Route AI Spend When Astra Costs 2.5x Sol

Your inference bill is easier to route when you price a finished task instead of a token. GPT-6 Astra's Standard API price is $10 per million input tokens and $50 per million output tokens, 2.5 times GPT-5.6 Sol's Standard price of $4 input and $20 output.

The spread is the entry fee, not the whole story. OpenAI is giving GPT-6 Astra to a limited set of organizations first, with planned access for ChatGPT Plus, Pro, Business, and Enterprise users, the OpenAI API, Microsoft Azure, and AWS Bedrock.

Sticker price misses the route

Capability is part of the decision. OpenAI's OSWorld 2.0 evaluation put Astra at 72.6 percent and Sol at 65.7 percent. On Terminal-Bench Science 0.1, Astra scored 64.6 percent against Claude Fable 5.1's 52.6 percent, with an estimated API cost about 31 percent lower for Astra.

Capability alone does not set the route. ML6's cost-efficiency guidance points to total task completion cost, not per-token list prices. It also calls for representative production tasks, success criteria set before running, and repeated tests of each configuration.

A solved-task cost counts the total spend for a task that meets your success bar. It includes input, output, cached input, cache writes, retries, and fallback calls. A failed attempt that is retried still belongs in the total. Count completed tasks, not attempts, as the denominator.

A model can look expensive per token and still be cheaper per completed task if it needs fewer retries. It can look cheap per token and still be expensive if it produces outputs that require human repair. Use the completed job as the unit, including repair and retries.

The solved-task number is the route

In ML6's internal Kubeflow Pipelines migration test, low-effort Astra completed the task with no execution or build failures, while using fewer tokens and less time than high-effort Sol. Artificial Analysis's Coding Agent Index shows Astra (Max) scoring 62 while averaging 3.3 million tokens per task, versus Sol (Max) scoring 55 while averaging 10.2 million tokens per task.

Astra's 1,050,000-token context window makes long-context routing a cost decision, not a convenience. Its Standard pricing includes cached input at $1 per 1M tokens and an explicit cache-write price of $12.50 per 1M tokens.

Router tests make the cost logic concrete. Nvidia's Switchyard test found LangChain reducing costs by 74 percent across 145 multi-turn Deep Agents tasks, routing only 7 percent of calls to a frontier model and accepting a 6 percent accuracy tradeoff. In the researchers' in-distribution CodeRouterBench run, ACRouter cost $13.21 for the full task run, compared with $34.02 for always using Opus, a 2.6x saving.

Treat routing as a rule, not a default. Some workloads stay on the cheaper model. Others move to the premium model. Mid-task splits are for work that becomes hard enough to justify the premium. Default to the cheaper model until the solved-task number says otherwise.

Keep the route honest

Verify this checklist before the next invoice. Each item is a check, not a project. Run it on the workloads that drive the bill.

• Tag each workload with task type, expected output length, retry rate, and success criterion.
• Set success criteria before any model comparison runs.
• Prepare a small representative sample for the cheaper and premium models.
• Record total tokens, failures, retries, and time for each completed task.
• Compute cost per completed task, not per token.
• Route to the premium model only when its completed-task cost is lower or quality is required.
• Recheck the route after prompt, cache, or context changes.

Guard the long-context cliff by treating context size as a routing input, not a default. If a task can be split, cached, or summarized, the solved-task cost may fall before the model does. Do this before the next model change.