Model selection is your single highest-leverage cost decision when building production AI agents. Choosing between Claude Sonnet 4 and GPT-4o isn't primarily a capability discussion — it's a cost architecture decision. The difference can be 2–5x on your monthly bill depending on how you use them.

This post breaks down the real cost math for common agent workloads, the trade-offs that actually matter, and how to pick the right model for each part of your stack — not just your whole stack.

Current Pricing (March 2026)

Prices per million tokens (input / output):

Claude Sonnet 4: $3.00 / $15.00
Claude Haiku 3.5: $0.80 / $4.00
GPT-4o: $2.50 / $10.00
GPT-4o-mini: $0.15 / $0.60
Gemini 1.5 Pro: $1.25 / $5.00
Gemini 1.5 Flash: $0.075 / $0.30

At these rates, a simple comparison looks like Claude Sonnet is more expensive than GPT-4o for output tokens. But raw pricing doesn't capture the full picture for agent workloads. What matters is cost per task completed — which depends on token efficiency, retry rates, and context window usage.

What "Cost Per Task" Actually Means for Agents

Unlike a simple chatbot, production agents have multi-step workloads. A single user request might involve:

A planning step (moderate context, structured output)
2–5 tool calls (shorter prompts, function results in context)
A synthesis step (full conversation history as input)
Potential retries on tool errors (re-runs = extra tokens)

A 10-step agent workflow can burn 5–20x more tokens than the final output token count suggests. When you're optimizing cost, you need to measure the full workflow cost — not just the last call.

Real Numbers: Common Agent Workflows

Workflow 1: Document Summarization Agent

Task: Summarize a 10-page PDF into a structured 500-word summary with key action items.

Typical input: ~8,000 tokens (document + system prompt)
Typical output: ~700 tokens
Runs: once per document, no retries assumed

Cost per run:

Claude Sonnet 4: (8K × $3.00 + 0.7K × $15.00) / 1M = $0.024 + $0.0105 = $0.035
GPT-4o: (8K × $2.50 + 0.7K × $10.00) / 1M = $0.020 + $0.007 = $0.027
Claude Haiku 3.5: (8K × $0.80 + 0.7K × $4.00) / 1M = $0.0064 + $0.0028 = $0.009
GPT-4o-mini: (8K × $0.15 + 0.7K × $0.60) / 1M = $0.0012 + $0.00042 = $0.0016

At 10,000 documents/month:

Claude Sonnet 4: $350/mo
GPT-4o: $270/mo
Claude Haiku 3.5: $90/mo
GPT-4o-mini: $16/mo

If document quality justifies Sonnet/4o, GPT-4o saves you ~22% over Claude Sonnet 4. But the mini-tier models save 83–95%. The real question is: does this task actually require frontier capability?

Workflow 2: Multi-Step Research Agent

Task: Answer a complex research question using 4 web search tool calls, synthesizing results into a structured report.

Planning step: 500 token input, 200 token output
4 tool calls: ~1,000 tokens each (input includes prior steps)
Synthesis: ~6,000 token input (accumulated context), 1,500 token output
Total: ~10,500 input, ~2,300 output

Cost per research task:

Claude Sonnet 4: $0.065
GPT-4o: $0.049
Haiku 3.5: $0.018

At 5,000 research tasks/month: Claude Sonnet 4 = $325/mo, GPT-4o = $245/mo. The gap grows as context accumulates across steps.

Workflow 3: Code Review / Generation Agent

Task: Review a 200-line function, identify issues, suggest fixes. This is where capability differences are most pronounced.

Both Claude Sonnet 4 and GPT-4o perform well here — but Claude has a documented edge on multi-file reasoning tasks and complex refactors. If your code review agent catches 20% more issues with Claude, you might need fewer retry cycles — which actually closes the cost gap when you measure end-to-end.

The Model-Matching Framework

The most cost-efficient production agent stacks don't use one model — they use a tier for each task type:

from tokenfence import TokenFence

tf = TokenFence(api_key="tf_live_xxx")

# Tier 1: Simple extraction, routing, classification
with tf.policy("classifier", budget="$0.001/task"):
    model = "gpt-4o-mini"  # $0.0016 per task

# Tier 2: Standard generation, summaries, drafts
with tf.policy("generator", budget="$0.05/task"):
    model = "gpt-4o"  # or claude-haiku-3-5

# Tier 3: Complex reasoning, code review, multi-step research
with tf.policy("reasoner", budget="$0.20/task"):
    model = "claude-sonnet-4"  # or gpt-4o for output-heavy tasks

This routing pattern — cheap model for classification, mid-tier for generation, frontier only for hard tasks — typically reduces blended cost by 50–70% versus using Sonnet or GPT-4o for everything.

When to Choose Claude Sonnet Over GPT-4o

Choose Claude Sonnet 4 when:

Long context is critical (200K context window vs GPT-4o's 128K)
Your task involves complex instruction-following with structured outputs
You need strong multi-document reasoning or cross-reference tasks
You're running with Extended Thinking for hard analytical problems
Latency is less important than accuracy (Claude tends to be more consistent)

Choose GPT-4o when:

Output token volume is high (GPT-4o output pricing is meaningfully lower)
Your existing stack is deeply OpenAI-native (function calling, Assistants, tool use)
You need vision/multimodal as part of the agent pipeline
You have tight latency requirements (GPT-4o tends to be faster)
Your task is output-heavy vs input-heavy

Controlling Cost Across Both Models

The biggest risk when running multi-model agent stacks is losing cost visibility — especially when some calls are Claude, some are GPT-4o, and some are mini-tier models. Without tagging, your invoice shows three provider bills with no attribution back to which agent workflow generated what cost.

TokenFence solves this with provider-agnostic policy enforcement:

import anthropic
import openai
from tokenfence import TokenFence

tf = TokenFence(api_key="tf_live_xxx")

# Claude call — tracked under same policy framework
with tf.policy("research-agent", budget="$500/month", tags={"provider": "anthropic"}):
    claude_client = anthropic.Anthropic()
    response = claude_client.messages.create(
        model="claude-sonnet-4-5",
        max_tokens=2048,
        messages=[{"role": "user", "content": research_prompt}]
    )

# GPT-4o call — same policy, different provider tag
with tf.policy("summarizer", budget="$200/month", tags={"provider": "openai"}):
    openai_client = openai.OpenAI()
    response = openai_client.chat.completions.create(
        model="gpt-4o",
        messages=[{"role": "user", "content": summary_prompt}]
    )

Now you get a unified cost view across both providers, with per-workflow budgets enforced regardless of which model is running.

The Bottom Line

Claude Sonnet 4 vs GPT-4o is not a "which is better" question — it's a routing question. For output-heavy tasks at scale, GPT-4o is typically cheaper. For long-context reasoning or complex instruction-following, Claude Sonnet 4 earns its cost premium. And for most classification, routing, and simple generation tasks, neither frontier model should be in the loop at all — that's what mini-tier models are for.

The teams spending the least on AI agents aren't the ones who found the cheapest single model. They're the ones who match task complexity to model tier — and have the visibility to know what each workflow is actually costing them.

pip install tokenfence   # Python
npm install tokenfence   # Node.js / TypeScript

Read the docs → · See pricing →

Pick the right model for the job. Control the cost of all of them.

Claude vs GPT-4o Cost Comparison for Production AI Agents (2026)