I Gave OpenAI o3 and Claude Opus 4.7 the Same $100 Budget. Here's What I Got.

Not a benchmark. Not a vibe check. An actual $100 budget split equally between o3 and Claude Opus 4.7, running the same production workload. Here's the full accounting.

The Workload

Same tasks, same prompts, same 7-day window:

• Code review on 40 pull requests (diff → structured review comment)
• 20 multi-step research tasks (question → 3-level deep answer with sources cited)
• 100 classification tasks (email → intent + priority + suggested action)
• 10 long-form technical articles (topic → 2,000-word draft)
• 5 complex debugging sessions (bug report + codebase → root cause + fix)

Methodology

$50 to each model. I used the respective Python SDKs with identical prompts. I tracked token counts, cost per task, output quality (manually rated), and errors/failures.

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Results: Volume

Task	o3 count	Opus 4.7 count
PR reviews	18	22
Research tasks	8	12
Classifications	47	53
Articles	4	6
Debug sessions	2	3
Total tasks	79	96

Opus 4.7 completed 21% more work on the same $50. Primarily because Opus 4.7 is cheaper per output token at equivalent quality tiers, and Anthropic's prompt caching (5-min TTL) cut system prompt costs on repeated task patterns.

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Results: Quality

This is where it gets more interesting.

PR Reviews — Opus 4.7 caught more issues per review (avg 3.2 vs 2.7 flagged items). o3 was more likely to explain why something was a problem. For solo devs: Opus. For team use where you need explainability: o3.

Research tasks — o3 consistently went deeper on reasoning chains. On ambiguous questions, it would surface competing interpretations before answering. Opus 4.7 was more direct but occasionally missed edge cases that o3 caught. Edge: o3 for research.

Email classification — Statistically identical. Both hit ~94% agreement with my manual labels. Not worth the cost premium for this use case.

Articles — I preferred Opus 4.7's voice. Less hedge-y, more direct. o3 articles needed more editing to remove qualifiers. Personal preference — your mileage may vary.

Debugging — o3 was better at reasoning through multi-file issues without hallucinating. Opus 4.7 was faster and cheaper, but on the hardest debugging session (a race condition with async state), o3 produced the correct fix on the first try while Opus 4.7 required 2 follow-up turns.

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Cost Breakdown

Claude Opus 4.7 ($50 budget):

• Input: ~2.1M tokens ($31.50)
• Output: ~620K tokens ($18.60)
• Cache hits: ~180K tokens saved ($2.70 savings, absorbed into budget)
• Tasks completed: 96
• Cost per task: $0.52

OpenAI o3 ($50 budget):

• Input: ~1.8M tokens (~$27)
• Output: ~460K tokens (~$23)
• No caching equivalent for my workload
• Tasks completed: 79
• Cost per task: $0.63

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Prompt Caching: The Hidden Advantage

Anthropics' prompt caching was the biggest practical differentiator. My classification pipeline reuses a 2,000-token system prompt. With caching:

response = client.messages.create(
    model="claude-opus-4-7",
    system=[
        {
            "type": "text",
            "text": CLASSIFICATION_SYSTEM_PROMPT,
            "cache_control": {"type": "ephemeral"}
        }
    ],
    messages=[{"role": "user", "content": email_body}]
)

On 53 classification tasks, this saved ~106K input tokens. At Opus 4.7 prices, that's $1.59 back. Across a production system running 500+ classifications/day, that's real money.

OpenAI doesn't have a direct equivalent for this pattern on o3 today.

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When to Use Which

Use Claude Opus 4.7 for:

• High-volume, repeated tasks (classifications, reviews, summaries)
• Workloads where prompt caching compounds savings
• Code generation and article writing
• Budget-sensitive production pipelines

Use o3 for:

• Complex multi-step reasoning where you need the chain of thought
• Ambiguous research where missing an edge case is costly
• Debugging novel, multi-file issues where first-try accuracy matters
• Cases where explainability > throughput

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The Honest Summary

On a $100 budget, Opus 4.7 gives you more tasks completed at similar or better quality for most workloads. o3 wins on hard reasoning and debugging where you're willing to pay for accuracy on the first try.

Neither model "wins." You should probably be using both: Opus 4.7 as your default workhorse, o3 as the specialist you call in when the problem is genuinely hard.

The most expensive mistake is defaulting to the more expensive model out of habit. Default to Opus 4.7. Escalate to o3 when the task warrants it.

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All tools → whoffagents.com