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

Related integration page and current specifications: BeatAPI

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.

• Old Sol and 6.1 Sol have the same Standard input, write, and output prices.
• The new model halves the cache-read rate, not every task's bill.
• 6.1 Sol and Astra require Responses for tools and do not support none reasoning.
• Compare success, retries, total usage, and latency with the same task and harness.
• Keep a verified escalation path instead of routing every task to the most expensive model.

GPT-6.1 Sol vs GPT-6 Sol vs Astra at a glance

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, GPT-6.1 Sol, GPT-6 Astra, and the migration guide.

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.

Is the new Sol actually cheaper?

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 illustrates why that distinction confuses users; the post's generated estimates are not billing evidence.

For longer loops, the Astra coding-agent cost worksheet explains repeated context. Its dated service-specific rates should be rechecked before reuse.

The migration boundary: tools and disabled reasoning

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

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.

A fair coding-agent comparison you can reproduce

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.

When should you choose each model?

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.

Long context and access-route caveats

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, old Sol, and Astra 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.

Frequently asked questions

Is switching from Sol to 6.1 Sol just a model-name change?

Only some compatible requests can be that simple. Audit reasoning settings, tools, sampling parameters, response parsing, and continuation handling first.

Is 6.1 Sol half the price of old Sol?

The cache-read rate is half. The other Standard token rates in this comparison are unchanged. Overall savings depend on actual usage and behavior.

Is Astra always five times more expensive?

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.

Which reasoning setting should I compare?

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.

Should every failed Sol task go to Astra?

No. Fix missing context, broken tools, or ambiguous acceptance criteria first. Escalation helps only when model capability is the likely limiting factor.

Where should a new user start?

Use the 6.1 Sol first-request guide, 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.