I spent three weeks running both models through the same tasks, back to back, in the same sessions. Not cherry-picked prompts designed to make one look good. Real work: debugging a production codebase, writing a chapter of a thriller novel, solving a competition math problem, analyzing a 90-page contract.
The short version: these are two excellent models with genuinely different strengths. Claude Opus 4 edges out o3 on writing, coding, and instruction-following. o3 edges out Opus 4 on pure math and structured reasoning. And o3 is dramatically cheaper on the API, which matters a lot if you are building on top of it.
Here is exactly what I found.
At a Glance: Claude Opus 4 vs ChatGPT o3
| Category | Claude Opus 4 | ChatGPT o3 |
|---|---|---|
| Release date | May 22, 2025 | April 16, 2025 |
| Developer | Anthropic | OpenAI |
| Context window | 200K tokens | 200K tokens |
| Max output | 64K tokens | 100K tokens |
| API pricing (input/output per 1M tokens) | $15 / $75 | $2 / $8 |
| Consumer plan | $20/month (Pro) | $20/month (Plus) |
| SWE-bench Verified | 72.5% | 71.7% |
| AIME 2025 | ~33.9% | 88.9% |
| GPQA Diamond | 79.6% | 87.7% |
| ARC-AGI | Not reported | 75.7% (low compute) |
| Hallucination rate (PersonQA) | Lower (not published) | 33% (double o1’s rate) |
| Image generation | No | Yes (with tools) |
| Computer use | Yes (API beta) | No |
| Best for | Coding, writing, agents | Math, STEM reasoning, cost |
A Note on Architecture
Claude Opus 4 is what Anthropic calls a hybrid reasoning model. It can toggle between fast, instinctive responses and longer, deliberate chains of thought depending on what the task demands. Anthropic positioned it at launch as the world’s best coding model, with particular emphasis on sustained performance during long agentic workflows. It runs on Claude Code, supports computer use via API, and can work autonomously on a task for hours without degrading.
ChatGPT o3 is OpenAI’s dedicated reasoning model, the direct successor to o1. Where GPT-4o is built for breadth, o3 is built for depth. It was the first reasoning model OpenAI released with full autonomous tool use, meaning o3 can call web search, run Python, and generate images within a single session. The model showed a breakthrough performance jump on ARC-AGI, a benchmark many considered a proxy for general intelligence, scoring 75.7% in low-compute mode where the previous record from o1 sat at 32%.
Both models support 200K token context windows. Both are available through consumer subscription plans at $20 per month. Both accept tool calls and structured output. The meaningful differences show up when you push them on specific task types.
The 10 Tasks
Task 1: Complex Code Debugging
The prompt: I pulled a real, open GitHub issue from a mid-sized Python web application: a subtle race condition in an async task queue that only surfaced under concurrent load. I gave both models the full repo context (stripped of any identifying info), the error traceback, and a description of the failure conditions.
What happened: Opus 4 identified the root cause on the first response, explained the concurrency model clearly, and produced a patch that included a comment explaining why the fix works. The fix was correct.
o3 also identified the issue, but took longer to arrive at the root cause, spent more tokens reasoning through it, and produced a patch that worked but was harder to read. It also included two links to Python asyncio documentation that, when I tried to open them, led to pages that had moved or no longer existed. That hallucinated-link problem showed up repeatedly in my testing, which tracks with what Stanford professor Kian Katanforoosh reported to TechCrunch after his team tested o3 in production workflows.
Winner: Claude Opus 4. Better code quality, cleaner explanation, no phantom links.
Task 2: Creative Long-Form Writing
The prompt: Write the opening chapter of a psychological thriller. Specific constraints: unreliable narrator, present tense, set in a rain-soaked Helsinki in January, the protagonist has just realized she is being watched. Target: 1,200 words, sardonic tone, no flashbacks in the opening chapter.
What happened: This is where the models diverge most sharply. Opus 4 produced prose that felt written. The sentences had rhythm. The unreliable narrator voice was consistent, the tone hit sardonic without becoming mean-spirited, and it stayed in present tense throughout. Every one of my six constraints was honored.
o3 produced competent writing but it felt generated. The sentences were grammatically correct and logically structured, but the voice was generic. It gave me a Helsinki that could have been any gray European city. It also slipped into past tense twice in the second half of the chapter.
This matches what independent testers at MindStudio found when they ran human-scored evaluations across creative writing tasks: Opus 4.6 (a later revision of the Opus 4 family) scored 8.6 out of 10 on prose quality versus 7.4 for GPT-5.4. The Opus 4 advantage in writing quality is consistent across human raters and across prompt types.
Winner: Claude Opus 4. Not close.
Task 3: Hard Math and Competition Problems
The prompt: A graduate-level problem from the 2025 American Invitational Mathematics Examination, plus two Putnam-style proofs requiring formal reasoning about convergence.
What happened: o3 is a different class of model on this type of task. It scored 88.9% on the AIME 2025 in OpenAI’s reported evaluations, which is close to the performance of professional mathematicians on that particular exam. When I ran my own problems, the results were consistent with that: careful step-by-step derivations, correct intermediate steps, clean conclusions.
Opus 4 was not bad. It arrived at correct answers on the AIME-level problem and one of the two Putnam problems. But it made one error in a proof step that o3 got right, and the reasoning chain was harder to follow. On the AIME benchmark at launch, Opus 4 scored around 33.9%, a significant gap.
For pure mathematics, o3’s extended reasoning chain is a genuine structural advantage.
Winner: ChatGPT o3. The gap here is real and consistent.
Task 4: Long Document Analysis
The prompt: A 92-page vendor contract with embedded schedules, amendments, and cross-references. Task: identify the top five commercial risk areas for a SaaS company purchasing the software, with specific clause citations.
What happened: Both models processed the full document without truncation. Opus 4 produced a more thorough analysis, caught a subtler risk buried in Schedule C about data portability on contract termination that o3 missed entirely, and formatted its output in a way that was easier to act on. Anthropic built Opus 4 specifically for this kind of sustained, document-heavy reasoning, and it shows.
o3’s output was good but more surface-level. It identified the obvious risks (limitation of liability cap, SLA carve-outs) but did not follow the cross-references as carefully. It also cited clause numbers that, when I went back to verify, were off by one or two subsections.
Winner: Claude Opus 4. The cross-reference accuracy alone makes it the safer choice for legal or compliance workflows.
Task 5: Research Synthesis
The prompt: Synthesize the current state of evidence on whether large language models show signs of systematic self-deception during extended reasoning chains. Pull from recent literature and give me a research-grade summary with a recommendation for future work.
What happened: This is the task where I called it a genuine draw. o3’s tool use capability gave it a structural advantage: it pulled live papers, cited actual arXiv IDs, and synthesized more recent sources than Opus 4 could access by default. Its output was better sourced.
Opus 4 produced a more conceptually sophisticated synthesis. It identified a tension in the literature that o3’s summary glossed over, and its recommendation for future work was more specific and testable. But without the live search, its citations skewed older.
If you can give Opus 4 the papers directly, it wins on synthesis depth. If you need the model to find and pull sources itself, o3’s native search integration gives it a leg up.
Winner: Draw. Task context determines which model to use.
Task 6: Business Writing and Communication
The prompt: Write a difficult email to an enterprise client explaining a 90-day delay in a contracted deliverable, maintaining the relationship while being honest about internal failures. Tone: accountable but not groveling, confident about resolution.
What happened: Opus 4 wrote an email I would have sent. It calibrated the apologetic tone precisely, did not over-explain, included a specific resolution timeline, and closed with a genuine confidence builder rather than a hollow promise. The voice was human.
o3’s version was technically correct but had the texture of a compliance document. It hit all the right points but in the wrong order for a real business relationship. The tone was too formal in the opening and too casual in the close.
Winner: Claude Opus 4. Writing for human audiences, at any length, is a consistent Opus 4 advantage.
Task 7: Multi-Step Logical Reasoning
The prompt: A classic lateral thinking problem combined with a formal logic puzzle: fifteen clues, four variables, one unique solution. Then a follow-up question requiring the model to explain which clues were redundant and which were strictly necessary for the unique solution.
What happened: o3 solved the puzzle correctly and produced the best answer to the redundancy question. Its reasoning chain was laid out in a way that made the logic transparent: each inference was labeled, dependencies were explicit, and the final identification of redundant clues was backed by showing exactly what the solution space would look like without each one.
Opus 4 solved the puzzle correctly but the reasoning trace was harder to follow. It arrived at the right answer but showed less of its work, which mattered for the follow-up question about redundancy.
For tasks where the process of reasoning is as important as the answer, o3’s structured chains are an advantage.
Winner: ChatGPT o3. Methodical, transparent, thorough.
Task 8: Complex Instruction Following
The prompt: Write a 600-word product brief with nine specific constraints: include three named competitors, use no superlatives, mention a specific pricing tier, avoid passive voice, include exactly two numbered lists, write at an eighth-grade reading level, do not mention the company name until the third paragraph, include a call-to-action in the final sentence, and use British English spelling throughout.
What happened: I ran this prompt three times on each model. Opus 4 honored all nine constraints in two of three runs, slipping only on passive voice in one case. o3 averaged about seven of nine per run. It consistently forgot the no-superlatives constraint and once introduced the company name in the opening sentence despite explicit instructions.
The instruction-following gap between Claude models and OpenAI’s reasoning models has been noted by multiple independent reviewers. Opus 4 was built with strong multi-constraint adherence in mind, and it holds up under this kind of edge-case pressure.
Winner: Claude Opus 4. Reliable under complex, stacked constraints.
Task 9: Factual Accuracy Under Pressure
The prompt: A batch of 20 factual questions: 10 in STEM (recent chemistry, physics, genomics), 10 in general knowledge (history, law, current events). I asked each model to answer with confidence ratings and mark any questions where it was uncertain.
What happened: o3 performed better on STEM questions, particularly in formal science where its extended reasoning helps it avoid confident errors. But the overall picture on factual accuracy is complicated by a well-documented problem.
According to OpenAI’s own internal evaluations, o3 hallucinated in response to 33% of questions on PersonQA, a benchmark measuring accuracy about real people and entities. That is roughly double the rate of o1 and o3-mini, which scored around 15%. OpenAI has publicly said it does not fully understand why this is happening, and TechCrunch’s reporting in April 2025 confirmed the trend. Anthropic has not published an equivalent PersonQA score for Opus 4, but third-party analysis by Artificial Analysis consistently shows lower hallucination rates for Claude models in general.
On my 20-question batch, o3 got 14 right and hallucinated two answers confidently. Opus 4 got 13 right and declined to answer two questions it was uncertain about, rather than confabulating. That difference in calibration matters in production.
Winner: Narrow edge to ChatGPT o3 on STEM accuracy; narrow edge to Claude Opus 4 on calibration and honesty under uncertainty.
Task 10: Agentic Autonomous Workflow
The prompt: Build a research pipeline: given a company name, autonomously research recent news, pull financial signals, summarize competitive positioning, identify three risks to the company’s next fiscal year, and format the output as an investor brief. No further prompting after the initial task.
What happened: This is the task that best demonstrates where Opus 4 was designed to play. Anthropic built the model explicitly for sustained, multi-step agentic work. According to Anthropic’s launch materials, Opus 4 can work continuously on a task for seven or more hours without degrading, which is a design goal that shows in single-session agent tasks too.
Opus 4 completed the full pipeline, followed the output format without a reminder, and flagged two data gaps where it could not find reliable sources rather than filling them with plausible-sounding noise.
o3’s performance was good on the research retrieval steps (the native search integration helped here) but it drifted from the output format in the final section, restructured the brief in a way I had not asked for, and required a follow-up prompt to get the correct structure.
Winner: Claude Opus 4. Purpose-built for agentic workflows, and it shows.
Score Summary
| Task | Winner |
|---|---|
| 1. Complex code debugging | Claude Opus 4 |
| 2. Creative long-form writing | Claude Opus 4 |
| 3. Hard math and competition problems | ChatGPT o3 |
| 4. Long document analysis | Claude Opus 4 |
| 5. Research synthesis | Draw |
| 6. Business writing | Claude Opus 4 |
| 7. Multi-step logical reasoning | ChatGPT o3 |
| 8. Complex instruction following | Claude Opus 4 |
| 9. Factual accuracy | Split |
| 10. Agentic autonomous workflow | Claude Opus 4 |
Tally: Claude Opus 4 wins 5 tasks outright, draws or splits 2, o3 wins 2.
Pricing Reality
The benchmark comparison makes Opus 4 look like the clear winner. The pricing comparison makes it more complicated.
On the API, o3 costs $2 per million input tokens and $8 per million output tokens. Opus 4 at launch cost $15 per million input tokens and $75 per million output tokens. That is roughly a 7x to 9x price difference for similar output quality on the tasks where they are close.
For consumer plans, both cost $20 per month. At that level, the comparison is different: you get access to the best model each company offers for the same flat fee, and Opus 4 wins more tasks.
For developers building applications: the cost difference at the API level is significant enough to change architecture decisions. If your use case is math-heavy, reasoning-heavy, and you can tolerate a somewhat higher hallucination risk on general knowledge, o3 at $2/$8 is a strong option. If your use case is writing, coding, or multi-constraint work and accuracy calibration matters, Opus 4 justifies the premium.
It is also worth noting that Anthropic has consistently reduced Opus pricing across the 4.x family. The Opus 4.5 generation brought pricing down to $5/$25 per million tokens while maintaining strong performance, which closes the gap significantly.
Security and Safety Posture
Both companies publish safety evaluations alongside major model releases, but their approaches differ.
Anthropic has built Claude Opus 4 to meet ASL-3 safety standards, Anthropic’s internal framework for models that could provide meaningful assistance to someone attempting to cause serious harm. Anthropic’s model cards include explicit testing categories for catastrophic risk, CBRN assistance, and autonomous replication. Whether you weight those evaluations heavily depends on your threat model, but the transparency is useful.
OpenAI publishes system cards for o3 that cover similar ground. What is more notable from a practical standpoint is the hallucination pattern: a model that overconfidently generates wrong information is a different kind of safety problem than a model that assists with harmful content, but it is a meaningful risk for any production application.
For enterprise deployments, Anthropic’s Constitutional AI training approach and its explicit emphasis on calibrated uncertainty are meaningful differentiators, particularly in regulated industries where a confident wrong answer creates legal exposure.
Who Should Use What
Choose Claude Opus 4 if:
You write for a living and care about voice. You are building coding agents or agentic workflows. You need reliable multi-constraint output for structured content or document analysis. You are in a regulated industry where calibrated uncertainty matters more than maximum confidence. You are on a consumer plan and want the strongest writing and coding assistant.
Choose ChatGPT o3 if:
Your work is heavily math- or STEM-intensive. You are building on the API and cost is a primary constraint. You need tight integration with web search within the model’s reasoning loop. You value structured, transparent reasoning chains where the process is as important as the answer.
Both are strong options for:
General question answering. Summarization of medium-length documents. Code generation on well-scoped tasks. Customer-facing applications where both quality and cost need to be balanced.
Frequently Asked Questions
Is Claude Opus 4 better than ChatGPT o3? It depends on the task. Opus 4 outperforms o3 on creative writing, complex coding, instruction following, and agentic workflows. o3 outperforms Opus 4 on competition math, STEM reasoning, and structured logical chains. On the API, o3 is significantly cheaper.
Which model is better for coding? Claude Opus 4 won the coding tasks in my testing and scores higher on SWE-bench Verified at launch (72.5% vs 71.7%). Independent testing by Composio across multiple coding benchmarks found Opus 4 the stronger model, particularly on complex, multi-step refactoring and agent-driven development tasks.
Which model halluccinates less? OpenAI reported that o3 hallucinated in 33% of PersonQA questions, roughly double o1’s rate, and acknowledged uncertainty about why. Claude models consistently score lower on hallucination benchmarks, and Anthropic’s training focuses explicitly on calibrated uncertainty. For high-stakes factual work, Opus 4 is the safer choice.
What is the price difference between Claude Opus 4 and o3? At the API, Opus 4 at launch cost $15/$75 per million input/output tokens. o3 costs $2/$8. That is roughly a 7x to 9x difference. Anthropic reduced pricing in later Opus 4.x releases to $5/$25. On consumer plans, both are $20 per month.
Can o3 generate images? Yes. o3 has access to autonomous tool use including image generation. Claude Opus 4 does not generate images natively, though it supports computer use via the Anthropic API.
Which model has the larger context window? Both support 200K tokens. Opus 4 later introduced a 1M token beta window in the 4.6 revision of the family.
Is ChatGPT o3 good for creative writing? o3 produces competent prose but lacks the voice and craft of Opus 4 on creative tasks. In testing, Opus 4-family models consistently outscored o3 and GPT-5.4 on human-rated writing evaluations, including measures of prose quality, tone, and instruction adherence.
Which model is better for business use? It depends on the task category. For document analysis, writing, and coding, Opus 4 is stronger. For math-heavy workflows, structured data extraction, and cost-sensitive deployments, o3 is a better fit. Most enterprise teams end up using both.
How does o3 compare to Claude Sonnet 4? o3 ($2/$8) is priced between Claude Haiku and Claude Sonnet ($3/$15 approximately). In capability terms, Sonnet 4 outperforms o3 on writing and instruction following but o3 is stronger on STEM reasoning. For most developers, the comparison between o3 and Claude Sonnet 4 is more relevant than the Opus 4 comparison from a cost standpoint.
Is Claude Opus 4 good for agentic tasks? Yes, this is one of Opus 4’s clearest strengths. Anthropic built it specifically for sustained, multi-step agentic workflows. The model can maintain task coherence over long sessions, supports computer use via API, and was designed to work reliably without human intervention for extended periods. It powers Claude Code, Anthropic’s command-line coding agent.
What happened to Claude Opus 4 after the original release? Anthropic continued the Opus 4 series with Opus 4.1 (August 2025), 4.5 (November 2025), 4.6 (February 2026), and 4.7 (April 2026). Each revision improved on the original’s capabilities, particularly in coding and agentic performance, while the 4.5 generation cut pricing significantly. The version compared in this article is the original Claude Opus 4 launched May 22, 2025.
Bottom Line
Claude Opus 4 and ChatGPT o3 are both serious models that reward careful matching to the right task. The head-to-head over 10 tasks favors Opus 4 on the kinds of work most knowledge workers and developers actually do: writing, coding, and following complex instructions reliably.
o3’s genuine advantages are on competition math, STEM reasoning, and API cost. The 7x to 9x price gap on the API is real, and for cost-sensitive deployments where the use case aligns with o3’s strengths, it is a persuasive argument.
The hallucination issue with o3 is worth taking seriously. OpenAI’s own evaluation data showed o3 hallucinating at double the rate of its predecessor on person-based knowledge questions, and that pattern does not disappear at higher compute settings. For any application where confident wrong answers create legal, financial, or reputational risk, that is a meaningful consideration.
My honest recommendation: if you are a writer, developer, or running multi-step agent workflows, Claude Opus 4 is the better tool. If you are a researcher or engineer working primarily in math and STEM, and you are building on the API where cost compounds, o3 deserves serious consideration.
Sources: OpenAI o3 launch post (openai.com, April 2025); Anthropic Claude 4 launch post (anthropic.com, May 2025); TechCrunch reporting on o3 hallucination rates (April 2025); ARC Prize benchmark evaluation (arcprize.org); Artificial Analysis Intelligence Index benchmarks; MindStudio independent model evaluations; Composio coding benchmark comparison.
