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Claude Opus 5.5 vs GPT-6 Sol: How to Choose by Use Case and Budget

Anthropic and OpenAI unveiled their new models on the same day, Tuesday 22 September 2026, roughly 90 minutes apart: Claude Opus 5.5 on one side, GPT-6 Sol on the other. The first is described as Anthropic's most capable public model after Fable 5.1; the second is the budget tier of OpenAI's GPT-6 Astra flagship. According to the source, neither vendor compares its new model directly against the other's — each benchmarks against the rival's previous generation. So the useful signals for a decision lie elsewhere: in pricing, in third-party evaluations, and above all in what you actually need the model to do.

Two models, two positioning strategies

Claude Opus 5.5 targets the high end: available through the Anthropic API, Amazon Bedrock, Google Cloud Vertex AI, Microsoft Foundry and paid subscriptions, with no free tier. GPT-6 Sol plays the volume game: offered in the OpenAI API, ChatGPT Work and Codex, but not in the standard ChatGPT chat mode. Both include an "effort" slider that controls how long the model reasons before answering — a setting that heavily influences the bill, as we'll see.

List price: a two-to-one gap

Anthropic charges $4 per million input tokens and $20 per million output tokens for Opus 5.5. GPT-6 Sol sits at $2 input and $10 output — half the price. Each vendor pairs its launch with an efficiency claim: Anthropic says Opus 5.5 costs 40% less than Opus 5 in practice, while OpenAI says Sol makes roughly half as many factual errors as its predecessor. These are vendor claims, not independent measurements.

What third-party evaluations show — and their limits

Artificial Analysis places Claude Opus 5.5 ahead of GPT-6 Sol across several domains: 58 points at maximum effort versus 48, 61 versus 48 in finance, 63 versus 51 in legal and 60 versus 49 in engineering. Two important caveats. First, Artificial Analysis tested Opus 5.5 with Anthropic's default fallback mechanism, which can route a blocked request to an older model such as Claude Opus 4.8 — so the score does not reflect Opus 5.5 alone. Second, these rankings often shift in the days after a launch, and the gaps could narrow.

Anthropic itself acknowledges that benchmark gaps have become "a less reliable guide to real-world differences." There is also a deeper methodological limit: the two vendors' official comparisons share almost no common tests, and tests bearing the same name are not necessarily measured under the same conditions.

The real trade-off: cost per task, not cost per token

This is the most telling figure in the brief. At maximum effort, a task costs an average of $5.98 with Opus 5.5 versus $1.06 with Sol, according to Artificial Analysis. In other words, the two-to-one gap at the token level becomes roughly five-to-one on a real task: the pricier model also burns more tokens thinking. If your budget is tight, track that metric rather than the rate card.

Selection criteria by use case

Writing and content

For short to medium texts, the perceived quality difference is often less decisive than the accumulated cost. A half-price model that produces a usable draft in one pass is frequently cheaper than a stronger model you have to re-prompt. Test your own prompts: that is the only reliable judge for your style.

Code

The engineering gap (60 versus 49 per Artificial Analysis) argues for Opus 5.5 on complex work: refactoring, long debugging sessions, architecture. But GPT-6 Sol is integrated into Codex, which simplifies day-to-day use. A mixed approach — Sol for routine iterations, Opus 5.5 for hard problems — is often the most rational setup.

Analysis, finance and legal

These are the areas with the widest reported gaps (61 versus 48 in finance, 63 versus 51 in legal). When a mistake is expensive, the premium is easier to justify. Conversely, for repetitive document summarisation, Sol is probably enough.

At a glance

  • Token pricing: Opus 5.5 at $4/$20 per million tokens (input/output); Sol at $2/$10.
  • Cost per task at maximum effort: $5.98 versus $1.06 (Artificial Analysis).
  • Overall scores: 58 versus 48 at maximum effort (Artificial Analysis).
  • Availability: Opus 5.5 via the Anthropic API, Bedrock, Vertex AI, Microsoft Foundry and paid subscriptions; Sol via the OpenAI API, ChatGPT Work and Codex.
  • Shared feature: an adjustable effort slider on both models.

Key takeaways

The choice is not simply "most powerful" versus "cheapest." Start by setting the effort slider to the level you genuinely need — that is where most of the bill is decided. Reserve the pricier model for tasks where an error costs more than the tokens, and switch to the cheaper one for everything else. Finally, keep in mind that the third-party scores cited here are recent, likely to change, and that the Opus 5.5 score includes a fallback mechanism to an older model.

This article is published by Roboto, a platform for generating texts, images, videos and voiceovers with AI. No testing was performed by us: the observations reported come from the cited source and Artificial Analysis.

Source

Claude Opus 5.5 vs GPT-6 Sol : une bataille entre la puissance et le prix

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