GPT-5.5 vs Claude Opus 4.7: Every Benchmark, One Clear Winner for Agentic Work

Every few weeks, a new frontier model drops and the same question surfaces: which one should we actually build on? With OpenAI releasing GPT-5.5 on April 23, 2026, just six weeks after GPT-5.4, the comparison that matters most right now is GPT-5.5 against Anthropic's Claude Opus 4.7. Both models claim state-of-the-art performance. Both are designed for agentic work. And the benchmark data tells a more complicated story than either company's blog post suggests.

We went through every published evaluation, cross-referenced the numbers, and mapped out where each model actually leads. If you're running AI agents in production or deciding which model to route tasks through, this is the breakdown that matters.

The headline numbers

GPT-5.5 is the first fully retrained base model OpenAI has shipped since GPT-4.5. The architecture, pretraining data, and training objectives were all reworked with agentic workflows as a primary design goal. It ships at $5 per million input tokens and $30 per million output tokens, double what GPT-5.4 cost. Claude Opus 4.7, Anthropic's current flagship, has been the default choice for many enterprise agent deployments since its release.

Both models compete across coding, knowledge work, tool use, scientific reasoning, and long-context tasks. Neither one wins everywhere.

Where GPT-5.5 pulls ahead

GPT-5.5's strongest gains show up in three areas: agentic coding, long-context processing, and abstract reasoning.

Coding and engineering tasks. On Terminal-Bench 2.0, which tests complex command-line workflows that require planning, iteration, and tool coordination, GPT-5.5 scores 82.7% compared to Claude Opus 4.7's 69.4%. That is a 13-point gap on a benchmark designed to test exactly the kind of work agentic coding tools depend on. On Expert-SWE, OpenAI's internal eval for coding tasks with a median human completion time of 20 hours, GPT-5.5 hits 73.1% (Claude Opus 4.7 has no published score on this eval). Dan Shipper, CEO of Every, called it "the first coding model I've used that has serious conceptual clarity."

Long-context performance. This is where GPT-5.5 creates the widest separation. On OpenAI's MRCR v2 8-needle test at the 512K-1M token range, GPT-5.5 scores 74.0% versus Claude Opus 4.7's 32.2%. At the 128K-256K range, the gap is 87.5% to 59.2%. For enterprise workflows that require processing large codebases, legal documents, or financial reports in a single pass, this difference is not trivial.

Abstract reasoning. On ARC-AGI-2, a verified benchmark for novel reasoning, GPT-5.5 scores 85.0% against Claude Opus 4.7's 75.8%. On FrontierMath Tier 4, OpenAI's model scores 35.4% versus 22.9%. These are the hardest math problems in public evaluation, and GPT-5.5 holds a consistent edge.

Cybersecurity. On CyberGym, GPT-5.5 scores 81.8% versus Claude Opus 4.7's 73.1%. OpenAI has classified GPT-5.5's cybersecurity capabilities as "High" under their Preparedness Framework, an incremental step up from GPT-5.4.

Where Claude Opus 4.7 holds its ground

Claude Opus 4.7 is not the runner-up across the board. It wins or ties on several evaluations that matter for production agent deployments.

Real-world code resolution. On SWE-Bench Pro, which evaluates actual GitHub issue resolution, Claude Opus 4.7 scores 64.3% compared to GPT-5.5's 58.6%. This is a meaningful gap on the benchmark most closely aligned with how developers actually work: reading issue descriptions, understanding existing code, and submitting fixes that pass tests.

Tool integration. On MCP Atlas (Scale AI's April 2026 update), Claude Opus 4.7 scores 79.1% versus GPT-5.5's 75.3%. For multi-model orchestration systems where agents need to call external tools, APIs, and services reliably, Opus 4.7 remains the stronger choice.

Finance and professional reasoning. On FinanceAgent v1.1, Claude Opus 4.7 scores 64.4% versus GPT-5.5's 60.0%. On Humanity's Last Exam (both with and without tools), Opus 4.7 also leads: 46.9% vs 41.4% without tools, and 54.7% vs 52.2% with tools.

Academic reasoning. On GPQA Diamond, the two models are essentially tied at 94.2% and 93.6% respectively. On Humanity's Last Exam, Claude Opus 4.7 maintains a slight but consistent advantage.

The efficiency argument

OpenAI's pricing decision is worth examining. GPT-5.5 costs twice as much per token as GPT-5.4 ($5/$30 versus $2.50/$15). That is the largest single-release price increase in the GPT-5.x series. But OpenAI argues that GPT-5.5 uses significantly fewer tokens to complete the same tasks, making the effective cost comparable or lower.

On Artificial Analysis's Coding Index, OpenAI claims GPT-5.5 delivers state-of-the-art intelligence at half the cost of competitive frontier coding models. The reasoning: a model that solves a problem in one pass instead of three is cheaper even at a higher per-token rate.

For enterprise AI agent platforms processing thousands of tasks daily, this math matters. A 2x token price with a 60% reduction in tokens consumed per task translates to a net cost decrease. But this depends heavily on the specific workload. Short, simple tasks where token counts are already low will just cost more.

What the benchmarks miss

Benchmark comparisons are useful but incomplete. The numbers above do not capture several factors that determine which model performs better in production:

Instruction following and tone. Claude Opus 4.7 has a reputation for more consistent instruction adherence, especially in tasks requiring a specific voice, format, or style. This does not show up in most evals.

Refusal behavior. GPT-5.5 ships with stricter cybersecurity classifiers that OpenAI acknowledges "some users may find annoying initially." For legitimate security work, OpenAI is offering a Trusted Access for Cyber program. Claude Opus 4.7 has its own refusal patterns, but they differ in shape and frequency.

Latency in practice. OpenAI states GPT-5.5 matches GPT-5.4's per-token latency. But real-world latency depends on load, region, and API tier. Anthropic's serving infrastructure has its own performance characteristics. Neither set of benchmarks captures the experience of waiting for a response at 2pm on a Tuesday when everyone else is also sending requests.

Multi-turn consistency. Both models can lose the thread over extended conversations. The benchmarks above are largely single-turn or short-horizon evaluations. Production agents that run 50-step workflows care about consistency across the full chain, not just the first response.

What this means for enterprise AI agents

The comparison above points to a clear conclusion: neither model is the right choice for everything. The case for model-agnostic agent platforms gets stronger with every release.

GPT-5.5 is the better pick for long-context coding tasks, complex mathematical reasoning, cybersecurity work, and workflows that require processing large documents in a single pass. Claude Opus 4.7 is the better pick for real-world code resolution, tool-heavy agent workflows, financial analysis, and tasks where instruction-following consistency matters more than raw intelligence scores.

The most capable production AI agent deployments already route different tasks to different models based on the specific requirements. A coding agent might use GPT-5.5 for architecture-level reasoning and Claude Opus 4.7 for the actual PR submission. A research agent might use GPT-5.5 for processing a 500-page document and Opus 4.7 for synthesizing the findings into a structured report.

The six-week gap between GPT-5.4 and GPT-5.5 tells you something about the pace of this race. Building your agent infrastructure around a single model is a bet that the current leader stays the leader. History suggests otherwise. The winning strategy is to build systems that can switch when the benchmarks shift, because they will shift again before the quarter is over.

Full benchmark comparison

For quick reference, here is the complete head-to-head across all published evaluations:

Final count: GPT-5.5 wins 11, Claude Opus 4.7 wins 5, Tied 2.

GPT-5.5 takes the majority. But the benchmarks Claude wins, specifically SWE-Bench Pro, MCP Atlas, and FinanceAgent, are among the most production-relevant evaluations on the list. Wins on paper do not always translate to wins in deployment.