I Tested GPT-5.5 vs Claude Opus 4.7

Just when I was about to crown Claude Opus 4.7 as the best coding model, OpenAI released GPT-5.5, and I immediately upgraded my plan to test it.

It turns out the model wars are not stopping any time soon, and neither are my reviews. If you have been following my content across the different platforms, I have finally come home, and I am writing on my blog daily.

Two flagship models, seven days apart — Anthropic released Opus 4.7 on April 16, and OpenAI followed with GPT-5.5 on April 23.

Both companies claim their model is the best for coding and agentic work, and both are targeting the same audience: developers and teams building serious AI-powered workflows.

I have been running both through real coding tasks to see where each one actually holds up, since benchmarks from the labs only tell part of the story.

In this article, I will break down what each model brings, where the benchmarks show a clear winner, what the pricing really costs you in practice, and which one you should be using depending on your workflow.

 

 

GPT 5.5 vs. Opus 4.7: What Each Model Brings

Seven days apart is a tight window, and both releases are substantial. Here is what changed on each side.

Claude Opus 4.7 — Anthropic’s Upgrade

Anthropic released Opus 4.7 on April 16, 2026, positioning it as their most capable generally available model for complex reasoning and agentic coding.

The headline changes:

• xhigh effort level — a new tier between high and max, now the default for many Claude Code workflows
• Task budgets — you set a token budget for long-running agents so they cannot silently burn through your quota mid-task
• /ultrareview in Claude Code — a slash command for deep multi-agent code review
• High-resolution vision — image support increased to 2,576px / 3.75MP, up from 1,568px / 1.15MP
• New tokenizer — improves performance across tasks but uses up to 35% more tokens on the same input compared to Opus 4.6
• 1M token context window — carried over from Opus 4.6, now standard with no beta header required
• 128K output tokens — same as Opus 4.6
• Adaptive thinking — on by default, replaces the old extended thinking toggle

Pricing stays at $5/$25 per million input/output tokens, same as Opus 4.6. The tokenizer change is the hidden cost story, and I will cover that properly in the cost section.

GPT-5.5 — OpenAI’s Retraining

OpenAI released GPT-5.5 on April 23, 2026. Unlike the GPT-5.1 through 5.4 releases which were incremental, GPT-5.5 is the first fully retrained base model since GPT-4.5.

The headline changes:

• Natively omnimodal — text, images, audio, and video processed in a single unified system
• Agentic-first architecture — the model was retrained with autonomous multi-tool workflows as a primary design goal
• Token efficiency — uses significantly fewer output tokens to complete the same tasks compared to GPT-5.4 and Claude Opus 4.7
• 1M token context window — matches Opus 4.7 on paper, but long-context retrieval performance differs significantly
• GPT-5.5 Thinking mode — available to all paying users
• GPT-5.5 Pro — rolling out to Pro, Business, and Enterprise users in ChatGPT only

Pricing is $5/$30 per million input/output tokens. Output is $5 more per million than Opus 4.7 at list price, though the token efficiency gap changes the real cost calculation considerably.

API rollout is still in progress at the time of writing. Day-one availability tilts toward Anthropic — Opus 4.7 launched across the API, Amazon Bedrock, Google Cloud Vertex AI, and Microsoft Foundry from day one. GPT-5.5 is live in ChatGPT and Codex, with the API following shortly.

Coding Tests

Benchmarks from the labs are a starting point, not a verdict. Both OpenAI and Anthropic report numbers under conditions that do not always match real production environments. That said, the pattern across these results is consistent enough to draw clear conclusions.

Here is where each model wins and what it means for your work.

1) Real-World Code Resolution — Opus 4.7 Wins

On SWE-Bench Pro, which tests actual GitHub issue resolution — reading an issue, understanding existing code, and submitting a fix that passes tests — Opus 4.7 scores 64.3% against GPT-5.5’s 58.6%.

This is the benchmark most closely aligned with how developers actually work day to day. A 5.7-point gap on this evaluation is meaningful, and Opus 4.7 has the production track record to back it up, particularly for Cursor users and MCP-heavy workflows.

2) Terminal and Agentic Workflows — GPT-5.5 Wins

On Terminal-Bench 2.0, which tests complex command-line workflows requiring planning, iteration, and tool coordination, GPT-5.5 scores 82.7% against 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. If you are running autonomous agents that execute multi-step terminal tasks, this difference shows up in real work.

3) Long-Context Retrieval — GPT-5.5 Wins by a Wide Margin

Both models ship with 1M token context windows. The headline is at parity. The actual retrieval performance is not.

On OpenAI’s MRCR v2 8-needle benchmark at the 512K–1M token range, GPT-5.5 scores 74.0% against Opus 4.7’s 32.2%. At the 128K–256K range, the gap is 87.5% to 59.2%.

If you are reasoning over entire codebases, large policy documents, or long agent traces in a single pass, that 41-point gap at the upper end of the context window changes architecture decisions.

One caveat worth noting: Anthropic’s new tokenizer in Opus 4.7 uses up to 1.35x more tokens than Opus 4.6 on the same input, so Opus 4.7 at 1M tokens holds slightly less raw information than the number suggests.

4) Abstract Reasoning and Math — GPT-5.5 Wins

On ARC-AGI-2, a verified benchmark for novel reasoning, GPT-5.5 scores 85.0% against Opus 4.7’s 75.8%.

On FrontierMath Tier 4, the hardest public math evaluation available, GPT-5.5 scores 35.4% against Opus 4.7’s 22.9%. These are consistent margins across multiple hard reasoning tasks.

5) Large Codebase Refactoring — Opus 4.7 Wins

On MCP-Atlas and CursorBench, which measure performance on large pull request refactors and MCP-heavy workflows, Opus 4.7 holds a clear lead. Anthropic explicitly positions Opus 4.7 for this use case, and the benchmark numbers match the positioning.

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Performance Summary

Neither model dominates across the board, and that is actually the most useful finding.

GPT-5.5 leads on planning-and-execution tasks — terminal automation, long-context retrieval, abstract reasoning, and new feature work from scratch. Opus 4.7 leads on codebase-resolution tasks — GitHub issue fixes, large PR refactors, and tool-heavy agent workflows where instruction-following consistency matters more than raw benchmark scores.

The best production setups route tasks based on this split rather than committing everything to one model.

Cost Analysis

The rate cards look almost identical. $5 per million input tokens for both models. The difference shows up on output — $30 per million for GPT-5.5 versus $25 for Opus 4.7.

At first glance, Opus 4.7 looks cheaper. In practice, it is not always that simple.

Token Efficiency Gap

GPT-5.5 uses roughly 72% fewer output tokens than Opus 4.7 to complete the same coding tasks. OpenAI retrained the model with output conciseness as a design goal, and the difference is structural, not marginal.

What that means in real numbers: if Opus 4.7 generates 1,000 output tokens to complete a task, GPT-5.5 completes the same task in roughly 280 tokens. At scale, across thousands of API calls daily, that gap compounds fast.

Output tokens are priced 5x higher than input tokens on both platforms. So the model that generates fewer output tokens wins on cost per task even if the per-token rate is higher.

Opus 4.7 Tokenizer Trap

Anthropic shipped a new tokenizer with Opus 4.7 that improves performance but uses up to 35% more tokens on the same input text compared to Opus 4.6.

Your rate card did not change. Your bill might.

A prompt that costs you X on Opus 4.6 can cost up to 1.35X on Opus 4.7 for the input alone, before the model generates a single output token. For teams running high-volume pipelines, this is the kind of change that shows up quietly on next month’s invoice.

The safe move before migrating a production workload is to run your actual prompts through /v1/messages/count_tokens on both models and measure the real delta on your traffic, not the ceiling estimate.

Long-Context Pricing Layer

Both models add a cost layer for long-context usage. Opus 4.7 charges a 2x surcharge on input tokens above 200K, bringing the effective input rate to $10 per million at that range.

If you are regularly pushing past 200K tokens — full codebases, large documentation sets, long agent traces — factor that into the comparison alongside the tokenizer change.

For solo developers and small teams running occasional tasks, the difference between the two models is unlikely to move your bill significantly either way. Pick based on performance for your use case.

For teams running autonomous coding agents at volume, the token efficiency gap is the deciding factor. GPT-5.5’s lower output token usage offsets its higher per-token output rate on most workloads, and the savings compound with every agent loop.

The most cost-efficient production setups use both — GPT-5.5 for high-volume agentic tasks where token efficiency compounds, Opus 4.7 for precision coding work where quality justifies the cost.

Use Cases

Seven days apart, two strong models, and no clear winner across the board — which is actually the most honest conclusion I can give you.

Here is how I would route based on what you actually do.

GPT-5.5

• Run autonomous coding agents that execute multi-step terminal workflows
• Process large codebases, documents, or agent traces above 500K tokens in a single pass
• Need the model to plan and execute new feature work from scratch
• Are you running high-volume API pipelines where token efficiency compounds into real savings
• Work on abstract reasoning, hard math, or cybersecurity tasks

Claude

• Resolve real GitHub issues and submit fixes against existing codebases
• Work heavily in Cursor or MCP-heavy workflows
• Need precise instruction-following across complex, multi-step tasks
• Do financial analysis, dense document work, or high-resolution image processing
• Want day-one availability across AWS Bedrock, Google Vertex AI, and Microsoft Foundry

My Take

I came into this test expecting one clear answer and did not get one, which tells me both labs are doing something right.

Opus 4.7 is the better model for the kind of coding work most developers do daily — reading existing code, resolving issues, following precise instructions across a long task. If you are using Claude Code or Cursor heavily, the xhigh effort level and task budgets alone make the upgrade from 4.6 worth it.

GPT-5.5 wins on the agentic side. The Terminal-Bench gap is too large to ignore; the long-context retrieval performance at the upper end of the window is significantly stronger, and the token efficiency advantage is real money at scale.

If you can only pick one, base it on your primary workflow. If you are building production AI systems, the routing approach — GPT-5.5 for agents and long-context tasks, Opus 4.7 for code resolution and precision work — is where the serious teams are heading.

I will be publishing a full hands-on coding test of both models shortly, running identical tasks across real projects.

Have you tested either model yet? Let me know in the comments what you are seeing in your workflows.

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