Claude Opus 4.8 vs GPT-5.5: Benchmarks, Tests, and Which to Choose

If you're picking a flagship model for serious agentic work right now, Claude Opus 4.8 and GPT-5.5 are clearly two of the top choices, alongside Gemini 3.5 Flash. Both are the current production ceilings from their respective labs, and both target long-horizon coding and autonomous workflows.

The headline numbers are close enough that the decision isn't obvious from benchmarks alone. Opus 4.8 leads on SWE-bench Pro (69.2% vs 58.6%) while GPT-5.5 leads on Terminal-Bench 2.0 (82.7% vs 74.6%). The more interesting story is qualitative: Anthropic is betting that honesty and calibrated uncertainty are the next frontier for production AI, while OpenAI is betting on raw agentic throughput and token efficiency.

In this article, I'll compare Claude Opus 4.8 and GPT-5.5 across five dimensions: coding and agentic workflows, reasoning and knowledge tasks, long-context performance, alignment and reliability, and pricing. You can also check out our standalone coverage of Claude Opus 4.8 and GPT-5.5 for deeper dives into each model individually.

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What Is Claude Opus 4.8?

Claude Opus 4.8 is Anthropic's current flagship model, released on May 28, 2026. It sits at the top of the Claude family above Sonnet and Haiku, and is designed for the most demanding tasks: agentic coding, complex multi-step reasoning, and long-running autonomous workflows. The headline improvement over Opus 4.7 is not just benchmark scores but a qualitative shift toward honesty: the model is four times less likely than its predecessor to let flawed code pass without flagging it.

Opus 4.8 also ships with a batch of new features, including dynamic workflows in Claude Code (which can run hundreds of parallel subagents in a single session), effort controls in claude.ai, and a fast mode that now costs one-third of what it did for previous Opus models. Pricing for standard usage is $5 per million input tokens and $25 per million output tokens, unchanged from Opus 4.7.

What Is GPT-5.5?

GPT-5.5 is OpenAI's April 2026 flagship, described by the company as its strongest agentic coding model to date. It's available in ChatGPT and Codex for Plus, Pro, Business, and Enterprise users, with a 1M context window in Codex. OpenAI's headline claim is that GPT-5.5 matches GPT-5.4 per-token latency in real-world serving while performing at a meaningfully higher intelligence level, and uses fewer tokens to complete the same Codex tasks.

A GPT-5.5 Pro variant is also available for higher-accuracy work, priced at $30 per million input tokens and $180 per million output tokens in the API. Standard GPT-5.5 API pricing is $5 per million input tokens and $30 per million output tokens.

Claude Opus 4.8 vs GPT-5.5: Head-to-Head Comparison

Here's a quick summary of where each model stands before we get into the details. The picture splits by domain, so the right choice depends heavily on what you're actually building.

Coding and agentic workflows

This is the dimension where the two models diverge most clearly, and the split is by environment rather than by overall quality. On SWE-bench Pro, which uses real actively-maintained repositories with no public ground-truth leakage, Opus 4.8 scores 69.2% versus GPT-5.5's 58.6%. That's a 10.6-point gap in favor of Opus 4.8 for repository-level software engineering.

The picture reverses on Terminal-Bench 2.0, where GPT-5.5 scores 78.2% versus Opus 4.8's 74.6%. Terminal-Bench tests complex command-line workflows requiring planning, iteration, and tool coordination, so if your work is shell-heavy or DevOps-oriented, GPT-5.5 has an edge. One detail worth noting from the Anthropic system card: at minimum effort, Opus 4.8 already matches the peak performance of Opus 4.7 at maximum effort on SWE-bench Pro, which says something about how much headroom the effort controls give you.

The coding picture splits cleanly: Opus 4.8 for repository-level engineering, where understanding a codebase's structure matters, GPT-5.5 for terminal-heavy workflows and shell automation. If you're running Claude Code with dynamic workflows, Opus 4.8 can now orchestrate hundreds of parallel subagents in a single session, which is a different capability class than what either model's raw benchmark scores capture.

Reasoning and knowledge tasks

On Humanity's Last Exam, a benchmark of genuinely hard graduate-level questions across science, mathematics, and humanities, Opus 4.8 leads both with and without tools. Without tools: 49.8% for Opus 4.8 versus 41.4% for GPT-5.5. With tools: 57.9% versus 52.2%. That's a consistent 7-8 point gap in favor of Opus 4.8 on multidisciplinary reasoning.

The math story is particularly striking. On the USA Mathematical Olympiad, Opus 4.8 scored 96.7% on this year's competition, which took place after the model's training data cutoff, ruling out contamination. Opus 4.7 scored 69.3% on the same problems. That's a 27-point jump on proof-based math in a single model generation. GPT-5.5 scores 51.7% on FrontierMath Tier 1-3 and 35.4% on Tier 4, which are strong results, but the USAMO comparison isn't directly available for GPT-5.5 in the research notes.

Anthropic hasn't published a GPQA Diamond score for Opus 4.8 specifically, likely because it is very saturated at this point, and the results are not as relevant as those from other benchmarks.

It's noteworthy that both models trail Gemini 3.5 Flash (57.9%) when it comes to financial knowledge work, as measured in the Finance Agent v2 benchmark (53.9% and 51.8%, respectively).

Tool use and computer interaction

Opus 4.8 leads on both major tool use and computer use benchmarks. On OSWorld-Verified, which tests a model's ability to complete tasks by controlling a live desktop with a mouse and keyboard, Opus 4.8 scores 83.4% versus GPT-5.5's 78.7%. On MCP-Atlas, which measures multi-step tool use across real APIs, Opus 4.8 reaches 82.2% versus GPT-5.5's 75.3%.

The OSWorld gap is notable because Opus 4.7 and GPT-5.5 were essentially tied on this benchmark (78.0% vs 78.7%). Opus 4.8 has pulled ahead by about five points, which is a meaningful improvement for teams building browser agents or desktop automation. Early testers reported that Opus 4.8 scored 84% on Online-Mind2Web, a web agent benchmark, which is a jump over both Opus 4.7 and GPT-5.5.

One caveat on agentic performance: Anthropic's system card flagged a regression in prompt injection resistance. Without safeguards, a single attack attempt succeeded against Opus 4.8 about 7% of the time, versus 2.3% for Opus 4.7. Deployed safeguards bring this back to 2%, but if you're building agentic pipelines that process untrusted input, this is worth knowing before you switch.

Long-context performance

This is where Opus 4.8 has the clearest lead. On GraphWalks, which stress-tests long-context reasoning by embedding a large directed graph in the context window and asking the model to traverse it, Opus 4.8 scores 85.9% on the 256K BFS subset versus GPT-5.5's 73.7%. At the full 1M token subset, the gap widens: 68.1% for Opus 4.8 versus 45.4% for GPT-5.5.

As we noted in our GPT-5.5 review, GPT-5.4 essentially fell apart past 128K tokens, and GPT-5.5 fixed that. But Opus 4.8 is still substantially ahead at the 1M end. For document-heavy workflows, dense financial filings, or any task that requires reasoning across a very large context, Opus 4.8 is the stronger choice by a wide margin.

Alignment, honesty, and reliability

This is the dimension that Anthropic is most explicitly competing on with Opus 4.8, and the results are genuinely interesting. In a test where the model summarizes a coding session that secretly contained failures, Opus 4.8 glosses over those failures only 3.7% of the time. It's also the first Claude model to score zero on a test where it must catch flawed data before reporting a result.

Anthropic's alignment team also found that Opus 4.8 has rates of misaligned behavior substantially lower than Opus 4.7, and similar to Claude Mythos Preview, which is Anthropic's most capable and most carefully aligned model. There's a caveat worth flagging: during training, Opus 4.8 sometimes appeared to reason about how it would be graded rather than how to complete the task. Anthropic says the behavioral impact is modest, but it's the kind of thing that could matter in high-stakes agentic deployments.

OpenAI hasn't published equivalent alignment metrics for GPT-5.5 in the research notes available here, so a direct comparison on this dimension isn't possible. What we can say is that Anthropic is making honesty and calibrated uncertainty a priority, although the recent results are mixed.

Pricing

At the standard API tier, the two models are close but not identical. Both charge $5 per million input tokens. On output, Opus 4.8 is $25 per million tokens versus GPT-5.5's $30 per million tokens, a 17% difference that adds up quickly on output-heavy workloads.

Opus 4.8 also has a fast mode that runs at 2.5x the speed, priced at $10 per million input tokens and $50 per million output tokens. Anthropic cut the fast mode price to one-third of what it was for previous Opus models, which makes it a more practical option for latency-sensitive workflows.

GPT-5.5 Pro, for higher-accuracy work, is priced at $30 per million input tokens and $180 per million output tokens, which is a significant premium over standard GPT-5.5.

When to Choose Claude Opus 4.8 vs GPT-5.5

The decision isn't about which model is better overall. It's about which one fits the specific shape of your work. Here's how I'd frame it.

Choose Claude Opus 4.8 if...

• Your work is repository-level software engineering. The 10-point SWE-bench Pro gap is a real signal, and our own code review tests confirmed that Opus 4.8 catches subtle bugs without prompting for them.
• You're building agentic pipelines that process long documents or large codebases. The GraphWalks 1M gap (68.1% vs 45.4%) is the largest performance difference between the two models on any benchmark.
• You need a model that flags its own uncertainty. Opus 4.8's honesty improvements matter most in unattended agentic runs where you can't supervise every step.
• You're running browser agents or desktop automation. Opus 4.8 leads OSWorld-Verified by about five points over GPT-5.5, and early testers reported 84% on Online-Mind2Web.
• Output token cost matters at scale. At $25 per million output tokens versus $30 for GPT-5.5, the difference compounds quickly on high-volume workloads.

Choose GPT-5.5 if...

• Your work is terminal-heavy. GPT-5.5 leads Terminal-Bench 2.0 by eight points (82.7% vs 74.6%), and that gap is consistent with what we saw in our GPT-5.5 testing.
• You need a Pro tier for the hardest tasks. GPT-5.5 Pro is available at $30 per million input tokens and $180 per million output tokens for higher-accuracy work. Opus 4.8 has no equivalent tiered variant.
• You're already deep in the OpenAI ecosystem. GPT-5.5 integrates with Codex, ChatGPT, and the broader OpenAI toolchain, which has a larger community and more integration examples than Anthropic's ecosystem.
• You're doing scientific research workflows. GPT-5.5 showed strong results on GeneBench (25.0%) and BixBench (80.5%), and OpenAI has positioned it explicitly as a co-scientist for biomedical research.

Final Thoughts

Opus 4.8 is the stronger model for most of the tasks that matter most to data scientists and ML engineers: repository-level coding, long-context reasoning, multi-step tool use, and agentic workflows that need to run unattended. The honesty improvements are the part I find most interesting, because a model that tells you when it's stuck is more useful in production than one that confidently reports success. Whether this holds up in practice is to be seen, but the direction seems promising.

GPT-5.5 is the right call for terminal-heavy work and for teams already invested in the OpenAI ecosystem. The Terminal-Bench gap is real, and GPT-5.5 Pro gives you a higher-accuracy option that Opus 4.8 doesn't currently match with a tiered variant.

One thing worth watching: Anthropic kept mentioning Claude Mythos Preview throughout the Opus 4.8 announcement, describing it as their best-aligned model and noting it's already in limited use for cybersecurity work. Opus 4.8 may not be the ceiling for long. If you're keen to get up to speed with the basics of AI and how to work with these models in practice, I'd recommend starting with the AI Fundamentals skill track on DataCamp.