Claude Opus 4.7 vs GPT-5.5: Which Frontier Model Is Best?

If you're deciding between Claude Opus 4.7 and GPT-5.5 for production agentic work, the choice is less obvious than it looks. Both are flagship models from their respective companies, both target complex multi-step tasks, and both arrived within weeks of each other in early 2026.

Anthropic released Claude Opus 4.7 on April 16, 2026, positioning it as a hybrid reasoning model built for long-running agentic coding and complex tool use. OpenAI followed with GPT-5.5, emphasizing efficiency gains and stronger long-context reasoning. Neither is a clear winner across the board. The benchmarks split in interesting ways, and the answer depends on what you're actually building.

In this article, I'll compare Claude Opus 4.7 and GPT-5.5 across five key dimensions: coding and agentic workflows, reasoning and knowledge tasks, tool use and computer interaction, multimodal capabilities, and pricing. For background on each model individually, I recommend reading our guides on Claude Opus 4.7 and GPT-5.5.

What Is GPT-5.5?

GPT-5.5 is OpenAI's agentic-focused model released on April 23, 2026. It comes in two variants: the standard GPT-5.5 and GPT-5.5 Pro, a higher-capability tier aimed at demanding business, legal, and data science tasks. GPT-5.5 Pro is roughly 6x more expensive per token than the base model.

The headline claims from OpenAI are improved token efficiency (fewer tokens to complete the same Codex tasks) and long-context reasoning that holds up past 128K tokens all the way to 1M, besides performance increases on agentic coding, computer use, and knowledge work. OpenAI also reports that an internal version of GPT-5.5 contributed to a new proof about off-diagonal Ramsey numbers. GPT-5.5 is available in ChatGPT and Codex, with API access rolling out separately.

For a full breakdown of GPT-5.5's benchmarks and efficiency claims, see our GPT-5.5 guide, where we tested long-context retrieval across a 300K-token document.

What Is Claude Opus 4.7?

Claude Opus 4.7 is Anthropic's current publicly available flagship model, released on April 16, 2026. It's the successor to Claude Opus 4.6 and sits below the internal-only Mythos Preview in Anthropic's lineup. The model is built for complex agentic workflows, advanced software engineering, and long-horizon tasks that require sustained performance across sessions.

The most significant changes from Opus 4.6 are a 10.9-point gain on SWE-bench Pro (53.4% to 64.3%), a three-fold increase in visual resolution (up to 3.75MP), improved file-system memory, and a new xhigh reasoning effort level sitting between high and max. Pricing is $5 per million input tokens and $25 per million output tokens, unchanged from Opus 4.6. The model is available via the Claude API (model ID: claude-opus-4-7), Amazon Bedrock, Google Cloud Vertex AI, and Microsoft Foundry.

If you want to see Opus 4.7 in action, our Claude Opus 4.7 Practical Benchmark tutorial walks you through testing whether its file-system memory actually improves coding performance across effort levels. You might also be interested in how it compares to another competitor in our Claude Opus 4.7 vs Gemini 3.1 Pro guide.

GPT-5.5 vs Claude Opus 4.7: Head-to-Head Comparison

Here's a quick reference before we get into the details.

Agentic coding

This is the dimension where the gap between the two models is most visible, without having one clear overall winner. 

GPT-5.5 is specifically designed for agentic coding loops: it checks its own work, continues until task completion, and is built to handle multi-step tasks with minimal user guidance. Opus 4.7 takes a similar approach, with self-output verification, task budgets, improved system-file memory, and a new xhigh reasoning effort level that sits at 10,000 thinking tokens between high (5,000) and max (20,000).

On SWE-bench Pro, Opus 4.7 leads with an impressive 64.3% versus GPT-5.5's 58.6%. In Terminal-Bench 2.0, the picture is reversed, with Opus 4.7 (69.4%) trailing GPT-5.5 (82.7%) significantly, by over ten percentage points. 

If your team mostly ships code (fixing bugs, building features across large repos), Opus 4.7's SWE-bench Pro lead makes it the better fit, but for terminal-heavy DevOps workflows like server setup and multi-step shell automation, GPT-5.5's dominant Terminal-Bench score gives it a clear edge.

Reasoning and knowledge tasks

When it comes to graduate-level reasoning, the two models are essentially tied. Opus 4.7 scores 94.2% on GPQA Diamond; GPT-5.5 scores 93.6%, which comes very close.

On Humanity's Last Exam, a multidisciplinary reasoning benchmark, Opus 4.7 scores 46.9% without tools and 54.7% with tools, while GPT-5.5 reaches 41.4% without tools and 52.2% with tools. While the gap isn't big with tool use, Opus 4.7 is leading by a significant margin of more than five percentage points over GPT-5.5 when it comes to reasoning without tools.

GPT-5.5 scores 84.4% (GPT-5.5 Pro even 90.1%) versus Opus 4.7's 79.3% on BrowseComp, which tests agentic web search. That's a real gap. If your workflows depend heavily on web research, GPT-5.5 has a clear advantage here.

Another area where GPT-5.5 takes the lead is mathematics. In both FrontierMath levels, the gap to Opus 4.7 is quite big:

For both levels, the Pro version manages to add a few percentage points on top of base GPT-5.5. Whether that justifies the six times higher price is another question. More on the pricing below.

Vision and multimodal capabilities

Opus 4.7 made vision one of its headline improvements, and the benchmark numbers back that up. It takes the top spot on the CharXiv Reasoning leaderboard, which tests visual reasoning over scientific charts, scoring 82.1% without tools and 91.0% with tools.

The architectural change behind this is a three-fold increase in supported image resolution, up to 3.75MP (2576px). Higher-resolution images consume more tokens, so Anthropic recommends downsampling if you don't need the extra fidelity. The gain over Opus 4.6 is substantial: 69.1% to 82.1% without tools, a 13-point jump.

Our Claude Opus 4.7 API Tutorial shows you how to use those capabilities to build a chart-digitizer, which is definitely worth checking out.

GPT-5.5 doesn't have published CharXiv scores in the research notes, so a direct comparison isn't possible here. What I can say is that if vision tasks are central to your workflow, Opus 4.7 has a documented, large improvement and a clear architectural reason for it. GPT-5.5's vision capabilities may be comparable, but the evidence isn't on the table yet.

Tool use and computer interaction

Opus 4.7 leads on MCP-Atlas, which measures multi-tool workflow orchestration, with 77.3% versus GPT-5.5's 75.3%. On OSWorld, which measures autonomous computer use, both models are essentially tied: Opus 4.7 scores 78.0% versus GPT-5.5's 78.7%.

Opus 4.7 also introduces task budgets in public beta on the API, which let you set a token spending cap per task. For production agentic workflows where cost predictability matters, this is a practical feature that GPT-5.5 doesn't have a direct equivalent for. Overall, GPT-5.5 is designed for similar long-running agentic loops, but the tool-use benchmark slightly favors Opus 4.7.

Pricing

Opus 4.7 is priced at $5 per million input tokens and $25 per million output tokens. Prompt caching cuts input costs by up to 90%, and standard caching saves 50%. These numbers are unchanged from Opus 4.6.

GPT-5.5 comes in at $5 per million input tokens and $30 per million output tokens, with batch and flex pricing available at half the standard rate and priority processing at 2.5x. GPT-5.5 Pro, designed for the most demanding tasks where accuracy matters most, jumps to $30 input / $180 output per million tokens, making it 6x more expensive than base GPT-5.5.

Based on the benchmark results, using GPT-5.5 Pro and paying the price associated with it seems to be only worth it for workflows that include difficult math and/or web search tasks, and where high accuracy matters. For example, that could mean financial modeling pipelines that need precise numerical reasoning, or automated research agents that synthesize answers from dozens of live sources.

On output tokens, where agentic workloads rack up cost, GPT-5.5 is 20% more expensive than Opus 4.7 at standard rates. The gap widens dramatically at the Pro tier. That said, Anthropic ships a new tokenizer with Opus 4.7 that makes direct per-token comparisons with Opus 4.6 tricky. According to Artificial Analysis, Opus 4.7 uses roughly 35% fewer output tokens than Opus 4.6 to run their Intelligence Index, which partially offsets the per-token rate. 

Long-context performance

Both models support a 1M token context window. The more interesting question is whether they can actually use it.

In our GPT-5.5 testing, we fed the model Berkshire Hathaway's FY2025 and FY2024 10-K filings stacked together, totaling just under 300K tokens of real financial text. GPT-5.5 passed that test (in contrast to GPT-5.4, which often visibly degraded past 128K tokens). On MRCR needle tests and Graphwalks reasoning tests, GPT-5.5 showed consistent performance across context sizes where GPT-5.4 fell apart.

Opus 4.7's 1M context window is paired with improved file-system memory, which lets the model write notes to itself across sessions and recall them reliably. These are complementary approaches: GPT-5.5 is better at reasoning over a single massive context, while Opus 4.7 is better at maintaining coherence across multiple sessions using structured memory. Which matters more depends on your workflow.

Still, in our Opus 4.7 benchmark tutorial, we found that users need to be careful when combining several new features: when using the persisted self-critique of the model to feed to the next task, it helped at max effort level, but consumed the budget needed to complete the task for high and xhigh effort levels.

When to Choose GPT-5.5 vs Claude Opus 4.7

What does that mean for your use cases? Here's a quick decision guide:

When to choose GPT-5.5

GPT-5.5 has clearer edges in terminal workflows, web search, mathematics, and long-context reasoning. It's also the natural choice if you're already deep in the OpenAI ecosystem via ChatGPT or Codex. Choose it for:

• Terminal-heavy DevOps and infrastructure work. GPT-5.5 scores 82.7% on Terminal-Bench 2.0 versus Opus 4.7's 69.4%. That's the largest gap in this entire comparison, in either direction.
• Long-context document analysis over single massive inputs. GPT-5.5 is the first OpenAI model where the full 1M context window is genuinely usable, and our 300K-token test confirmed it holds up where GPT-5.4 didn't.
• Web-research-heavy workflows. GPT-5.5 scores 84.4% on BrowseComp versus Opus 4.7's 79.3%, and GPT-5.5 Pro pushes that to 90.1%.
• Mathematics-heavy reasoning. GPT-5.5 leads on both FrontierMath tiers, with the gap widening sharply on the hardest problems (35.4% vs 22.9% on Tier 4). For workflows where numerical precision is non-negotiable, this matters.

When to choose Claude Opus 4.7

Opus 4.7 confirms the Claude Opus model family's status as the number one coding LLM. The upgrade in visual capabilities makes it a good choice for multimodal use cases as well. Use Claude Opus 4.7 for:

• Long, agentic coding sessions without close supervision. Opus 4.7's self-verification and xhigh effort level are designed for exactly this, and the SWE-bench Pro lead is the largest single-benchmark gap in the comparison.
• Pipelines working with high-resolution charts, technical diagrams, or financial documents. The 13-point CharXiv gain over Opus 4.6 is the biggest improvement in this release.
• Predictable costs on high-volume agentic runs. Published per-token pricing plus task budgets makes Opus 4.7 much easier to budget for.
• Multi-tool orchestration across complex workflows. Opus 4.7 tops the MCP-Atlas benchmark at 77.3%, confirming it handles chained tool calls more reliably than any other model tested.

Final Thoughts

On the benchmarks available right now, Claude Opus 4.7 is the stronger choice for most agentic coding and tool-use workflows. The SWE-bench Pro gap (64.3% vs 58.6%), the MCP-Atlas lead (77.3% vs 75.3%), and the CharXiv vision advantage (82.1% with no GPT-5.5 score reported) are consistent across different task types, not a single-benchmark fluke. If your work is primarily software engineering, multi-tool orchestration, or visual reasoning, Opus 4.7 is where I'd start.

GPT-5.5 has real advantages in terminal workflows, mathematics, web search, and long-context reasoning. The Terminal-Bench 2.0 gap (82.7% vs 69.4%) is the largest single advantage in either direction across this entire comparison. The BrowseComp lead (84.4% vs 79.3%, or 90.1% with Pro) and the FrontierMath margins, especially on Tier 4 (35.4% vs 22.9%), are substantial. If your workflows are terminal-heavy, math-intensive, research-driven, or depend on reasoning over single massive documents, GPT-5.5 is worth serious consideration.

Opus 4.7 is 20% cheaper on output tokens at standard rates ($25 vs $30 per million), and the gap widens dramatically if you need GPT-5.5 Pro (which is not worth the high rate for over 90% of use cases, if you ask me). The 35% output token reduction Anthropic reports for Opus 4.7 versus Opus 4.6 also means the effective cost is lower than the per-token rate suggests. For production systems where cost predictability matters as much as raw performance, Opus 4.7's task budgets add another layer of control that GPT-5.5 doesn't yet match.

To get up to speed with agentic AI more broadly, I recommend enrolling in our AI Agent Fundamentals skill track as a good place to start.