Claude Opus 5.5 vs GPT-6 Sol vs Luna: Prices, Access & Real Value

Claude Opus 5.5, GPT-6 Sol, and GPT-6 Luna are worth comparing—but not as three interchangeable subscriptions. Opus 5.5 is the higher-priced option with the strongest Intelligence Index result in the snapshot below. Sol occupies the middle price tier for demanding work. Luna is dramatically cheaper for repeatable tasks that you can check reliably. Crucially, OpenAI lists Sol and Luna in Work and Codex, not ordinary ChatGPT Chat.

Published and checked September 23, 2026. The providers date these releases September 22. This is an independent, source-based buying and usage guide, with third-party benchmark data—not a claim that HiseHub personally tested the three models.

The quick choice: start with your work, not the newest name

• Already paying for Claude? Try Opus 5.5 on an important task within your existing allowance before changing plans. Its launch does not automatically mean you need a second subscription.
• Already using Work or Codex? Sol is the sensible starting candidate for a multi-file change or an ambiguous task. Use Luna for a tightly specified extraction, transformation, or small fix with clear acceptance checks.
• Building an automated service? Evaluate Luna first for routine volume, with a tested escalation path to Sol or Opus when it fails your checks. Do not route sensitive or costly decisions solely by token price.
• Only want a chatbot? Confirm the actual product and model picker before buying. API access, ordinary chat, and an agent workspace are different purchases and experiences.

These are HiseHub’s starting recommendations, not a universal performance ranking. They follow the documented product roles and the price differences below. OpenAI’s current model guide recommends Sol for complex work and Luna for well-defined, repeatable work. Use our Model Access Map to check the route, and the AI Model Comparison tool to explore the available evidence.

Where can you actually use each model?

Sources: Anthropic’s Opus availability page, OpenAI’s Work/Codex plan and pricing guide, and OpenAI’s model availability guide. A listed plan is not a guarantee that every client, managed workspace, or region exposes the model immediately.

If a model is missing, check four things before spending more: the signed-in account, the product surface, the workspace’s allowed models, and whether your client is current. A new subscription will not fix an unsupported surface. Likewise, signing an integration in with an API key changes its billing route; see Claude Code API billing versus subscription usage and our broader subscription-versus-API cost guide.

API prices: Luna is 20 times cheaper than Sol at base token rates

The table shows USD per one million tokens, not monthly plan prices. OpenAI’s base rates apply to requests with up to 272,000 input tokens; the long-context adjustment is explained below. These are Standard rates, excluding tool fees, regional processing adjustments, and taxes.

Sources: Opus 5.5 specifications, GPT-6 Sol specifications, and GPT-6 Luna specifications. Sol and Luna separately list a 922,000-token maximum input and 128,000-token maximum output. Do not read the total context window as an input allowance plus a free output allowance.

For identical uncached input and output counts, Sol’s base token bill is half Opus’s, and Luna’s is one twentieth of Sol’s. That arithmetic does not establish equal task quality, equal token consumption, or equal end-to-end cost. A model that needs repeated repair attempts can lose its apparent advantage.

Two worked examples—not benchmark results

Example A: 1,000 short jobs. Assume each job uses 10,000 uncached input tokens and 2,000 total billable output tokens, no caching, tools, retries, or premium processing. The totals are $80 with Opus, $40 with Sol, and $2 with Luna. Those are arithmetic examples, not estimates of how many tokens a real task will require. Include billed reasoning tokens when comparing usage records, not only the visible answer.

Example B: repeated context with cache hits. Across a set of short requests, assume one million total input tokens: 100,000 uncached and 900,000 cache reads, plus 10,000 output tokens. Each request stays below OpenAI’s long-context threshold. Excluding the earlier cost of populating the cache, the totals are $0.78 for Opus, $0.48 for Sol, and $0.024 for Luna.

The second example narrows the Opus–Sol gap because their cache-read rates are equal. It also exposes a common budgeting mistake: reusing a prompt does not prove a cache hit. Inspect actual cache usage. Count initial writes, expired entries, changed prefixes, and retry costs separately. Opus lists a five-minute cache write at $5 per million tokens and a one-hour write at $8; Sol and Luna list writes at 1.25 times uncached input pricing.

For your own comparison, keep the workload fixed and record total spend divided by accepted results, together with human correction time. Our API Cost Calculator helps with estimates, but verify that its selected model, price timestamp, and assumptions match your billing route.

Try these exact assumptions in the calculator: short batch, cached context, or long input. The fragment carries only model and numeric assumptions—not your prompt, account, or API key.

Three pricing traps worth checking

1. Large OpenAI prompts change the rate. Above 272,000 input tokens, Sol and Luna apply 2× input/cache rates and 1.5× output rates to the full request. The more expensive rate is not limited to the tokens above the threshold.
2. Fast mode is not one universal multiplier. OpenAI’s API model pages list 2× applicable API rates; Work/Codex credit billing lists 2.5× the Standard credit rate where available. Anthropic lists Opus 5.5 Fast at $8 input/$40 output per million tokens. Compare like billing routes.
3. A lower API bill is not a lower subscription fee. Anthropic’s “40% less” launch claim describes its typical token-billed workloads. Base input/output prices fell 20% from Opus 5, while cache reads fell 60%. Your savings depend on the workload; it is not a promise of a 40% cheaper monthly plan.

The Fast and price-reduction figures come from the Opus 5.5 announcement, the two OpenAI API specifications above, and the separate Codex credit rate card. Anthropic also announced higher five-hour subscription limits; that does not establish an unlimited allowance or a fixed message count.

What do the independent benchmarks actually say?

HiseHub’s existing Artificial Analysis feed already contains all three models. Here is a September 23, 2026, 06:36 UTC cache snapshot, not a permanently live ranking. We selected each model’s Max row and kept Opus’s Default Fallback qualifier visible.

Source: Artificial Analysis’s linked model pages and HiseHub’s cached API response. The feed labels this snapshot Index version 4.3; the public site may show newer methodology revisions and rounded values. Cost per Index task is the evaluator’s aggregate workload cost, not the cost of your prompt or the short-job examples above.

Our reading is that Opus buys a stronger composite result at materially higher evaluation cost, while Luna makes much cheaper attempts possible. Neither statement tells you which model writes your best article or fixes your specific bug. An Index point is not a percentage of intelligence. “Max” is not a standardized cross-provider compute budget, and the Opus row includes the evaluator’s stated fallback configuration.

At this snapshot, the feed’s separate coding and agentic fields are missing for these rows. A dash in our tools means unavailable, not zero. We will not manufacture a coding league table from those gaps. Use the live model comparison for refreshed rows, and judge a representative task before switching a production workflow.

Which one is worth trying for your actual task?

Routine extraction, classification, and repeatable small edits

Start an evaluation with Luna when correctness is cheap to check: required fields are present, numbers reconcile, a schema validates, or a test suite catches regressions. The useful design is not “always use the cheapest model.” It is “accept only results that pass checks, and escalate uncertain cases.” Set a retry limit so a broken task cannot generate an uncontrolled loop.

Multi-file code changes, research synthesis, and ambiguous requests

Sol is a reasonable first candidate when the task needs more interpretation than a template can provide. Write down the constraints, expected deliverable, and tests before running it. If it repeatedly loses a requirement or needs extensive repair, compare Opus on the same material. The question is whether the extra cost removes enough failures and review time—not whether one model writes a more confident explanation.

Long, high-value jobs where mistakes create expensive rework

Put Opus on the shortlist when a stronger result could save substantial human effort. The current composite benchmark supports evaluating it; it does not justify unattended access to production systems. Use a branch, staging copy, or reversible workspace; retain approval for consequential changes. More autonomy should come from better verification and rollback, not from a launch headline.

For an existing subscriber, the first experiment can often fit inside the plan you already have. Do not buy three subscriptions just to reproduce a leaderboard. Compare a small set of your real tasks, recording completion quality, review minutes, total usage, and wall-clock time. Keep the model and reasoning settings with each result so you can repeat the comparison later.

Before switching an API integration, check compatibility

A successful model-picker switch does not prove an older API integration can accept the new model.

• Opus 5.5: Anthropic’s migration guide documents always-on adaptive thinking and rejects forced tool_choice values such as any or a named tool. Preserved thinking and older computer-use tool versions also require attention. Its what’s-new guide explains the changed handling of text between tool calls; a progress display can go quiet even when a request succeeds.
• Sol and Luna: the official API specifications recommend Responses for built-in tools. On Chat Completions, function calling is documented only with reasoning_effort=none. An older chat-compatible wrapper is not proof that a reasoning-and-tools workflow is supported.

Test a small non-production request, a real tool round trip, structured output, and a continued conversation. Inspect billing and errors, then keep the previous configuration available for rollback. If you use a self-hosted AI interface, verify its provider adapter supports the required API features before changing the default model for everyone.

The bottom line

Use Luna to test a low-cost route for checkable routine work; use Sol as a middle-cost candidate for demanding everyday tasks; evaluate Opus when better completion quality can justify more spend. Those are starting points. Your product access, actual token usage, cache behavior, and acceptance rate decide the final choice.

For follow-up, bookmark the AI Model Release Tracker for launch facts and independent-data status, the Model Access Map for plan routes, and AI subscription prices by country for published app-store subscription prices. That last tool compares subscriptions—not the API token rates in this article.