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OpenAI o3Claude Sonnet 4.5

OpenAI o3 vs Claude Sonnet 4.5: The Developer's Cost Guide

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OpenAIAnthropicCost OptimizationModel Comparison
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o3 and Claude Sonnet 4.5 are both frontier models, but they're built for different jobs. This guide tells you exactly which one to use — and when switching saves you 70%+ on AI costs.

The Short Version

Use o3 when: multi-step reasoning, mathematics, competitive programming, or PhD-level science problems.

Use Claude Sonnet 4.5 when: writing, code review, analysis, long-document summarisation, customer support, or anything where cost efficiency matters.

For 80% of production use cases, Claude Sonnet 4.5 is the right choice. o3 is a specialist.


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Cost Comparison

ModelInput (per 1M tokens)Output (per 1M tokens)Context
OpenAI o3~$10~$40200K
Claude Sonnet 4.5$3$15200K
GPT-4o$5$15128K
Claude Haiku 3.5$0.80$4200K

Key takeaway: For most tasks, Claude Sonnet 4.5 is 60-70% cheaper than o3 and delivers comparable or better results on non-reasoning workloads.


When o3 Wins

o3 is a reasoning model — it uses extended internal "thinking" before answering. This makes it uniquely powerful for:

  • Mathematical proofs and competition math (AMC/AIME level)
  • Complex multi-step code generation with self-correction
  • Scientific reasoning requiring domain-specific knowledge chains
  • Legal or financial analysis with multi-clause dependencies
  • python
    # o3 is worth the premium here — multi-constraint optimisation
    response = client.chat.completions.create(
        model="o3",
        messages=[{"role": "user", "content": "Solve the travelling salesman problem for these 15 cities using dynamic programming, then explain your approach and complexity."}],
        reasoning_effort="high"  # o3-specific parameter
    )

    The reasoning_effort parameter lets you control the thinking budget — use "low" for 70% cost reduction when the task isn't worth full reasoning.


    When Claude Sonnet 4.5 Wins

    Claude Sonnet 4.5 is Anthropic's flagship production model — high intelligence at reasonable cost. It's the right choice for:

  • Content generation: blog posts, emails, product descriptions
  • Code review and refactoring: understanding existing codebases
  • Document analysis: summarising PDFs, contracts, earnings reports
  • Customer support: intent classification, response drafting
  • RAG pipelines: answering questions over retrieved context
  • python
    import anthropic
    
    client = anthropic.Anthropic()
    
    response = client.messages.create(
        model="claude-sonnet-4-5",
        max_tokens=1024,
        messages=[
            {"role": "user", "content": "Summarise this earnings report and flag any risk factors: ..."}
        ]
    )

    The Migration Decision

    Migrating from o3 to Claude Sonnet 4.5

    Most apps currently using o3 are over-paying for non-reasoning tasks. Ask yourself:

  • Does your task require chain-of-thought reasoning over multiple steps? → Keep o3
  • Is the task primarily generation, analysis, or classification? → Switch to Claude
  • A/B test pattern:

    python
    import random
    
    def get_model_for_task(task_type: str) -> str:
        REASONING_TASKS = {"math", "algorithm_design", "proof_verification"}
        if task_type in REASONING_TASKS:
            return "o3"
        # Route 10% to o3 as quality baseline, 90% to Sonnet
        return "o3" if random.random() < 0.1 else "claude-sonnet-4-5"

    Checklist Before Switching

  • [ ] Run your evaluation suite on 50 representative examples with both models
  • [ ] Check latency requirements — o3 with reasoning is slower (5–30 seconds)
  • [ ] Review API schema differences (Claude uses messages + system vs OpenAI's single messages array)
  • [ ] Update max_tokens — Claude's default is lower; set explicitly
  • [ ] Test tool/function calling — Anthropic's tool schema differs from OpenAI's

  • Bottom Line

    For a typical SaaS app spending $500/month on o3, switching to Claude Sonnet 4.5 for non-reasoning tasks saves $300–400/month with no quality degradation. Claim your free Anthropic credits and run the benchmark yourself.

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