In this comparison
Overview
These are the flagships. Claude 4 Opus and GPT-o3 represent the absolute ceiling of what AI can do right now — and they take very different approaches to getting there.
GPT-o3 uses explicit chain-of-thought reasoning, spending extra time "thinking" before it responds. You can literally watch it work through problems step by step. Claude 4 Opus takes a more integrated approach, weaving reasoning throughout its response without a separate thinking phase (though it has extended thinking mode too).
Both models cost significantly more than their smaller siblings, but for hard problems — the kind that stump GPT-4o and Claude Sonnet — these are the ones you reach for. The question is which one to reach for first.
Claude 4 Opus vs GPT-o3: Side-by-Side
| Category | Claude 4 Opus | GPT-o3 |
|---|---|---|
| Developer | Anthropic | OpenAI |
| Context Window | 200K tokens | 200K tokens |
| API Input Price | $15.00/M tokens | $10.00/M tokens |
| API Output Price | $75.00/M tokens | $40.00/M tokens |
| GPQA Diamond | 74.1 | 79.7 |
| MATH-500 | 96.4 | 96.7 |
| HumanEval | 96.4 | 92.8 |
| SWE-bench Verified | 72.5 | 69.1 |
| Reasoning Style | Integrated + Extended Thinking | Chain-of-thought (visible) |
| Speed | Moderate | Slow (thinking time) |
Hard Reasoning & Math
GPT-o3 was built for this. Its chain-of-thought approach lets it tackle competition-level math and logic problems that other models choke on. On GPQA Diamond (graduate-level science questions), o3 scores nearly 80% — a remarkable achievement.
Claude 4 Opus is no slouch, hitting 96.4 on MATH-500 — virtually tied with o3's 96.7. On problems requiring creative insight rather than brute-force reasoning chains, Opus sometimes finds elegant solutions that o3 misses because it's too busy methodically working through steps.
Winner: GPT-o3 for pure logic and math. Claude 4 Opus for problems requiring creative reasoning.
Coding & Software Engineering
This is where Claude 4 Opus pulls ahead convincingly. On SWE-bench Verified — which tests real-world software engineering tasks like fixing bugs in actual open-source repos — Opus scores 72.5% to o3's 69.1%. On HumanEval, the gap is even wider: 96.4 vs 92.8.
In practice, Opus writes production-quality code that's well-structured and maintainable. It understands codebases holistically rather than just solving the immediate problem. o3 is strong at algorithmic challenges but sometimes produces code that works but isn't something you'd want to ship.
Winner: Claude 4 Opus, clearly.
Analysis & Research
Give both models a complex research question and you'll get substantively different responses. o3 tends to be thorough and systematic — it'll cover every angle. Opus tends to be more insightful — it might skip obvious points but surfaces non-obvious connections.
For tasks like "analyze this company's 10-K filing" or "evaluate this research paper," both produce excellent output. Opus's responses tend to read better and include more nuanced takes. o3's are more comprehensive but can feel like a textbook.
Winner: Claude 4 Opus for insight and readability. GPT-o3 for exhaustive coverage.
Speed & Cost
Neither of these is cheap or fast. But there are meaningful differences.
GPT-o3's thinking time varies wildly — simple questions might take 5 seconds, hard ones can take 30+ seconds. You're paying for that thinking time in tokens. Claude 4 Opus is more consistent in its response time and generally faster for non-trivial tasks.
On pricing, o3 is actually cheaper per token ($10/$40 vs $15/$75), but the thinking tokens add up. For complex tasks, the total cost often ends up similar.
Winner: GPT-o3 on sticker price. Roughly equal in practice.
Instruction Following & Reliability
Claude 4 Opus is remarkably good at following detailed, multi-constraint instructions. Give it a prompt with ten specific requirements and it'll hit all ten. o3 sometimes gets so caught up in its reasoning process that it loses sight of the original constraints.
For structured outputs (JSON, specific formats), Opus is more reliable. o3 occasionally returns reasoning traces mixed into its formatted output, which can break parsers.
Winner: Claude 4 Opus.
The Verdict
For developers and power users, Claude 4 Opus is the better all-around model. Its coding ability, instruction following, and writing quality give it an edge in most real-world tasks. It's the model we reach for when the work matters.
GPT-o3 earns its keep on the hardest reasoning problems — competition math, complex logic puzzles, and questions that require extended step-by-step thinking. If you're building something that needs to solve genuinely hard analytical problems, o3's explicit reasoning chain is valuable.
The honest recommendation: use Opus as your default flagship and switch to o3 when you hit something that specifically needs chain-of-thought reasoning on a hard problem. That gives you the best of both worlds.
Overall winner: Claude 4 Opus, by a meaningful margin for most use cases.
Frequently Asked Questions
Is Claude 4 Opus worth the high price?
For hard problems that smaller models can't handle, yes. Opus excels at complex coding tasks, nuanced analysis, and multi-step reasoning. For routine tasks, Claude Sonnet is 90% as good at a fraction of the cost. Use Opus when you need the best.
What makes GPT-o3 different from GPT-4o?
o3 uses explicit chain-of-thought reasoning — it 'thinks' before responding, spending extra tokens to work through problems step by step. This makes it dramatically better at math, logic, and science problems, but slower and more expensive than GPT-4o.
Which is better for coding?
Claude 4 Opus. It scores higher on both HumanEval and SWE-bench Verified, and in practice writes cleaner, more maintainable code. GPT-o3 is strong at algorithmic problems but Opus is the better software engineer.
Can I use these models in ChatGPT and Claude apps?
GPT-o3 is available in ChatGPT Plus and Pro subscriptions. Claude 4 Opus is available in Claude Pro. Both require paid subscriptions — the free tiers use smaller models.