April 2026 just had its most competitive week in AI. Anthropic dropped Claude Opus 4.7 on April 16, reclaiming the coding leaderboard, and OpenAI answered exactly one week later with GPT-5.5. Both models represent genuine step changes in what AI can do within a codebase. Picking the right one, though, depends entirely on what kind of coding work you’re actually doing. You’re not short on choices, and learning about the strengths and caveats of each model will help you make the right decision. Here’s a detailed GPT-5.5 vs. Claude Opus 4.7 comparison offering just that.
Understanding GPT-5.5 and its Competitor Claude Opus 4.7
GPT-5.5 is not just another incremental post-training update in the GPT-5 family. It is the first fully retrained base model OpenAI has shipped since GPT-4.5. Every release between them, GPT-5.1 through 5.4, was built on top of the same underlying architecture. GPT-5.5 reworks that foundation from scratch, with a new pretraining corpus and agent-oriented objectives baked in at the base level. OpenAI calls it “a new class of intelligence for real work,” and its internal codename, “Spud,” comes from the potato emoji the company used to tease the release on social media.
Claude Opus 4.7, meanwhile, is Anthropic’s most capable publicly available model as of April 2026. It launched as a focused upgrade over Opus 4.6, targeting the specific failure modes that frustrated developers on long-running agentic tasks: loop stalling, instruction drift, and output verification gaps. Anthropic says users can now “hand off their hardest coding work, the kind that previously needed close supervision, with confidence.” The model sits below Claude Mythos Preview in Anthropic’s internal hierarchy, but Mythos is limited to 12 founding partners and a handful of vetted critical-infrastructure organizations.
GPT-5.5 vs Claude Opus 4.7 – Technical Specs at a Glance
| </> | GPT-5.5 | Claude Opus 4.7 |
| Release Date | April 23, 2026 | April 16, 2026 |
| Context Window | 1M tokens | 1M tokens |
| Max Output Tokens | 400K (Codex CLI) | 128K |
| Architecture | Fully retrained base | Adaptive thinking |
| Omnimodal (audio/video) | Yes | No |
| Vision Resolution | Not specified | 3.75MP (2,576px) |
| API Input Pricing | $5.00 / MTok | $5.00 / MTok |
| API Output Pricing | $30.00 / MTok | $25.00 / MTok |
| Variants | Standard, Thinking, Pro | Single model, xhigh effort level |
GPT-5.5 vs Claude Opus 4.7 – Coding Benchmarks and Performance
This is where the comparison gets specific and where general impressions break down. The two models yield different evaluations, and understanding why matters more than reading a single headline number.
SWE-bench Pro is the benchmark most developers cite as the closest proxy to real-world GitHub issue resolution. It requires solving multi-language, multi-file tasks end-to-end with no hand-holding. Claude Opus 4.7 scores 64.3% on SWE-bench Pro, compared to GPT-5.5’s 58.6%. That 5.7-point gap is meaningful at this level of difficulty. Opus 4.7 also leads its predecessor Opus 4.6, which scored 53.4%, by a substantial margin, and it beats GPT-5.4’s 57.7% and Gemini 3.1 Pro’s 54.2%.
SWE-bench Verified, the 500 human-validated GitHub issue set, tells a similar story. Opus 4.7 scores 87.6% there, up from 80.8% on Opus 4.6. GPT-5.5 was not scored on this benchmark at launch.
Terminal-Bench 2.0 is where the tables turn. This benchmark tests command-line workflows that require planning, iteration, and tool coordination. GPT-5.5 scores 82.7%, which OpenAI describes as state-of-the-art. Anthropic’s Mythos Preview is the only model that comes close, at 82.0%. Claude Opus 4.7 does not publish a Terminal-Bench 2.0 score for direct comparison.
CursorBench puts Opus 4.7 at 70%, up from 58% on Opus 4.6. That is a 12-point improvement on one of the more practically useful IDE-context coding evaluations available.
The pattern is consistent across evaluations: Opus 4.7 wins on codebase resolution tasks that require deep multi-file context and self-verification. GPT-5.5 wins on planning-heavy, multi-tool agentic workflows that play out across long sequences of terminal commands.
Where Each Model Actually Wins for Developers
Claude Opus 4.7 Is Better At:
- Resolving real GitHub issues in large, multi-language codebases (SWE-bench Pro 64.3%)
- Self-verifying outputs before reporting results back, catching logical faults mid-task
- Multi-tool orchestration, with MCP-Atlas at 77.3% versus GPT-5.5’s 75.3%
- Reading screenshots, dashboards, and technical diagrams at 3.75MP resolution
- Running /ultrareview in Claude Code, which performs a multi-pass bug detection session beyond standard review
- Financial analysis and structured enterprise work, with Finance Agent v1.1 at 64.4% versus GPT-5.5’s 61.5%
- Agentic sessions that span hours, with improved loop resistance and file-system memory across multi-session work
GPT-5.5 Is Better At:
- Complex command-line workflows with planning and iteration (Terminal-Bench 2.0 at 82.7%)
- Token efficiency: GPT-5.5 matches GPT-5.4’s response speed while using fewer tokens per completed task
- Omnimodal tasks that require processing text, images, audio, and video in a single unified system
- Understanding system architecture holistically, with early testers reporting clearer visibility into why things fail and where fixes belong
- Knowledge work breadth, with GDPval at 84.9% and Tau2-bench Telecom at 98.0%
- Access through Codex, which over 85% of OpenAI’s own employees now use weekly across departments
GPT-5.5 vs Claude Opus 4.7 – Pricing Comparison
The sticker prices look similar until you dig into output token rates. Both models charge $5 per million input tokens. Claude Opus 4.7 charges $25 per million output tokens. GPT-5.5 charges $30 per million, which is a 20% premium on the output side.
For most coding workloads, output tokens dominate cost. A typical agentic task generates around 3,000 output tokens. At those numbers, Opus 4.7 comes out 17% cheaper per completed task at the same reasoning effort. OpenAI’s counter-argument is that GPT-5.5 burns fewer tokens per task because of improved efficiency, which can offset the higher rate in practice. Independent analysis by The Decoder estimated the net cost increase at roughly 20% once token efficiency is factored in.
There is one more wrinkle specific to Opus 4.7 migration. Anthropic’s updated tokenizer uses roughly 1x to 1.35x more tokens than Opus 4.6 depending on content type. Teams upgrading from Opus 4.6 should replay real production traffic before finalizing cost projections. The official list price is real, but effective cost per task depends on your specific prompts and content mix.
For teams on Anthropic’s subscription plans, Opus 4.7 is included at no extra cost on Claude Pro, Max, Team, and Enterprise. GPT-5.5 is available to Plus, Pro, Business, and Enterprise ChatGPT subscribers. The API pricing above applies to both only when accessed via API key.
GPT-5.5 vs Claude Opus 4.7 – New Developer Features
Both releases shipped meaningful tooling improvements alongside the model upgrades.
Claude Opus 4.7 introduced three features worth knowing:
- xhigh effort level: A new reasoning depth setting that sits between the existing high and max options. It gives developers finer control over the quality-speed-cost tradeoff without jumping straight to maximum token burn. Claude Code now defaults to xhigh for all subscriber plans.
- Task budgets (public beta): Developers can set a hard token ceiling on an agentic loop. The model sees a running countdown and finishes the task gracefully as the budget is consumed, rather than cutting off mid-way. This directly solves a production cost-control problem for teams running overnight autonomous coding agents.
- /ultrareview command: A multi-agent code review pass inside Claude Code that catches bugs and design flaws that standard single-pass review misses. Anthropic offered three free ultrareview sessions at launch.
GPT-5.5, on the other hand, introduced:
- Natively omnimodal architecture: Text, images, audio, and video processed in a single unified system rather than routed through separate specialized models.
- Token efficiency improvements: GPT-5.5 completes tasks using fewer tokens than GPT-5.4 while maintaining comparable response speed.
- Stronger cybersecurity safeguards: Stricter classifiers for potential cyber risk, building on safeguards first introduced with GPT-5.2 in December 2025.
Public Reception
Neither launch landed without friction. GPT-5.5 earned genuine respect for its Terminal-Bench performance and system-level conceptual clarity. Dan Shipper, CEO of Every, described it as the first coding model with “serious conceptual clarity,” citing a test where he replicated a complex engineer-authored rewrite using the model. GPT-5.4 could not replicate it. GPT-5.5 could. That is exactly the kind of concrete signal that earns developer trust.
The pricing, though, was an immediate friction point. At $30 per million output tokens, double what GPT-5.4 charges, several Reddit threads flagged that Plus users receiving 200 messages per week of GPT-5.5 effectively got a material downgrade in access, even if the model is more capable per call. Experienced practitioners also pushed back on the Terminal-Bench headline, pointing out that the lead over Anthropic’s Mythos Preview on the same evaluation is narrow, and that Terminal-Bench is not SWE-bench.
Claude Opus 4.7 received more uniform enthusiasm from developers, partly because it shipped at the same price as Opus 4.6. The SWE-bench Pro jumped from 53.4% to 64.3% in a single generation, which was the biggest figure cited across technical coverage. The BrowseComp regression, from 83.7% to 79.3%, received fair attention too. If your agents rely on web research, Gemini 3.1 Pro at 85.9% and GPT-5.4 Pro at 89.3% both outperform Opus 4.7 on that specific dimension.
One note worth flagging on hallucination data. An AA-Omniscience evaluation cited in community benchmark aggregation put GPT-5.5 (xhigh) at the highest recorded accuracy of 57% on that test while simultaneously reporting the highest hallucination rate at 86%. This has not been independently verified against Opus 4.7 on the same run, and single-benchmark hallucination figures without controlled conditions should be treated cautiously.
The Bottom Line
GPT-5.5 vs Claude Opus 4.7 for coding does not resolve to a single winner, because neither model dominates the other across every coding dimension. If your workflow is built around resolving complex, multi-file GitHub issues and running self-verifying agentic loops inside large codebases, Opus 4.7 is the stronger choice right now. Its SWE-bench Pro lead is real, and its MCP-Atlas tool-use score leads everything currently available.
If your workflow leans on terminal-heavy agentic pipelines, omnimodal input, and long-horizon task coordination through a CLI, GPT-5.5’s Terminal-Bench 2.0 performance and improved token efficiency make a genuine case. On pure cost, Opus 4.7 is 17% cheaper on output and ships no hidden tokenizer surprises for new users.
Or maybe, stop guessing and benchmark both against your actual production workload before you commit. Benchmark figures are just theory; the only decision that matters is how they perform against your specific codebase.
