Summary
Quick Decision (March 2026)
- Choose Opus 4.6 if: You need a general-purpose model for coding, writing, and analysis. It leads SWE-bench Verified (80.8%), is fast enough for interactive use (46 tok/s), and costs $5/$25 per million tokens. Best for software engineering and daily AI workflows.
- Choose o3 if: You need maximum reasoning depth on math, science, or abstract problems. It leads ARC-AGI (87.5%), MATH 500 (96.7%), and competition programming. Significantly slower and more expensive. Best for reasoning-heavy tasks where accuracy is the only metric that matters.
- Use both via Morph: Route standard tasks to Opus for speed and cost. Route reasoning-intensive tasks to o3. One API, task-appropriate compute allocation.
Opus 4.6 and o3 are designed for different workloads. Opus is a foundation model that handles everything and reasons well enough for most tasks. o3 is a reasoning specialist that handles a narrower set of tasks at higher accuracy. The question is whether your tasks hit the ceiling of Opus's reasoning or whether they need the generalist capability that o3 lacks.
Stat Comparison
Claude Opus 4.6
Foundation model with hidden reasoning
"Best general-purpose model. Coding leader. Fast enough for interactive use."
OpenAI o3
Dedicated reasoning model
"Strongest reasoning. Trades speed and versatility for depth."
Architecture: Foundation vs Reasoning Model
The fundamental architectural difference explains every benchmark gap and every trade-off.
| Aspect | Opus 4.6 | o3 |
|---|---|---|
| Model type | Foundation model | Reasoning model |
| Reasoning approach | Hidden thinking traces (moderate compute) | Extended chain-of-thought (high compute) |
| Reasoning tokens | Moderate (invisible to user) | Extensive (thousands per response) |
| Compute per response | Moderate | High to very high (configurable) |
| Task scope | General: code, write, analyze, chat | Focused: math, science, logic, code |
| Compute scaling | Fixed per request | Scales with problem difficulty |
Opus: Selective Reasoning
Opus 4.6 generates hidden thinking traces before every response. These traces are moderate in length and invisible to the user. They add 5-8 seconds of latency but improve accuracy on hard problems. The key trade-off: Opus allocates a fixed amount of reasoning compute per request. It does not scale compute based on problem difficulty.
o3: Scaled Reasoning
o3 generates extensive chain-of-thought reasoning, often producing thousands of internal tokens before the visible answer. At high compute settings, it can spend 30-60+ seconds reasoning through a single problem. The compute scales with difficulty: simple problems get fewer reasoning tokens, hard problems get more. This is why o3 leads on benchmarks designed to test reasoning limits.
The Core Trade-off
Opus spends fixed reasoning compute across all task types, making it fast and versatile. o3 spends variable reasoning compute scaled to difficulty, making it slower but stronger on the hardest problems. Neither approach is universally better. It depends on the difficulty distribution of your workload.
Reasoning Benchmarks
Reasoning benchmarks test logical deduction, mathematical proof, scientific knowledge, and abstract pattern recognition. This is where o3 was built to dominate.
| Benchmark | Opus 4.6 | o3 | What It Tests |
|---|---|---|---|
| ARC-AGI | ~68% | 87.5% | Abstract pattern recognition from minimal examples |
| MATH 500 | 96.4% | 96.7% | Competition-level math problems |
| GPQA Diamond | 68.4% | ~79% | Graduate-level science questions |
| AIME 2024 | ~16/30 | ~27/30 | American math competition |
| Codeforces | ~1800 ELO | ~2700 ELO | Competitive programming rating |
ARC-AGI: o3's Defining Benchmark
ARC-AGI tests whether a model can infer abstract rules from a handful of examples and apply them to new inputs. Previous frontier models scored below 50%. o3 scored 87.5% at high compute, a result that was widely discussed as a milestone in AI reasoning. Opus scores around 68% on comparable abstract reasoning tasks. The 19.5-point gap is the largest between these models on any benchmark.
MATH 500: Nearly Tied
Opus at 96.4%, o3 at 96.7%. A 0.3-point gap. On competition-level math, both models are near-saturated. The real differentiation comes on harder math: AIME 2024, where o3 solves 27/30 problems vs Opus at roughly 16/30. The harder the math, the more o3's extended reasoning pays off.
Competitive Programming: Large Gap
o3 achieves a Codeforces rating around 2700, competitive with strong human programmers. Opus sits around 1800. The 900-point gap reflects o3's ability to spend extensive compute on algorithmic problem-solving. For Codeforces-style problems (pure algorithms with well-defined inputs/outputs), o3 is in a different class.
Coding Benchmarks
Coding is where the distinction between "reasoning model" and "foundation model" matters most. Real-world software engineering requires more than pure reasoning.
| Benchmark | Opus 4.6 | o3 | What It Tests |
|---|---|---|---|
| SWE-bench Verified | 80.8% | ~71% | Real GitHub issue resolution (500 tasks) |
| SWE-bench Pro | 55.4% | ~50% | Harder GitHub issues, cleaner dataset |
| HumanEval | 97.6% | ~97% | Function-level code generation |
| Codeforces | ~1800 ELO | ~2700 ELO | Algorithmic competitive programming |
SWE-bench: Opus Leads by 10 Points
Opus scores 80.8% on SWE-bench Verified vs o3 at roughly 71%. The 10-point gap is significant. SWE-bench tests real software engineering: reading codebases, understanding interdependencies, making targeted fixes, and passing test suites. This requires breadth (understanding frameworks, APIs, test patterns) more than pure reasoning depth.
o3's extended reasoning does not help much on SWE-bench because the bottleneck is not reasoning depth but codebase understanding. Opus's foundation model training gives it broader knowledge of libraries, frameworks, and real-world code patterns.
Codeforces: o3 Dominates
On Codeforces, the inverse is true. Competitive programming problems have well-defined inputs, outputs, and algorithmic solutions. The bottleneck is pure reasoning: finding the optimal algorithm, proving correctness, and handling edge cases. o3's 2700 ELO vs Opus's 1800 shows the advantage of extended chain-of-thought on pure algorithmic problems.
Opus: Real-World Software Engineering
SWE-bench tests real GitHub issues in real codebases. Opus leads by 10 points (80.8% vs ~71%). Its foundation model training provides broader understanding of frameworks, APIs, and code patterns. The bottleneck here is knowledge breadth, not reasoning depth.
o3: Algorithmic and Competitive Coding
Codeforces, AIME, and competition problems have well-defined solutions requiring pure algorithmic reasoning. o3's extended chain-of-thought produces 2700 ELO vs Opus's 1800. The bottleneck here is reasoning depth, not knowledge breadth.
Speed and Pricing
o3's extended reasoning comes at a cost in both latency and dollars.
| Metric | Opus 4.6 | o3 |
|---|---|---|
| Time to first token | ~7.83s | 10-60+ seconds |
| Output speed | ~46 tok/s | Variable (compute-dependent) |
| Typical response time | 10-20s | 30-120s |
| Input pricing | $5 / 1M tokens | ~$10-15 / 1M tokens |
| Output pricing | $25 / 1M tokens | ~$40-60 / 1M tokens |
| Reasoning token cost | Included (hidden) | Billed separately at output rate |
The Reasoning Token Tax
o3 generates thousands of reasoning tokens per response. These tokens are billed at the output rate. A response that shows 200 visible tokens might generate 5,000 reasoning tokens internally. At $40-60 per million output tokens, a complex reasoning task can cost $0.20-0.50 per request. Opus's hidden thinking traces are included in the standard pricing with no separate charge.
Cost Consideration
o3's per-request cost can be 3-6x higher than Opus on reasoning-heavy tasks. For high-volume workloads, this compounds fast. Reserve o3 for the tasks that genuinely require its reasoning depth. Route everything else to Opus or cheaper models.
When to Use Opus 4.6
Software Engineering
Opus leads SWE-bench Verified at 80.8% (vs o3's ~71%). For real-world coding, bug fixing, refactoring, and feature implementation, Opus's broader training on code patterns and frameworks provides a measurable advantage over pure reasoning.
Interactive Workflows
Opus responds in 10-20 seconds. o3 takes 30-120 seconds. For coding copilots, chat interfaces, and any use case where the human is waiting, Opus is 3-6x faster. The speed difference is the difference between a useful tool and a frustrating one.
Writing and Analysis
Opus handles writing, summarization, research analysis, and conversation. o3 is not designed for these tasks. If your workload mixes coding with writing and analysis, Opus covers everything. o3 covers only the reasoning portion.
Cost-Sensitive High-Volume
At $5/$25 per million tokens with no separate reasoning token charge, Opus is 3-6x cheaper than o3 on reasoning-heavy tasks. For 10,000 daily API calls, the monthly cost difference can exceed $30,000.
When to Use o3
Competition-Level Math
o3 solves 27/30 AIME 2024 problems (vs Opus at ~16/30) and scores 96.7% on MATH 500. For mathematical proofs, number theory, combinatorics, and analysis, o3's extended reasoning handles problems that push the limits of what AI can solve.
Abstract Pattern Recognition
87.5% on ARC-AGI. This benchmark tests the ability to infer rules from minimal examples. For tasks requiring novel reasoning without prior training data, anomaly detection in unfamiliar domains, or pattern discovery, o3 is the benchmark leader.
Scientific Reasoning
o3 scores ~79% on GPQA Diamond (vs Opus at 68.4%). For graduate-level physics, chemistry, and biology questions requiring multi-step logical chains, o3's extended reasoning produces more accurate conclusions.
Algorithmic Programming
2700 ELO on Codeforces (vs Opus at ~1800). For competitive programming, algorithm design, and pure algorithmic problem-solving with well-defined inputs and outputs, o3's reasoning depth is in a different class.
Routing Between Both
Most workloads contain a mix of tasks: some requiring deep reasoning, most not. The optimal strategy is routing each task to the appropriate model.
Morph: Foundation + Reasoning Model Routing
# Morph routes based on task complexity
# Standard coding task → Opus (fast, cost-effective, 80.8% SWE-bench)
response = client.chat.completions.create(
model="morph-v3-fast",
messages=[{"role": "user", "content": "Fix the race condition in the connection pool"}]
)
# Reasoning-heavy task → optimal reasoning model
response = client.chat.completions.create(
model="morph-v3-fast",
messages=[{"role": "user", "content": "Prove that this distributed consensus algorithm is correct under network partition"}]
)
# Same API. Morph detects reasoning requirements and routes accordingly.Frequently Asked Questions
Is Opus 4.6 or o3 better?
Opus for software engineering (80.8% SWE-bench), general-purpose tasks, and cost-effective high-volume use. o3 for competition math (27/30 AIME), abstract reasoning (87.5% ARC-AGI), and scientific reasoning (79% GPQA Diamond). Different tools for different problems.
Is o3 better for coding than Opus?
No, for real-world software engineering. Opus leads SWE-bench Verified by 10 points (80.8% vs ~71%). Yes, for algorithmic competitive programming. o3 leads Codeforces ELO by 900 points (2700 vs 1800). The distinction is between practical coding and algorithmic reasoning.
How much does o3 cost vs Opus?
o3: roughly $10-15 input, $40-60 output per million tokens, plus reasoning tokens billed at output rate. Opus: $5/$25, reasoning tokens included. On reasoning-heavy tasks, o3 costs 3-6x more per request.
How slow is o3?
o3 takes 30-120 seconds per response on hard problems, generating thousands of internal reasoning tokens. Opus responds in 10-20 seconds. For interactive use, Opus is practical. o3 is best suited for batch or asynchronous workflows.
What is ARC-AGI?
ARC-AGI (Abstraction and Reasoning Corpus) tests whether models can infer abstract rules from a few examples and apply them to new inputs. Previous models scored below 50%. o3 scored 87.5% at high compute. It is considered one of the hardest reasoning benchmarks and a proxy for general intelligence.
Can I use both through one API?
Morph's API routes between foundation models (Opus, Sonnet) and reasoning models (o3) based on task complexity. Standard tasks get Opus-tier speed and cost. Reasoning-intensive tasks get the appropriate model. One endpoint, automatic routing.
Route Between Foundation and Reasoning Models
Morph's API sends standard tasks to fast foundation models and reasoning-heavy tasks to specialized models. One endpoint. Optimal cost per task. No manual model selection.