Quick answer: which model should you use?
There is no universal winner. For low-cost, high-volume API work, GPT-6 Luna has the lowest published token rates among these models. For long, complex coding tasks, compare current task-level results and your own app build rather than relying on one score. Claude Opus 5.5 and Grok 4.7 both publish strong coding results, but their task costs differ substantially by reasoning effort and harness.
Bottom line: Start with the least expensive model that reliably completes your workflow. Escalate to a stronger or higher-effort model only when the cheaper one fails your tests.
Choosing between Claude Opus 5.5, GPT-6 Sol, GPT-6 Luna, and Grok 4.7 is less about picking a permanent champion and more about matching capability to the work. A model that wins a benchmark can still be the wrong default if it costs more than the task warrants or needs repeated fixes.
What the comparison actually measures
In the video above, No Code MBA compares model output on a CRM app-building task in Dyad, then discusses CursorBench 4.0 and API costs. The CRM result is a useful hands-on example, not a broad independent benchmark: one prompt, one app, a particular model configuration, and the Dyad workflow can all affect the outcome. Treat the reported 87% score and roughly $1 run as creator-reported test results, not a guarantee that the same model will build every app more accurately or at that price.
Official API prices: compare tokens, not headline anecdotes
Published API prices are per one million tokens and exclude the number of tokens a particular task consumes. Based on the vendors' current public rates, GPT-6 Luna is $0.20 input and $1.20 output per million tokens in short context; GPT-6 Sol is $4 input and $20 output. Anthropic lists Opus 5.5 at $4 input and $20 output, while xAI lists Grok 4.7 starting at $2 input and $6 output. Long-context rates, cached tokens, fast modes, provider routing, and actual token usage can change a task's bill.
| Model | Input / 1M tokens | Output / 1M tokens | What to know |
|---|---|---|---|
| GPT-6 Luna (short context) | $0.20 | $1.20 | Lowest listed token rate here; verify current pricing before budgeting. |
| GPT-6 Sol (short context) | $4 | $20 | Higher-cost OpenAI option in this family. |
| Claude Opus 5.5 | $4 | $20 | Anthropic says it costs 40% less to run than Opus 5 overall; that is not a direct 40% price cut against every other provider. |
| Grok 4.7 | From $2 | From $6 | Fast variant costs more; check the current API rate card. |
These rates make Luna attractive for repeated, bounded tasks such as classifying records, drafting straightforward CRUD screens, or generating test cases. But token price alone is not total cost: a model that takes several attempts, emits more tokens, or needs extensive human repair can be more expensive in practice.
How to read the benchmark results
Cursor describes CursorBench 4.0 as a set of ambiguous, multi-file tasks drawn from real Cursor sessions. Its published table includes score, cost per task, token count, and steps, which is more informative than a score alone. A task cost depends on the model's reasoning setting, context, harness, and token use; do not compare it directly with the cost of a different CRM prompt in Dyad.
A fair comparison needs the same app brief, constraints, integrations, model effort, and test suite. Score the finished app on working flows, data integrity, usability, accessibility, and the amount of manual repair. Repeat the run if you need a dependable result; a single attempt is a demonstration, not a distribution.
A practical way to choose for your app
1. Start with the cheapest model that supports the tools and context your workflow needs. For a small feature or repeatable task, try Luna first.
2. Use Sol, Opus 5.5, or Grok 4.7 when the work is ambiguous, spans multiple files, or needs sustained debugging. Compare task outcomes at the exact effort level you will use.
3. Add automated checks before judging the result: run the app, submit forms, reload persisted data, test authentication and permissions, and inspect mobile layout.
4. Track total task cost, number of retries, and human repair time. The useful metric is cost per accepted result—not cost per million tokens or benchmark rank alone.
5. Keep a fallback model available. If one model fails a required flow, switch based on the failure you observed rather than brand loyalty.
What to take away
The useful story is the widening choice between capability and cost. GPT-6 Luna's low token price can make iteration cheap; Opus 5.5 and Grok 4.7 target demanding work, while GPT-6 Sol sits at a higher price tier. None is automatically best for every app. Test your own workflow with a fixed brief and acceptance checks, then choose the model that delivers reliable results for the lowest end-to-end cost.
Sources and further reading
Vendor prices and model descriptions change. Verify current rates before making a budget decision. Sources: Anthropic's Opus 5.5 announcement; OpenAI's GPT-6 Sol and Luna announcement; xAI's Grok 4.7 announcement; Cursor's CursorBench 4.0 results; and the No Code MBA video comparison
Frequently Asked Questions
Which AI model is cheapest for building apps?
Among the models compared here, OpenAI lists GPT-6 Luna at $0.20 per million input tokens and $1.20 per million output tokens in short context. Actual task cost depends on token use, retries, and any platform or routing fees, so measure cost per finished app task.
Is Claude Opus 5.5 better than GPT-6 Sol?
There is no universal winner. Compare both on the same task and effort setting. CursorBench 4.0 provides task scores and costs for its own multi-file coding tasks, while the video’s CRM build is one Dyad test. Use the model that passes your acceptance checks at the lower end-to-end cost.
What does the 87% GPT-6 Luna result mean?
It is the result reported for the specific CRM app build in the video, not a general model accuracy score. A different prompt, app scope, model settings, or evaluation rubric may produce a different result.
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