Select the appropriate GPT-6 model by evaluating task scope, API pricing and failure overhead. This article includes fixed-quantity calculation examples and blank acceptance templates. It avoids ranking models purely from official marketing positioning.
Engineers can start evaluation with Luna for high-throughput classification tasks, Sol for daily coding and general-purpose workloads, and Astra for highly complex scenarios. This grouping is just a starting point guided by official positioning, not a fixed performance ranking of the three models on every use case.
This article references the Sol/Luna release announcements and OpenAI official documentation, verified on September 23, 2026. No paid head-to-head benchmark testing across the three models has been executed, so no claims are made about absolute accuracy, raw speed or maximum completion rate.
Segment Workloads by Acceptance Difficulty
| Task Characteristics | Recommended Starting Model | Core Acceptance Metrics |
|---|---|---|
| Large batches of classification or extraction tasks with similar structure and clear output boundaries | Luna | Field accuracy, outlier sample handling and retry cost |
| Routine code edits, document drafting and multi-step lightweight workflows | Sol | Diff validation, unit test results, tool invocation frequency and manual repair overhead |
| High-stakes complex tasks with tangled constraints and expensive failure outcomes | Astra | Full goal fulfillment, implicit constraint compliance and total end-to-end task cost |
A single workflow can be split into multiple stages. Cheaper models can preprocess input data first, and more capable models handle hard edge cases. However, routing rules need empirical sample validation. Teams cannot simply forward all failed outputs to higher-tier models only based on official positioning. Prompt quality, tool limitations and context window constraints are also common sources of failure.
Direct API Price Comparison
The table below shows OpenAI standard pricing for input tokens up to 272K, measured in USD per million text tokens. Other modes and surcharge rules apply separately.
| Model | Standard Input | Cached Read | Cached Write | Output |
|---|---|---|---|---|
| GPT-6 Luna | 0.10 | 0.01 | 0.125 | 0.50 |
| GPT-6 Sol | 2 | 0.20 | 2.50 | 10 |
| GPT-6 Astra | 10 | 1 | 12.50 | 50 |
These figures are official API rates. They are not Treerouter quotations and cannot be converted into ChatGPT subscription message credits. Once input volume exceeds 272K tokens, the input and cached charges for the whole request double, and output pricing becomes 1.5 times the base rate.
Take a fixed example: 20,000 standard input tokens and 5,000 billable output tokens, ignoring cache. The estimated cost is $0.0045 for Luna, $0.09 for Sol, and $0.45 for Astra. The rate sheet and local calculator include these sample calculations.
This arithmetic is built on static token volume assumptions. It does not represent the actual token consumption of real tasks, nor prove low-cost models can deliver equivalent quality. Tool calls, retries and manual human corrections add extra expense outside this simple formula.
Write Acceptance Standards Before Judging Model Output
Download blank acceptance record templates to document input specifications, allowed operations and success criteria for realistic test tasks. For code tasks, fix the starting git commit, dependency versions and test commands. Extraction tasks need ground-truth reference answers and anomaly logging.
The evaluation record must capture precise model ID, vendor, endpoint, reasoning configuration, tool permission scope, cache usage, failure events and manual repair workload. Different models can use distinct reasonable configurations, but these configurations must be isolated. Identical reasoning labels do not guarantee identical compute behavior.
Evaluation should not only keep the best single successful run. Discarded retry attempts must be counted into total cost, and incomplete tasks should be tracked separately. This table is not a precomputed benchmark and does not assign simple numerical scores.
Evaluate Upgrade Cost Together With Failure Cost
If Luna requires frequent manual correction, its low per-token price may not translate to the lowest overall task cost. On the other hand, simple, mechanically verifiable tasks do not always require the highest-end model at the start. Both conclusions should be backed by recorded experimental data.
Teams can define pre-set upgrade triggers, such as missing fields, failed test cases or violated constraints. Track incremental cost after routing tasks to a stronger model. Higher model pricing is not direct proof of correctness. One successful sample result cannot be generalized to the whole task distribution.
Lock Down Integration Method First
Review the entry points and usage guides for Sol and Luna, estimate batch workload budgets for Luna, check tool compatibility and plan Responses API migration. If tool protocol implementation remains incomplete, comparing model capability against invalid tool calls delivers limited insight.
Treerouter offers unified access for model routing and batch budget management, simplifying evaluation across multiple OpenAI model variants during pre-production validation.
The final selection should rely on repeatable task test data, full end-to-end expense tracking and human labor overhead. Official positioning helps narrow candidate pools, but cannot replace acceptance test data.
Frequently Asked Questions
Is Sol better than Astra for all programming tasks?
This statement cannot hold universally. Sol works well as an evaluation starting point for daily coding tasks. Complex, high-risk tasks still need evaluation under identical acceptance standards.
Are the cost examples measured from real model benchmark runs?
No. They use fixed input and output token quantities only to demonstrate rate differences.
Can routing decisions directly use model names as the routing condition?
Teams should define task rules and upgrade triggers first, then validate routing logic from test results. Model tier alone cannot serve as a guarantee of task success.
Final Remarks
The three GPT-6 variants are optimized for different cost-performance tradeoffs. Luna targets high-volume simple extraction and classification. Sol balances cost and capability for daily coding and general multi-step tasks. Astra serves high-complexity work with strict constraints.
Token price alone cannot determine the best model. Total task cost includes retries, manual repair and business loss caused by incorrect outputs. Build acceptance criteria, run repeatable tests, and calculate full lifecycle cost before production routing.
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