GPT-6.1 Sol vs GPT-6 Sol vs Astra: Costs and Migration for Coding Agents

A developer published a selection framework for choosing between OpenAI's GPT-6 Sol, GPT-6.1 Sol, and GPT-6 Astra for coding agents, comparing API IDs, Standard pricing, caching rates, context limits, and endpoint support. Using a declared short-context fixture of 100,000 uncached input tokens, 900,000 cache reads, and 50,000 output tokens, the analysis puts GPT-6.1 Sol at $0.790 per task versus $0.880 for GPT-6 Sol and $4.400 for Astra — roughly 10.2% cheaper than the older Sol and 82.0% cheaper than Astra on those counts. The author recommends evaluating GPT-6.1 Sol as the next default for Responses-based agents, retaining GPT-6 Sol where integrations depend on disabled reasoning, and reserving Astra for cases where extra correct work justifies the premium.

Related integration page and current specifications: BeatAPI https://beatapi.io/gpt-6-1-sol-api For GPT-6.1 Sol vs GPT-6 Sol vs Astra, start with compatibility and complete-task cost. For a Responses-based coding agent, evaluate GPT-6.1 Sol as the next default; retain GPT-6 Sol when an existing integration depends on disabled reasoning, and use GPT-6 Astra when it produces enough additional correct work to justify the premium. This is a selection framework, not a claim that we ran a three-model benchmark. The most useful comparison is not just intelligence versus token price. It is whether your agent remains compatible, what its complete task costs, and whether the resulting patch passes your acceptance tests. Specifications and Standard prices below were checked on September 30, 2026 . none reasoning. | Decision point | GPT-6 Sol | GPT-6.1 Sol | GPT-6 Astra | |---|---|---|---| | API ID | gpt-6-sol | gpt-6.1-sol | gpt-6-astra | | OpenAI input / output per million | $2 / $10 | $2 / $10 | $10 / $50 | | Cached input per million | $0.20 | $0.10 | $1.00 | | Cache write per million | $2.50 | $2.50 | $12.50 | | Context / maximum output | 1,050,000 / 128,000 | 1,050,000 / 128,000 | 1,050,000 / 128,000 | | Disabled reasoning | none supported | Unsupported | Unsupported | | Tool calls through Responses | Supported | Supported | Supported | | Tool calls through Chat Completions | Only with none | Unsupported | Unsupported | These are OpenAI direct Standard rates and documented endpoint capabilities, not a guarantee of feature parity at another gateway. Sources: GPT-6 Sol https://developers.openai.com/api/docs/models/gpt-6-sol , GPT-6.1 Sol https://developers.openai.com/api/docs/models/gpt-6.1-sol , GPT-6 Astra https://developers.openai.com/api/docs/models/gpt-6-astra , and the migration guide https://developers.openai.com/api/docs/guides/latest-model . OpenAI positions the new Sol close to Astra for complex coding, computer use, and professional work. That makes it worth testing before paying Astra rates for every turn. It does not establish a universal winner for writing, repository review, or difficult debugging. If you are new to the model, start with what GPT-6.1 Sol is and how to call it https://beatapi.io/gpt-6-1-sol-api . At equal token counts, the changed Standard rate relative to old Sol is cached input: $0.20 becomes $0.10 per million. If there are no cache reads, the listed token rates produce the same bill. If reasoning changes, retries change, or the model writes longer answers, equal rates do not imply equal spending. Take a declared short-context workload , not an executed benchmark: 100,000 uncached input tokens, 900,000 cache reads, and 50,000 total output tokens, distributed across requests that each stay below the long-context threshold. There are no cache writes or paid tools in this fixture. | Model | Uncached input | Cache reads | Output | Total | |---|---|---|---|---| | GPT-6 Sol | $0.200 | $0.180 | $0.500 | $0.880 | | GPT-6.1 Sol | $0.200 | $0.090 | $0.500 | $0.790 | | GPT-6 Astra | $1.000 | $0.900 | $2.500 | $4.400 | The new Sol saves approximately 10.2% against old Sol on this ledger. It is approximately 82.0% cheaper than Astra for these same counts. Neither percentage predicts task-level savings if the models consume different amounts of reasoning or require different numbers of attempts. For an actual decision, replace the fixture with returned usage. Count all attempts, including failed patches and retries. Do not infer API spend from a ChatGPT or Codex subscription's percentage meter. A first-person cost discussion https://www.reddit.com/r/codex/comments/1wtjvh5/is gpt 61 that cheap/ illustrates why that distinction confuses users; the post's generated estimates are not billing evidence. For longer loops, the Astra coding-agent cost worksheet https://beatapi.io/blog/gpt-6-astra-coding-agent-cost explains repeated context. Its dated service-specific rates should be rechecked before reuse. Suppose your old request combines these fields: { model: "gpt-6-sol", reasoning effort: "none", tools: { type: "function", function: { name: "run tests", description: "Run the isolated project's tests.", parameters: {"type": "object", "properties": {}, "required": } } } } Changing only model to gpt-6.1-sol leaves two problems: disabled reasoning is unsupported, and tool calling belongs on Responses. A migrated request uses the Responses tool shape and a supported effort: { model: "gpt-6.1-sol", reasoning: {"effort": "low"}, input: "Run the tests and explain the first failure. Do not edit files yet.", tools: { type: "function", name: "run tests", description: "Run the isolated project's tests.", parameters: { type: "object", properties: {}, required: , "additionalProperties": false }, strict: true } } Send it to your selected service's /v1/responses . These snippets demonstrate request shapes, not a complete agent or an authenticated test. Your application must inspect the typed output, execute an authorized function, and return function call output with the matching call id . A schema named run tests cannot run your tests by itself. Function calling documentation https://developers.openai.com/api/docs/guides/function-calling Before switching, verify a complete round trip: first tool request, real test execution, returned result, and final answer. Preserve necessary reasoning and tool items when managing history. Recheck sampling fields such as temperature and top p against the migration guide; an adapter can inject unsupported defaults even if your visible request does not. Use a small repository with a known failing test. For example, a chunk function using range 0, len items - size, size loses the final chunk and fails when the input length equals the chunk size. Ask each model to diagnose and fix it without changing the tests. Keep the same repository commit, tool permissions, prompt, test suite, turn cap, and output budget. Record the reasoning effort instead of treating each model's default as equivalent. Run more than one attempt if you want to discuss consistency. | Record | Why it matters | |---|---| | Tests passed and patch diff | Plausible explanations do not establish correctness. | | Attempts and tool calls | A cheaper call may require more iterations. | | Uncached, cached, write, and output usage | Visible answer length hides reasoning and repeated input. | | Elapsed time and failures | A fast answer is not always a fast completed task. | | Accepted result and total charge | Measures cost per useful outcome. | We have not run this controlled model experiment. It is the evaluation procedure to use before claiming that one model is faster, smarter, or cheaper per completed task. An arithmetic rate comparison and a vendor evaluation do not substitute for it. Evaluate 6.1 Sol first for a new Responses-based coding agent or an existing Sol workflow that already uses supported reasoning. Its pricing makes it a reasonable candidate for everyday implementation, review, and repository analysis, subject to your acceptance checks. Keep old Sol while migrating if your production agent depends on none or Chat Completions tool calling. First make the endpoint and parsing changes on an isolated task; then compare behavior. Keeping a working model during a controlled migration is preferable to changing all components at once. Escalate to Astra when a representative failure analysis shows that it produces more correct accepted results on a particular class of difficult work. Supply a compact summary, failed approaches, and the test evidence rather than blindly replaying an enormous transcript. Escalation should have a turn cap and spend cap. For a simple budget rule, compare total cost divided by accepted tasks over the same test set, while also tracking latency. That includes failed attempts. Avoid declaring an Astra task “worth it” merely because the answer sounds more sophisticated. All three official model pages apply higher rates to an entire request once input exceeds 272K tokens: input and cache rates double, output rates multiply by 1.5. Sending a 1M-token repository is therefore a different budget decision from using a 100K-token working context. A larger maximum window is not a reason to include every file. If you choose BeatAPI, inspect 6.1 Sol https://beatapi.io/gpt-6-1-sol-api , old Sol https://beatapi.io/gpt-6-sol-api , and Astra https://beatapi.io/gpt-6-astra-api individually. Use those customer rates to recalculate the ledger, and verify your required tools. OpenAI direct service tiers, hosted tools, residency controls, and account terms are not implied by a gateway's compatible request format. Only some compatible requests can be that simple. Audit reasoning settings, tools, sampling parameters, response parsing, and continuation handling first. The cache-read rate is half. The other Standard token rates in this comparison are unchanged. Overall savings depend on actual usage and behavior. Its ordinary input, output, and cache-write Standard rates are five times the new Sol's; cached reads are ten times. Task-level cost also depends on token counts, attempts, processing mode, and tools. Preserve an existing supported effort initially. When replacing none or minimal , evaluate low . Record settings and compare actual outcomes rather than assuming equal effort names ensure equal computation. No. Fix missing context, broken tools, or ambiguous acceptance criteria first. Escalation helps only when model capability is the likely limiting factor. Use the 6.1 Sol first-request guide https://beatapi.io/gpt-6-1-sol-api , then evaluate one bounded tool task before adopting a default or escalation policy. Disclosure: We build BeatAPI. This article distinguishes documentation-based examples from tests actually executed; it does not report a model benchmark.