Model Release10 min read · April 5, 2026

OpenAI o3 vs Claude Opus 4.6: Best AI Reasoning Model in 2026?

Q: Is OpenAI o3 better than Claude Opus 4.6?

OpenAI o3 outperforms Claude Opus 4.6 on pure mathematical and scientific reasoning benchmarks — AIME 2025 (99.5% vs 95.2%) and ARC-AGI-2 (88% vs ~60%). Claude Opus 4.6 outperforms o3 on coding tasks (SWE-bench Verified: 72.5% vs 55%), agentic reliability, instruction following, and general writing quality. The right choice depends on your task: use o3 for math/science research, use Claude Opus 4.6 for coding, agents, and complex multi-step tasks.

Q: What is extended thinking in o3 and Claude Opus 4.6?

Extended thinking (called 'reasoning tokens' in o3, 'extended thinking' in Claude) allows the model to spend additional computation working through a problem step-by-step before producing an answer. The model's internal reasoning process is invisible to the user but significantly improves accuracy on hard problems. o3's extended thinking is more powerful on pure math/science; Claude Opus 4.6's extended thinking is more reliable for software engineering and multi-step agentic tasks.

Q: How much does o3 cost vs Claude Opus 4.6?

OpenAI o3 costs $10 per million input tokens and $40 per million output tokens, plus additional reasoning token costs that can make complex queries expensive. Claude Opus 4.6 costs $15 per million input tokens and $75 per million output tokens. Both are among the most expensive frontier models. For cost-sensitive production use, Claude Sonnet 4.6 ($3/$15 per million tokens) provides 80–90% of Opus 4.6 performance at 5x lower cost.

Q: What is ARC-AGI and why does it matter?

ARC-AGI (Abstraction and Reasoning Corpus for Artificial General Intelligence) is a benchmark designed by François Chollet to test genuine reasoning and novel problem-solving that can't be solved by pattern-matching on training data. ARC-AGI-2 (2025) is even harder — requiring abstract visual reasoning from minimal examples. o3's ~88% score on ARC-AGI-2 is a significant milestone; it's the first model to clearly exceed expert-level human performance on this benchmark. Claude Opus 4.6 scores approximately 60% on ARC-AGI-2.

TL;DR

o3 wins on: Pure math (AIME 99.5%), abstract reasoning (ARC-AGI-2 ~88%), hard science problems
Claude Opus 4.6 wins on: Coding (SWE-bench 72.5%), agentic tasks, instruction following, writing quality
Both excel at: Extended thinking, complex multi-step reasoning, graduate-level science
Pricing: o3 at $10/$40 per M tokens; Opus 4.6 at $15/$75 per M tokens
Verdict: Use o3 for research/math; use Claude Opus 4.6 for coding/agents

OpenAI's o3 and Anthropic's Claude Opus 4.6 are the two most capable AI reasoning models available in 2026 — and they've taken different bets on what "reasoning" means.

o3 optimizes for mathematical and scientific reasoning through massive test-time compute scaling. Claude Opus 4.6 optimizes for reliable agentic execution — the ability to reason across long, multi-step software engineering and research tasks without derailing. Both are exceptional, and they're not interchangeable.

Model Overview

Spec	OpenAI o3	Claude Opus 4.6
Developer	OpenAI	Anthropic
Architecture	Reasoning model (test-time compute scaling)	Extended thinking + agentic optimization
Context window	200K tokens	200K tokens
Input price (per M tokens)	$10	$15
Output price (per M tokens)	$40	$75
Thinking tokens (reasoning)	$10 per M reasoning tokens	Billed as output tokens
Latency (first token)	High (reasoning first)	High (extended thinking)
Multimodal	Image + text	Image + text

Benchmark Showdown

Benchmark	OpenAI o3	Claude Opus 4.6	What It Tests
AIME 2025 (math olympiad)	99.5%	95.2%	Hard competition math
ARC-AGI-2	~88%	~60%	Abstract visual reasoning
GPQA Diamond (science)	82.4%	79.1%	PhD-level science questions
SWE-bench Verified (coding)	55.0%	72.5%	Real GitHub bug fixing
HumanEval (code gen)	93.1%	96.1%	Python function generation
MMLU (general knowledge)	91.2%	91.8%	57-subject general knowledge
Frontier Math (new math)	25.4%	18.7%	Unpublished research-level math

How Extended Thinking / Reasoning Works

Both models have a "thinking" mode where they spend extra tokens reasoning through a problem before answering. They differ significantly in how they use this compute:

OpenAI o3 Reasoning

• Scales test-time compute dynamically based on problem difficulty
• Internal chain-of-thought is hidden (not visible to user)
• Excels on problems with clear right/wrong answers (math, logic, science)
• Reasoning tokens billed at $10/M — adds up fast on hard problems
• First token latency: 5–30+ seconds on hard problems

Claude Opus 4.6 Extended Thinking

• Configurable thinking budget (set max tokens for reasoning)
• Partial visibility into thinking process (shows reasoning steps)
• Excels at long multi-step tasks and agentic software engineering
• Thinking tokens billed as output tokens ($75/M)
• First token latency: 3–15 seconds with extended thinking enabled

What ARC-AGI Actually Means

ARC-AGI (Abstraction and Reasoning Corpus for AGI) was designed by François Chollet to test genuine novel reasoning — problems that can't be solved by pattern-matching on training data. It presents grids with colored patterns and asks the model to identify the rule and apply it to a new case.

ARC-AGI-2 (2025 version) is significantly harder — requiring multi-step abstract reasoning from just 2–3 demonstration pairs. Human experts score about 85%.

Model	ARC-AGI-2 Score	Notes
Human experts	~85%	Baseline human performance
OpenAI o3 (high compute)	~88%	First model to clearly exceed human experts
Claude Opus 4.6	~60%	Strong, but below human level
GPT-5.4	~65%	Non-reasoning model comparison

o3's ARC-AGI-2 score is genuinely significant — it represents a meaningful advance in abstract reasoning beyond training data. However, ARC-AGI is one benchmark. It measures a specific type of abstract visual reasoning, not general intelligence or practical task performance.

Where o3 Excels: Math, Science, and Research

Competition mathematics

o3 scores 99.5% on AIME 2025 — competition math designed to challenge the top 1% of high school students. It solves problems that require creative mathematical insight, not just computation. Claude Opus 4.6 at 95.2% is still exceptional but o3 is the clear leader here.

Novel scientific reasoning

For problems that require combining multiple scientific domains in novel ways — experimental design, hypothesis generation, research paper analysis — o3's extended reasoning produces more rigorous and creative results. NASA's Perseverance rover deployed Claude for navigation decisions; for pure scientific analysis, o3 is often the stronger choice.

Formal verification and logic

Proving mathematical theorems, verifying formal logic, and checking proofs is where o3's test-time compute scaling shines. It can allocate more thinking time to harder steps within a proof.

Where Claude Opus 4.6 Excels: Coding, Agents, and Long Tasks

Software engineering (SWE-bench)

Claude Opus 4.6 scores 72.5% on SWE-bench Verified vs o3's 55%. Real GitHub issue resolution requires not just reasoning but understanding code architecture, tracking state across files, and executing multi-step fixes correctly — areas where Opus 4.6's agentic training pays off.

Long agentic workflows

For tasks requiring 10–50+ sequential steps — building a feature, conducting research and writing a report, refactoring a codebase — Claude Opus 4.6 maintains coherence and follows instructions more reliably over extended sessions. Claude Code is specifically optimized for this.

Instruction following

Claude Opus 4.6 consistently outperforms o3 on complex system prompt compliance — following formatting rules, persona constraints, negative instructions, and multi-part requirements across long conversations.

Decision Matrix

Use Case	Best Model	Why
Competition math, theorem proving	OpenAI o3	AIME 99.5%, best test-time compute scaling for hard math
Autonomous software engineering	Claude Opus 4.6	SWE-bench 72.5%, Claude Code integration, agentic reliability
Scientific research analysis	o3	GPQA 82.4%, stronger on novel scientific reasoning
Long multi-step agent tasks	Claude Opus 4.6	More reliable instruction following over extended sessions
Drug discovery, protein design	o3	Best on biology/chemistry reasoning benchmarks
Complex writing with nuance	Claude Opus 4.6	Constitutional AI training produces better-calibrated, nuanced text
Budget-conscious reasoning tasks	o3 mini / Sonnet 4.6	o3 mini is significantly cheaper; Sonnet 4.6 is 5x cheaper than Opus

Access Claude Opus 4.6 with HappyCapy

HappyCapy gives you access to Claude-powered AI capabilities for coding, research, writing, and agents. Start with a free trial — no API key needed.

Try HappyCapy Free

Frequently Asked Questions

Is OpenAI o3 better than Claude Opus 4.6?

o3 is better for pure math and abstract reasoning (AIME 99.5%, ARC-AGI-2 ~88%). Claude Opus 4.6 is better for coding (SWE-bench 72.5% vs 55%) and agentic tasks. The right model depends entirely on what you're building.

What is extended thinking in o3 and Claude Opus 4.6?

Both models can spend extra compute reasoning through problems before answering. o3's reasoning tokens are billed at $10/M; Claude's extended thinking is billed as output tokens at $75/M. o3 excels on formal reasoning; Claude Opus 4.6 excels on software engineering with extended thinking enabled.

How much does o3 cost vs Claude Opus 4.6?

o3 costs $10/$40 per million input/output tokens. Claude Opus 4.6 costs $15/$75. Both are expensive — use Claude Sonnet 4.6 ($3/$15) for most production workloads where the top-tier quality premium isn't justified.

What is ARC-AGI and why does it matter?

ARC-AGI tests abstract visual reasoning that can't be solved by training data pattern-matching. o3 scoring ~88% on ARC-AGI-2 (vs ~85% human experts) is a meaningful milestone in genuine reasoning. Claude Opus 4.6 scores ~60% — strong but not yet at human expert level on this specific benchmark.

Sources: OpenAI o3 system card, Anthropic Claude Opus 4.6 model card, ARC-AGI-2 benchmark results, SWE-bench leaderboard, AIME 2025 benchmark results, GPQA Diamond leaderboard, Frontier Math benchmark (Epoch AI).