Silver Week's AI ShowdownGPT-6 Sol, Luna and Opus 5.5 Compared on Cost and Quality

Packing light makes a holiday easier—until you leave behind the tool you need and have to buy it again at your destination. Choosing an AI model involves a similar trade-off.

During Japan's 2026 Silver Week holiday period, OpenAI released GPT-6 Sol and GPT-6 Luna, while Anthropic introduced Claude Opus 5.5. Both companies date their announcements September 22. That is the date on their official pages, rather than a conversion into Japanese local time: three new choices arriving on the same announcement day.^[1][2]

What makes this interesting is that the models do not all pursue the same balance. One route makes large volumes of work inexpensive; another raises the quality of difficult deliverables. The price differences are too large to reduce the story to a single winner.

1. The recommendation: buy completed work that passes your checks

Here is a practical starting point: try Luna for routine processing, use Sol as a baseline for everyday development, and compare Opus 5.5 on complex deliverables. Decide using total cost per accepted task. This is our recommendation based on published specifications and evaluations, not a claim that we ran all three models through the same production workload.

Luna is a candidate for frequent, inexpensive processing. Sol is a candidate for coding and multistep tasks with a controlled budget. Opus 5.5 is a candidate when the quality of long-running development or knowledge work matters most. The manufacturers' positioning follows those broad roles: complex coding and agentic workflows for Sol, focused high-volume work for Luna, and long-running coding and knowledge work for Opus.^[3][4][5]

But “accepted” means different things to someone classifying email and someone migrating an existing system. The former might need a correct label in a fixed format. The latter needs working code, preserved behavior and changes that a reviewer can understand. A pleasant conversation is also a different outcome from a finished job.

This comparison reflects information checked on September 23, 2026. Official specifications, external measurements and our illustrative calculations are identified separately. These are newly released models; long-term reliability in everyday work and subtle differences in Japanese writing are not established rankings here.

2. Prices are 1:20:40—but first identify what is being billed

Start with standard API pricing. An API is the metered interface used by applications and programs to call a model. The figures below are in US dollars per million tokens. Tokens are units used to process text; they are not equivalent to a fixed number of Japanese characters or English words.

OpenAI figures are Standard rates for prompts with no more than 272K input tokens. Opus figures are standard rates. Taxes, regional processing, tools, cache creation and other charges are excluded. These numbers come from pricing and model documentation.^[1][6][5]

For uncached input and output, the Luna:Sol:Opus unit-price ratio is 1:20:40. With identical quantities read and generated, Sol costs half as much as Opus, and Luna one-fortieth as much. But quantities change when a model reasons for longer or repeatedly uses tools. A unit-price ratio is not automatically the ratio between final bills.

Notice that Sol and Opus both charge $0.20 for cache reads. For work that repeatedly supplies the same long materials, looking only at the twofold difference in uncached input can mislead. Caching reuses previously processed input; it does not mean every input token automatically receives the lowest rate.

Do not turn this into a subscription comparison

ChatGPT and Claude subscriptions, and allowances in Codex or Claude Code, are separate comparison axes. Halving an API rate does not establish that a monthly subscription costs half as much or permits twice as many requests. Check the models, limits and additional-usage terms attached to the actual subscription.

For someone using AI a few times a day, access in an existing app and avoiding repeated explanations may matter more than a few cents. For a service making tens of thousands of calls a day, small differences in unit prices and failure rates matter. Personal value and a service's production costs ask different questions even when both are called cost-effectiveness.

3. Opus leads on measured quality. Why choose Sol or Luna?

To compare a common evaluation source, we use Artificial Analysis model pages. Its Intelligence Index combines multiple evaluations; it is not a simple accuracy percentage. The table uses each model at max effort, with Default Fallback enabled in the published Opus result.^{[7][8][9][10]}

Opus scores higher and costs more in these results. 58 does not mean 58 percent correct, and dividing it by 48 does not establish that a model is about 21 percent smarter. The cost is a weighted average for this evaluation suite, not a market price for summarizing one meeting. “Max” also does not imply identical compute or time across providers.

The useful observation is that cost changes much more sharply than the index score. If Luna already passes every requirement for a task, paying for Opus every time may add little. Conversely, repeatedly choosing Sol on price alone is not sensible if it misses important conditions in that workload.

A new generation does not win every test

Artificial Analysis's launch analysis reports a mixture of gains and regressions from the previous generation. In its Coding Agent Index using the Codex environment, Sol rises from 55 to 57, while Luna falls from 43 to 41. Better cost efficiency and better capabilities in every domain are different claims.^[11]

That is part of what makes this release interesting. Progress does not only mean solving a problem that was previously impossible. It can mean running every day a task that previously cost too much to justify. Product-description cleanup, support-ticket classification and drafting test candidates can benefit substantially from inexpensive repetition at sufficient quality.

Benchmark scores come with test conditions

Anthropic reports 66.4 percent on Terminal-Bench 4.0; Artificial Analysis's launch analysis reports 59.6 percent. An identical benchmark name does not imply an identical evaluation setup. Anthropic specifies xhigh effort and a standard error of ±2.6 percentage points, and describes fallback models completing certain tasks when safeguards intervene. Combining differently measured results in one table without those notes would mislead.^[2][12]

A general index cannot directly establish how a model handles Japanese business etiquette, internal abbreviations or requests that require clarification. Break “quality” into factual correctness, instruction adherence, functioning deliverables and stylistic preference. Those categories reveal what you actually need to test.

4. $2.50, $50 or $100 for 1,000 tasks: how much human time is that?

This section is an illustrative calculation, not a measurement. Assume each task uses 10,000 uncached input tokens and 3,000 billed output tokens, repeated 1,000 times. Output means the total billed quantity, including chargeable reasoning. For now, exclude search, execution environments, regional premiums, tax, retries and human labor.^[6][5][13]

On these figures, Luna appears to win comfortably. Now suppose correcting its output takes two more human hours than correcting Sol's output across the entire batch. If we value that time at an illustrative $30 per hour, the extra $60 exceeds the $47.50 saved.

Under the same assumptions, the Luna–Sol break-even difference is 95 minutes across 1,000 tasks. The Sol–Opus difference is 100 minutes, or six seconds per task. If Opus actually saves that much review time, it recovers the API premium. This is not a measured claim that Opus saves six seconds: it calculates a boundary worth measuring.

The argument also works in the opposite direction. If classification output can be checked automatically and needs no manual correction, the inexpensive model retains its advantage. A more expensive model does not necessarily reduce review, either. Longer responses can increase reading time.

A useful formula is straightforward:

Cost per accepted task = (API, tool and retry costs + human review and correction costs) ÷ accepted tasks

Do not remove failed requests from both the denominator and the cost calculation. A task that succeeds on its second attempt carries the cost of both attempts. Abandoned work also consumes resources. This formula evaluates more than a fast first response.

5. Caching, long prompts and reasoning change the apparent price

Cache-heavy work narrows the Sol–Opus gap

Assume a request contains 100,000 input tokens: 90,000 cache-read tokens and 10,000 uncached tokens, followed by 3,000 output tokens. For one request with an existing cache, Sol costs $0.068 and Opus $0.118. The ratio is approximately 1.74, below the twofold difference for ordinary input and output. Cache creation costs extra, and reuse must occur within the applicable retention period.^[6][5]

This matters for agents repeatedly using long specifications or shared instructions. It matters less when every request contains a completely different short text. Measure the proportion of your input that actually hits the cache, rather than looking only at the advertised discount.

Crossing 272K input tokens changes the bill

When Sol or Luna receives more than 272K input tokens, input and cache rates double and output rates rise by 50 percent for the entire request. The premium is not restricted to the tokens above the boundary. Sol's uncached input becomes $4 and output $15 per million tokens. A budget for large document bundles cannot rely only on the initial pricing table.^[3][4][6]

Official context windows are 1,050,000 tokens for Sol and Luna and 1,000,000 for Opus; standard maximum output is 128,000 tokens for all three. But fitting material into a large box and correctly using its contents are different capabilities. If the task is to identify one exception buried in a large collection, measure both the amount supplied and the evidence supporting the answer.^[3][4][5]

A short answer does not necessarily mean a small bill

OpenAI bills invisible reasoning tokens as output. A three-line conclusion can follow substantial internal processing. Read usage records rather than estimating cost from the length of the visible answer.^[13]

Likewise, Anthropic's claim of roughly 40 percent lower running cost than Opus 5 and an external evaluation at max effort describe different conditions. Artificial Analysis reports about 1.6 times as many output tokens at max as the previous generation, leaving cost per task broadly similar. Depending on the work and configuration, a model can spend a price reduction on additional reasoning.^[2][12]

Record the reasoning setting alongside the model name when evaluating cost. The highest setting does not mean the best value in every situation. It is worth asking whether simple work needs the longest deliberation.

6. Speed, writing and development experience extend beyond a scorecard

Speed has at least three meanings: the delay before a response starts, the rate at which text arrives and the time until the job is complete. Code can stream quickly and still need three rounds of repair. Time during which you can work elsewhere also feels different from time spent waiting for a decision at the screen.

Opus 5.5 Fast mode is documented as offering up to 2.5 times the output speed at twice the standard token rates. “Up to” is not a constant guarantee, and faster output does not imply a 2.5-fold reduction in total task duration. Interactive work with a deadline and an overnight batch attach different value to the premium.^[14]

For writing, separate preference from accuracy. Does a Japanese business email obey prohibited-word rules, suit the relationship and avoid adding unwarranted certainty? Does research cite real sources that support its claims? Does code pass tests while avoiding unrelated changes? These are more useful questions than whether one response feels impressive.

Even with the same model, documents, search access, editing tools and test environments affect results. If the model is an engine, the agent application supplies the vehicle and controls. Comparing complete applications and comparing models in an otherwise fixed tool environment are both useful, but they answer different questions.

Developers building their own API integrations also face migration costs. Opus 5.5 cannot disable thinking and introduces breaking changes for existing settings such as forced tool use. With Sol and Luna, Chat Completions function calling is limited to reasoning effort none; check the Responses API when combining reasoning and tools. Replacing a model name does not establish that a migration is finished.^[15][3][4]

7. Choose by job: an inexpensive entry point and a route for difficult cases

The following is a testing order, not a measured universal ranking. The stronger the specification and the cheaper the acceptance check, the stronger the argument for starting with Luna.

For support requests, Luna could classify cases and send ambiguous ones to Sol. But the model's own assurance that it is confident should not be the only pass. Use externally checkable conditions: missing required fields, absent supporting passages or known exception categories.

Using the earlier assumption of 10,000 input and 3,000 output tokens per task, passing 1,000 tasks through Luna and repeating 100 of them on Sol at the same usage costs $2.50 + $5 = $7.50, compared with $50 for Sol on every task. This calculation excludes the validator, routing and extra context. It guarantees nothing about accuracy: bad routing can become a cheap way to produce many wrong answers.

Conversely, when the request itself is ambiguous and an early mistake can disrupt substantial downstream work, starting with a more capable candidate may be worthwhile. Putting a cheap model first should not become an objective in itself. Additional stages create additional handoffs and validation work.

8. Bring your own test back from the holiday

Enjoying new releases and deciding what to use every day can coexist. Build a small reusable comparison set at the end of the holiday, and the next launch becomes easier to assess.

Our proposed starting point is 20 real tasks, attempted three times per model. Neither number is an official recommendation. This is a small pilot for spotting obvious biases and lucky runs, not proof of statistical superiority. Mix easy cases, tasks requiring judgment and examples that previously failed.

1. Supply the same materials. Fix prompts, documents, tools and time limits; record the model, reasoning setting and run date.
2. Write scoring rules first. Define required conditions, major errors and acceptable corrections. Hide model names during scoring if practical.
3. Record both usage and human time. Include input, cache and output tokens, tool costs, retries, review minutes and correction minutes.
4. Separate failure types. Missing knowledge, ignored instructions, tool errors, format problems and stylistic preference are different issues.
5. Choose the least expensive setup that passes. Exclude applications with unresolved major failures; improve materials or validation before testing again.

The launch-day winner is not automatically the winner for your work. These releases offer a wider choice of budgets and quality levels, rather than one answer for everyone.

Finding jobs Luna handles adequately expands where AI is affordable. If Sol passes reliably, it can become an everyday tool. If Opus reduces the time needed to fix difficult work, its premium has a reason. After asking which model is most capable, ask what it costs to finish your job. That turns comparison into something useful beyond the announcement cycle.

The images are AI-generated conceptual illustrations. Cost examples use only their stated assumptions; they are not performance measurements, price guarantees or subscription allowances. Prices and availability can change, so check the linked documentation before implementation.

References

1. [1]OpenAI, API Changelog — September 22, 2026. Release date, APIs and standard rates through 272K input tokens. Checked September 23, 2026. ↩
2. [2]Anthropic, Introducing Claude Opus 5.5. September 22 announcement; vendor evaluations, settings and cost claims. Checked September 23, 2026. ↩
3. [3]OpenAI, GPT-6 Sol. Specifications, reasoning settings and long-context pricing. Checked September 23, 2026. ↩
4. [4]OpenAI, GPT-6 Luna. Specifications, intended use and pricing conditions. Checked September 23, 2026. ↩
5. [5]Anthropic, Claude Opus 5.5 overview. Pricing, caching, context and standard maximum output. Checked September 23, 2026. ↩
6. [6]OpenAI, API Pricing. Standard short- and long-context rates and caching. Checked September 23, 2026. ↩
7. [7]Artificial Analysis, GPT-6 Sol (max). Index 48 and $1.06 per evaluation task. Checked September 23, 2026. ↩
8. [8]Artificial Analysis, GPT-6 Luna (max). Index 37 and $0.07 per evaluation task. Checked September 23, 2026. ↩
9. [9]Artificial Analysis, Claude Opus 5.5 — max, default fallback. Index 58 and $5.98 per evaluation task. Checked September 23, 2026. ↩
10. [10]Artificial Analysis, Benchmarking methodology. Evaluation methods and performance metrics. Checked September 23, 2026. ↩
11. [11]Artificial Analysis, GPT-6 Sol and Luna push the cost efficiency frontier. September 22 launch analysis. Checked September 23, 2026. ↩
12. [12]Artificial Analysis, Claude Opus 5.5 takes the top spot. September 22 launch analysis, evaluation results and token usage. Checked September 23, 2026. ↩
13. [13]OpenAI, Reasoning models. Billed reasoning tokens and usage. Checked September 23, 2026. ↩
14. [14]Anthropic, Fast mode. Output speed and premium rates. Checked September 23, 2026. ↩
15. [15]Anthropic, Migrating to Claude Opus 5.5. Breaking changes. Checked September 23, 2026. ↩