Claude Opus 4.7 vs GPT-5.5: The Honest Flagship Breakdown

Seven days apart, same target

Claude Opus 4.7 shipped on April 16, 2026. GPT-5.5 followed on April 23. Both flagships arrived within a single week, both claimed significant gains on agentic coding, and both launched with 1M-token context windows at the same input price of $5 per million tokens. The timing made a direct comparison unavoidable.

Anthropic built Opus 4.7 on a foundation of cautious, inspectable reasoning. It is designed for complex long-horizon tasks where reliability matters more than speed - software engineering, financial analysis, multi-document research. It sits just below the internal-only Mythos Preview in Anthropic's lineup. Key gains over Opus 4.6 include a 10.9-point jump on SWE-bench Pro (from 53.4% to 64.3%), a three-fold improvement in visual resolution up to 3.75MP, and a new 'xhigh' reasoning effort level between high and max. Anthropic also launched Claude Design alongside it.

GPT-5.5 is OpenAI's efficiency-first flagship - the pitch is doing more with fewer tokens. OpenAI claims the model uses significantly fewer output tokens than GPT-5.4 to complete the same Codex tasks, which matters when token budgets compound at scale. It comes in three variants: standard, Thinking, and the premium Pro tier for demanding legal, scientific, and business workflows. GPT-5.5 Pro is roughly 6x more expensive per token than the base model. A lighter version, GPT-5.5 Instant, rolled out to all free users on May 5 as the new ChatGPT default, with OpenAI claiming it cut hallucinations by 52.5% over the previous default in internal evals.

The question isn't which is smarter. It's which one fits the specific work you're doing.

Where the benchmarks actually split

Source: Linkedin - Michelangelo D'Agostino

Across the 10 benchmarks both labs report, Opus 4.7 leads on six: GPQA, HLE with and without tools, SWE-bench Pro, MCP Atlas, and FinanceAgent v1.1. GPT-5.5 leads on four: Terminal-Bench 2.0 (82.7%), BrowseComp, OSWorld-Verified, and CyberGym. The pattern is readable - Opus 4.7 wins on reasoning-heavy and review-grade evaluations, GPT-5.5 wins on shell-driven tool execution and long-running computer-use tasks.

On SWE-bench - the most cited benchmark for agentic coding because it tests against real GitHub issues in production repositories - Opus 4.7 holds a meaningful edge. GPT-5.5 scores around 84%, which is strong, but Opus 4.7 leads particularly on multi-file refactoring and bug reproduction tasks that mirror actual engineering work.

The biggest single gap in this comparison is long-context retrieval. On OpenAI's MRCR v2 8-needle benchmark at 512K-1M tokens, GPT-5.5 scores 74.0% versus Opus 4.7's 32.2%. At 256K-512K tokens, 87.5% versus 59.2%. Both models advertise a 1M context window - but what they can reliably retrieve from deep inside that window is not the same thing. For workloads that routinely process full codebases, large policy documents, or multi-document research at 500K+ tokens, GPT-5.5 has a decisive retrieval advantage.

On math and graduate-level reasoning the two are essentially tied. GPT-5.5 Pro pushes FrontierMath Tier 1-3 to 52.4% - the best published numbers at this tier in April 2026 - and scored 81.2 on AIME 2025. Opus 4.7 holds a slight lead on applied reasoning benchmarks like GPQA. Neither gap is large enough to drive a model decision on its own.

The token efficiency trap

Source: Substack - Zvi Mowshowitz

Pricing is close but not identical. Both charge $5 per million input tokens. On output, Opus 4.7 is $25 per million and GPT-5.5 is $30 - Opus is 17% cheaper on output at equivalent token counts. That advantage flips above 200K-token prompts: Opus 4.7 doubles its rates to $10/$37.50, while GPT-5.5 holds flat. If your workload regularly crosses 200K tokens, GPT-5.5 is the more predictable cost.

The more significant number: GPT-5.5 uses 72% fewer output tokens than Opus 4.7 on the same coding tasks. In agentic coding, models run dozens or hundreds of steps per task. Each step generates output tokens that cost money and consume context window. A model generating 3x the tokens per step hits context limits faster, costs more per completed task, and runs slower. At scale, GPT-5.5's conciseness is a structural cost advantage even though its per-token output price is nominally higher.

Opus 4.7 also introduced a new tokenizer that uses 1.0 to 1.35x more tokens than Opus 4.6 depending on content type. Teams migrating from 4.6 should run real workload tests before assuming the list price holds - effective cost per task can run meaningfully higher than headline numbers suggest.

The coding matchup

Opus 4.7 is better at architectural reasoning across large codebases. It maintains context more reliably over long sessions, follows complex multi-step instructions more precisely, and produces more thorough documentation alongside code. The verbosity that makes it expensive is the same thing that makes it thorough - it reasons in a traceable, inspectable way. For reliability-critical, long-horizon engineering work where quality per step matters more than throughput, Opus 4.7 is the stronger model.

GPT-5.5 is faster, more token-efficient, and integrates more tightly with OpenAI's Codex environment. It holds an edge on tasks requiring precise tool use and file navigation, and the session length advantage from using fewer tokens per step makes it better for high-volume, interactive, or user-facing coding features. For teams already on the OpenAI stack where cost-per-task matters more than individual step depth, GPT-5.5 fits better.

One honest caveat: benchmark gaming is real. Both labs report numbers under conditions that don't always match production. SWE-bench is the closest proxy to real engineering work, but it still has limits. Running your actual task distribution against both models beats any published leaderboard.

The use-case breakdown

Source: Substack - Zvi Mowshowitz

Based on benchmark data and third-party comparisons from DataCamp, MindStudio, LLM Stats, and Digital Applied:

• Agentic coding on complex codebases: Opus 4.7. Leads on SWE-bench Pro, stronger architectural reasoning, better instruction adherence across long sessions.
• High-volume or cost-sensitive coding pipelines: GPT-5.5. 72% fewer output tokens per task means lower real cost at scale despite the higher per-token output price.
• Long-context retrieval above 256K tokens: GPT-5.5. A 41-point gap on MRCR at 512K-1M tokens is the largest single spread in this comparison. 1M context parity does not mean retrieval parity.
• Financial and enterprise knowledge work: Opus 4.7. Leads on FinanceAgent v1.1. Enterprise revenue data shows Anthropic gaining ground with large organizations.
• Computer use and shell-driven tool tasks: GPT-5.5. Leads on OSWorld-Verified and Terminal-Bench 2.0 (82.7%).
• Vision and document analysis: Opus 4.7. A 13-point jump on CharXiv visual reasoning to 82.1%, plus 3.75MP image resolution support.
• Math and scientific research: GPT-5.5 Pro leads FrontierMath; base models are essentially tied on GPQA and MATH-500.
• Speed and ecosystem breadth: GPT-5.5/ChatGPT. Faster throughput, tighter Codex integration, image generation, voice chat, and broader consumer surface area.

What the matchup actually tells you

Two flagships released within a week, priced identically on input, both targeting agentic AI. The benchmarks don't produce a clean winner - they produce a map of which model wins on which type of work.

Opus 4.7 is the choice when the task requires deep reasoning, sustained architectural context, precise instruction-following, and strong visual analysis - and you can tolerate higher token usage and slower throughput. It's built for hard, careful work.

GPT-5.5 is the choice when throughput, cost efficiency at scale, long-context retrieval above 500K tokens, and OpenAI ecosystem integration matter most. It does more with less per step, even if the reasoning is slightly less exhaustive.

The bigger picture: ChatGPT holds roughly 80% of consumer market share, but Anthropic's enterprise revenue surpassed OpenAI's in the first half of 2025. The two companies are winning in different places. Opus 4.7 and GPT-5.5 reflect that split exactly - one built for depth, one for breadth and reach.

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