Claude 5.5 Models Compared: Opus, Sonnet, and the GPT-6 Alternatives

Updated September 29, 2026.

Claude 5.5 gives you two choices today: Sonnet for defined everyday work and Opus when a task needs sustained judgment. Haiku 5.5 is announced but unreleased. The harder question is whether either available model fits your workflow better than GPT-6.

Following our GPT-6 model selection guide, this comparison separates official specifications from early user reports. Opus has been public for a week; Sonnet launched yesterday. Their reputations are still forming.

Claude 5.5 Pricing and Positioning at a Glance

These are standard API rates, not subscription prices. Anthropic’s pricing documentation lists cache reads at $0.20 per million tokens for both released models. Five-minute cache writes cost $5 for Opus and $2.50 for Sonnet; one-hour writes cost $8 and $4. Batch processing discounts input and output by 50%; fast mode and regional options have separate conditions.

Both released models accept text and images and generate text, with a 1M-token context window and a normal maximum output of 128K tokens. Their model specifications make clear that document capacity alone will not decide between them.

This guide focuses on the 5.5 family; it is not a complete catalog of every Claude model.

Sonnet or Opus: Start With the Shape of the Task

Start with Sonnet when you can describe success clearly. Examples include fixing a reproducible bug, turning approved notes into slides, or revising copy against a style guide. Anthropic positions it around scoped work and quick iteration in the Sonnet 5.5 announcement.

Test Opus when the model must help define the solution. Think unfamiliar codebases, conflicting requirements, or a report whose argument needs restructuring. The Opus documentation identifies long-running coding and knowledge work as its focus. My practical starting point would be Opus when repeated correction costs more than the token premium.

Treat Haiku as a forthcoming option. Anthropic says it will arrive in the coming weeks for high-volume, cost-sensitive applications. There is no released Haiku 5.5 to review here, and no basis for assigning it Luna’s price or performance.

What Claude 5.5 Benchmarks Actually Tell You

On FrontierCode 1.1 (Main), which assesses whether code changes are mergeable, Anthropic reports Sonnet 5.5 at 52.1% with Xhigh effort but 46.2% with Max. Its explanation: extra reviewing sometimes caused timeouts or changes beyond scope. These are vendor-published results, not our tests; see the evaluation and footnotes.

The useful lesson is to test reasoning effort—the setting controlling how much work the model does—alongside model choice. Turning it up is not an automatic improvement.

Where GPT-6 Fits Into the Comparison

OpenAI’s model catalog positions Astra for its hardest work, Sol for a balance of capability and cost, and Luna for focused high-volume tasks. That makes Opus–Astra and Sonnet–Sol useful comparisons by intended role, without implying equivalent capability.

At current standard API rates, Astra costs $10/$50 per million input/output tokens, Sol $2/$10, and Luna $0.10/$0.50. Those GPT-6 rates apply through 272K input tokens; longer prompts cost more. Sonnet and Sol therefore share headline prices, while Opus’s input and output rates are 60% below Astra’s. Completed-task costs can tell a different story.

Opus 5.5 vs. GPT-6 Astra: Reports Since Release

On September 27, Stephen Smith described rebuilding a presentation with Opus after being dissatisfied with Astra and Fable 5.1 at Max. He preferred Opus’s narrative, design, and responsiveness to direction, using Medium for everyday work and Max for polish. This is a useful writing-and-slides anecdote, not a controlled comparison: the prompts and effort settings differed.

Kevin Kern’s published finance-app experiment provides a counterweight. In September 25–26 solo runs at High, Opus scored 79.8/100 and Astra 76.5 on his private composite assessment. Astra finished sooner—35m 55s versus 43m 25s—and had a slightly lower estimated API cost: $14.48 versus $14.93.

Kern used Claude Code and Codex CLI respectively, so tooling is part of the comparison. One TypeScript feature cannot establish a universal winner. It does show why cheaper tokens need not produce the cheaper finished job.

Sonnet 5.5 vs. GPT-6 Sol: First-Day Evidence

In a September 28 community test, Reddit user _Duex asked both models to build a Rust decompressor at Medium effort. The author reported that both passed 4,055 hidden tests. Sonnet finished in 3m 41s for $0.52; Sol took 6m 37s for $0.27. Sonnet’s generated program also ran faster.

That is a specific tradeoff: quicker delivery versus lower reported spend. It was one task and one run per model, without independent reproduction here.

Feedback within Claude’s community also differs. In another launch-day discussion, user msw3age reported that Sonnet High was faster and cheaper on two tasks but missed issues Opus caught; adding Opus review erased the savings. That compares Claude models, not Sol, but illustrates why rework belongs in the decision.

These examples establish possible tradeoffs, not community consensus. OpenAI also fixed an image-understanding bug in Sol and Luna on September 25, making older visual-task complaints particularly worth retesting.

Which Model Should You Choose Today?

For Claude 5.5, try Sonnet on clear, repeatable assignments and Opus on ambiguous work where judgment matters. Compare Sonnet with Sol at your actual budget; compare Opus with Astra using the tools you intend to keep.

Give each the same brief and materials. Count accepted results, corrections, elapsed time, and total cost. For writing, judge voice and structure; for code, run the application. Keep Haiku 5.5 on the watchlist until its release supplies evidence you can test.