Claude Opus 4.6 outscores GPT-4o on the majority of major benchmarks, but GPT-4o costs half as much. We break down every benchmark, pricing tier, and use case so you can pick the right model.
Claude Opus 4.6 leads the SWE-bench Verified leaderboard with scores ranging from approximately 72–81% depending on scaffolding configuration (self-reported). OpenAI's o3 with scaffolding manages 69.1%. That gap represents a serious capability divide in real-world software engineering benchmarks.
The 2026 flagship model race has become a two-horse contest between Anthropic and OpenAI (see also our Opus 4.6 vs GPT-5 comparison and OpenAI vs Anthropic API breakdown). Both companies shipped major updates, and the benchmark results paint a surprisingly split picture. Claude Opus 4.6 dominates coding and knowledge tasks. OpenAI's models (GPT-4o for general use, o3 for deep reasoning) push back hard on math and novel problem-solving.
So which model actually deserves your API budget in the Claude Opus 4.6 vs GPT-4o showdown? Eight major benchmarks tell the full story, and the answer isn't as simple as you'd expect.
On raw benchmark performance, Claude Opus 4.6 is the stronger model. It outscores GPT-4o on the majority of major benchmarks, including MMLU, HumanEval, SWE-bench, GSM8K, and the LMSYS Chatbot Arena. However, GPT-4o costs roughly half as much and performs nearly identically in blind conversational quality tests.
Choose Claude Opus 4.6 if you're building coding tools, need strong software engineering performance, or want a massive 1M token context window.
Choose GPT-4o if you want solid all-around performance at roughly half the price. It runs $2.50/$10 per million tokens versus Opus 4.6's $5/$25.
Choose o3 if you need peak mathematical reasoning. It's unmatched on MATH (96.7%), GPQA Diamond (87.7%), and ARC-AGI (87.5%). But it's a specialized reasoning model, not a general-purpose workhorse.
The short version: Opus 4.6 for coding and analysis. o3 for math and science. GPT-4o if cost is your primary concern.
| Feature | Claude Opus 4.6 | GPT-4o |
|---|---|---|
| Developer | Anthropic | OpenAI |
| Context Window | 1,000,000 tokens | 128,000 tokens |
| Input Cost | $5 / MTok | $2.50 / MTok |
| Output Cost | $25 / MTok | $10 / MTok |
| MMLU | 92.3% | 88.7% |
| HumanEval | 93.7% | 90.2% |
| GSM8K | 97.8% | 95.8% |
| LMSYS Elo | ~1,496 | ~1,345 |
Bold indicates the leader in each category. For SWE-bench, MATH, GPQA Diamond, and ARC-AGI, see the detailed breakdowns below, since the two models aren't always tested in the same benchmark runs.
This is where Opus 4.6 makes its strongest argument.
On HumanEval, which measures code generation accuracy, Opus 4.6 scores 93.7% versus GPT-4o's 90.2% (self-reported by each provider). That lead is consistent across evaluations and puts Opus 4.6 at the top of the leaderboard. DeepSeek V3 (82.6% per its technical report) trails further behind.
But the real differentiator is SWE-bench Verified. This benchmark tests whether a model can fix actual GitHub issues pulled from popular open-source repositories. It's the closest thing the industry has to measuring practical software engineering ability.
According to SWE-bench data, Claude Opus 4.6 with scaffolding scores approximately 72–81% depending on the scaffolding configuration (self-reported). OpenAI's o3 with scaffolding reaches 69.1%. GPT-4.1, OpenAI's code-focused model, sits at 54.6%. And DeepSeek R1 comes in at 49.2%.
Opus 4.6's SWE-bench performance represents the highest reported score from any model (self-reported; scores vary by scaffolding configuration). For developers evaluating which AI to integrate into their workflow, this single benchmark might matter more than all the others combined.
If you're building anything that touches code, SWE-bench is the benchmark that matters — and Opus 4.6 owns it.
Tools built on Claude's coding strengths reflect this advantage. Claude Code, Anthropic's CLI coding agent, carries a 9.4/10 user rating, making it one of the highest-rated developer tools available. Cursor, the AI-first IDE that also supports Claude, scores 9/10. If you're building anything that generates, reviews, or modifies code, the data points toward Opus 4.6.
On MMLU (Massive Multitask Language Understanding), Claude Opus 4.6 leads convincingly: 92.3% versus GPT-4o's 88.7%. That's a 3.6-point margin on the most widely cited general knowledge benchmark. DeepSeek R1 sits at 90.8% (per its technical report), between the two flagships.
The LMSYS Chatbot Arena reinforces the pattern. This benchmark relies on blind human preference voting, where real users compare model responses side by side without knowing which model produced which answer. Claude Opus 4.6 leads with an Elo of approximately 1,496 compared to GPT-4o's approximately 1,345 (scores fluctuate as the leaderboard updates).
That roughly 150-point Elo gap is significant. Opus 4.6 ranks among the top models on the Arena leaderboard, while GPT-4o has been surpassed by several newer models from multiple providers.
Practically speaking? Opus 4.6 has a measurable edge on both knowledge accuracy and human preference rankings. GPT-4o remains a solid performer at its price point, but it no longer competes at the top tier of conversational quality.
If math and formal reasoning are your top priority, OpenAI wins this category convincingly. Just not through GPT-4o.
On the MATH benchmark, o3 scores approximately 96.7% and o1 scores 94.8% (self-reported by OpenAI; note that different MATH benchmark variants like MATH-500 and the full MATH dataset yield different scores). Claude Opus 4.6's MATH performance is reported at approximately 85–94% depending on the source and evaluation methodology. OpenAI's reasoning-optimized models hold a clear lead in this category.
GPQA Diamond (graduate-level science questions) shows strong results across providers. o3 scored 87.7% in its preview evaluation (self-reported by OpenAI; the publicly released model may score differently), and o1 at approximately 78%. Anthropic reports Claude Opus 4.6 at approximately 91.3% on GPQA Diamond, which would put it competitive with or ahead of most other models. DeepSeek R1 scores 71.5% (per its technical report).
On GSM8K (grade-school math), the gaps narrow considerably. o3 reportedly hits 99.2%, Opus 4.6 reportedly reaches 97.8%, and GPT-4o manages 95.8% (all self-reported). All three handle practical everyday math with ease.
The important distinction here: o3 is a specialized reasoning model, not a direct competitor to Opus 4.6 or GPT-4o in the general-purpose category. Comparing o3's math performance to Opus 4.6 is a bit like comparing a race car to a luxury sedan on a track. They're built for fundamentally different jobs.
ARC-AGI tests reasoning on problems a model has genuinely never encountered. It aims to measure something approaching general intelligence, and the results here are dramatic.
o3 preview on high compute: 87.5% on ARC-AGI-1 (confirmed by ARC Prize; the publicly released o3 scored 82%). Claude Opus 4.6: 68.8% on ARC-AGI-2. DeepSeek R1: 15.8% on ARC-AGI-1 (per ARC Prize analysis). Note: scores come from different ARC-AGI versions, making direct comparison difficult.
OpenAI's reasoning-optimized models show notably strong performance on abstract reasoning tasks. The gap between o3 and other models is significant, though direct comparison is complicated by different ARC-AGI benchmark versions being used for different models.
That said, ARC-AGI performance doesn't always predict real-world usefulness. Strong ARC-AGI scores indicate flexible abstract pattern recognition, which matters enormously for some applications and barely registers for others. If you're building a customer support bot, this benchmark is irrelevant. If you're working on scientific discovery tools, it could be the most important number on this page.
Cost matters. For many production deployments, it matters more than any single benchmark score.
Claude Opus 4.6 costs $5 per million input tokens and $25 per million output tokens. GPT-4o charges $2.50 and $10 respectively. That makes GPT-4o roughly half the price across the board.
For high-volume API applications processing millions of tokens daily, that 2x difference translates to thousands of dollars per month. If your application needs strong AI performance rather than the absolute peak, GPT-4o's price-to-performance ratio is genuinely hard to argue with.
For teams processing millions of tokens daily, the 2x price gap between GPT-4o and Opus 4.6 can mean thousands saved per month — a real budget line item, not a rounding error.
Context window size complicates the math, though. Opus 4.6 offers 1,000,000 tokens versus GPT-4o's 128,000. If your use case involves long documents, full codebases, or extended multi-turn conversations that push past 128K, Opus 4.6 handles it natively with its 1M token context window. That massive context capacity has real dollar value when it saves you from building complex retrieval pipelines.
| Model | Input | Output | Context |
|---|---|---|---|
| Claude Opus 4.6 | $5/MTok | $25/MTok | 1M |
| Claude Sonnet 4.6 | $3/MTok | $15/MTok | 1M |
| GPT-4o | $2.50/MTok | $10/MTok | 128K |
| Gemini 2.5 Pro | $1.25/MTok | $10/MTok | 1M |
| Mistral Large 2 | $2/MTok | $6/MTok | 128K |
Anthropic also offers Sonnet 4.6 at $3/$15, which deserves a mention. It scores 89.5% on MMLU and 88% on HumanEval, competitive with GPT-4o at a similar price point. And if context window size is your deciding factor above all else, Google's Gemini 2.5 Pro offers a 1-million-token context window, matching Opus 4.6.
Winner: Claude Opus 4.6. The SWE-bench and HumanEval leads are decisive. For code generation, debugging, code review, and building AI-powered developer tools, Opus 4.6 is the strongest model you can use today. Pair it with Claude Code for terminal-based agentic coding, or run it through Cursor for IDE-integrated assistance.
Winner: Claude Opus 4.6. Higher MMLU scores plus a 1M context window make it better suited for processing long research papers, legal documents, or technical specifications. Fewer chunks means better coherence across the full document.
Winner: o3. Nothing competes with 96.7% on MATH and 87.7% on GPQA Diamond. If your application involves advanced calculations, scientific reasoning, or mathematical proof verification, o3 is the clear pick.
Winner: GPT-4o. At half the token cost with strong general performance, GPT-4o makes the most financial sense for applications serving millions of requests. The broader OpenAI ecosystem, including ChatGPT and a deep third-party integration library, adds practical value.
Winner: Claude Opus 4.6. Opus 4.6 leads GPT-4o by a significant margin on the LMSYS Chatbot Arena. GPT-4o remains a reasonable choice if cost is the priority.
This comparison focuses on Anthropic and OpenAI's flagships, but the competitive field is crowded. DeepSeek has emerged as a powerful open-source alternative, with R1 scoring 90.8% on MMLU and 97.3% on MATH-500 (self-reported). For teams that want to self-host or need complete control over their model, DeepSeek is the most capable open option available.
Google's Gemini 2.5 Pro occupies a notable position with its 1-million-token context window. If your application needs to process entire codebases, full books, or massive datasets in a single prompt, Gemini's large context capacity is a strong differentiator.
Meta's Llama 4 Maverick, with 1 million tokens of context and open weights, gives developers a self-hosting option with impressive scale. Pricing depends entirely on your infrastructure, but the flexibility of running your own model can be valuable for privacy-sensitive deployments.
Overall winner for most use cases: Claude Opus 4.6.
It leads on the benchmarks that matter most for practical AI applications: coding (93.7% HumanEval, 72% SWE-bench), general knowledge (92.3% MMLU), and everyday math (97.8% GSM8K). The 1M context window provides flexibility that 128K simply can't match. And the quality gap on software engineering tasks is large enough to justify the higher token price for developer-focused applications.
Best value: GPT-4o. Strong across the board at half the cost. The right choice for budget-conscious production deployments where you need reliable performance without paying premium rates.
Best for reasoning: o3. Unmatched on mathematical and scientific benchmarks. A specialist tool for specialist problems.
The honest take? Many production systems in 2026 will benefit from routing queries to multiple models. Send math-heavy tasks to o3, coding work to Opus 4.6, and high-volume general queries to GPT-4o. Picking a single model for everything is becoming less practical as each model develops clear strengths.
Sources
Yes. Services like OpenRouter and LiteLLM provide unified APIs that let you access both Claude and GPT models with a single integration. This makes it practical to route different types of queries to different models based on task requirements, without maintaining separate API clients for Anthropic and OpenAI.
Yes, Claude Opus 4.6 is multimodal and can analyze images, charts, screenshots, and PDF documents alongside text. GPT-4o also supports vision inputs. Both models accept images directly in API requests, though file format support and size limits differ between providers. Check Anthropic's and OpenAI's current API documentation for exact specifications.
OpenAI offers fine-tuning for GPT-4o through their API, letting you train on custom datasets to improve performance for specialized tasks. Anthropic does not currently offer public fine-tuning for Opus 4.6. If custom model training is a requirement for your workflow, this is a meaningful differentiator in OpenAI's favor.
Both models handle major world languages well, but publicly available multilingual benchmarks for the latest versions are limited. GPT-4o has historically strong multilingual performance from OpenAI's broad training data. Anthropic has improved Claude's multilingual capabilities significantly, but for less common languages, testing both models on your specific language pair before committing is strongly recommended.
Absolutely. Sonnet 4.6 costs $3/$15 per million tokens (40% less than Opus 4.6) and scores 89.5% on MMLU and 88% on HumanEval, putting it in the same performance range as GPT-4o. For many applications, Sonnet 4.6 hits the sweet spot between Claude-level quality and competitive pricing. It also shares Opus 4.6's 1M context window.
