * (in_tok/500)
    raise ValueError(model)

print(cost_per_request("gpt-4o", 2000, 500))  # ~$0.0175
print(cost_per_request("claude-3.5", 2000, 500))  # ~$0.0115

A gateway such as n4n.ai exposes a single OpenAI-compatible endpoint for 240+ models with per-token metering and automatic fallback when a provider is degraded, which simplifies tracking the cost per request GPT-4o Claude Llama side by side.

Latency / Throughput

GPT-4o and Claude are both rate-limited by provider infrastructure; p50 time-to-first-token on a 1K prompt is typically 300–600ms for both, with Claude slightly slower on long contexts. Llama latency is entirely your stack: vLLM on A100 gives ~30ms TTFT for 8B, ~80ms for 70B at batch 1. Throughput scales with batching; API providers hide that from you but charge for it indirectly via rate tiers.

If you need 100 req/s sustained, API quota negotiations become the bottleneck. Self-hosted Llama lets you scale horizontally with GPUs you provision. A small benchmark from a single H100 running TensorRT-LLM shows 70B hitting 120 tok/s/batch=1 and 1,800 tok/s at batch=32—numbers no shared API will quote you.

Ergonomics

All three speak JSON. GPT-4o and Claude support function calling natively; Llama needs a scaffolding layer like vLLM’s chat endpoint with manual tool parsing or llama-cpp’s grammar constraints.

Example OpenAI-compatible call that works for GPT-4o and Claude via a proxy:

{
  "model": "gpt-4o",
  "messages": [{"role": "user", "content": "Summarize: ..."}],
  "max_tokens": 500,
  "temperature": 0.2
}

Swap "model" to "claude-3.5-sonnet" on a unified gateway and the rest is identical. Llama requires the same shape but you must host the endpoint and manage weights.

Claude’s prompt caching (cache_control headers) can cut repeat-input cost by up to 90% on long system prompts. GPT-4o has automatic prompt caching on certain endpoints. Llama gives you cache for free because you control the KV cache across requests.

Ecosystem

GPT-4o: deepest third-party tooling, Assistants API, Realtime API, broad middleware support. Claude: strong Anthropic SDK, good for agent loops, open weights not available. Llama: HuggingFace dominance, unlimited fine-tunes, quantization (GGUF), and a sprawling self-host community with Ollama, LM Studio, and TGI.

If you need to fine-tune on proprietary data without sending it to a lab, Llama is the only option of the three. GPT-4o and Claude offer hosted fine-tunes but your data crosses their boundary. For quick experimentation, the OpenAI-compatible surface area means most LangChain or Haystack code runs unchanged across all three if you abstract the base URL.

Limits

That table is the only comparison matrix you need for architecture talks. Note the Llama context row: native training used 8K, but inference-time scaling works reliably to 128K with degraded recall—know your accuracy bar before trusting it.

Which to Choose

Prototype or low-volume SaaS (< 1M tokens/mo): Use GPT-4o or Claude via API. Start with Claude 3.5 for cost skew on input-heavy flows; switch to GPT-4o when you need vision. The cost per request GPT-4o Claude Llama at this scale is cents, not dollars, so optimize for dev time, not token math.

High-volume, latency-sensitive (> 50M tokens/mo): Self-host Llama 70B if you have GPU ops competence. The token savings eclipse API spend, but you own pager duty. If ops bandwidth is thin, negotiate enterprise API rates with OpenAI/Anthropic and use a fallback gateway to absorb spikes.

Privacy-bound workloads (healthcare, finance): Llama on VPC. No provider sees payloads. Use GPT-4o/Claude only with zero-retention contracts and regional isolation.

Multimodal product: GPT-4o is the only turnkey choice. Don’t bolt vision onto Llama unless you have research time and a tolerance for pipeline fragility.

Long-context RAG: Claude 3.5 Sonnet with prompt caching. Its 200K window and citation reliability beat GPT-4o’s 128K for dense docs, and the input pricing is lower.

Edge or offline: Llama 8B quantized to 4-bit runs on a laptop CPU at 5–10 tok/s. Neither API model can leave the datacenter.

The cost per request GPT-4o Claude Llama is a moving target as providers reprice and new weights drop. Track per-token metering, set fallback routes, and re-evaluate quarterly against your real traffic shape.