GPT-6.1 Sol vs GPT-6 Astra vs Claude Opus 5.5: Which Should You Use?
Start with GPT-6.1 Sol for most coding, document, and agent workflows when published token cost matters. Escalate to GPT-6 Astra when failure is expensive and peak capability is worth testing. Test Claude Opus 5.5 when you prefer the Claude stack or need long-running agentic coding and knowledge work. This guide separates provider evaluations from your own acceptance tests, compares API list prices on the same basis, and explains why GPT-6.1 Sol may not appear in ordinary ChatGPT Chat.
Contents
Use GPT-6.1 Sol as the default candidate for most coding, document, and multi-step workflow tasks. Test GPT-6 Astra when the work is unusually difficult or a wrong result is expensive. Test Claude Opus 5.5 when your workflow already uses the Claude ecosystem, or when long-running agentic coding and knowledge work are the main job.
That is a starting route, not a universal ranking. OpenAI’s launch evaluations favor GPT-6.1 Sol on several cost-versus-performance comparisons, but they are provider-run evaluations with specific harnesses and reasoning settings. They do not prove which model will finish your repository change, contract review, or browser workflow with the fewest retries.
There is also an access trap: as verified on October 1, 2026, GPT-6.1 Sol is available in ChatGPT Work, Codex, and the OpenAI API, but not yet in ordinary ChatGPT Chat. If it is missing from the Chat model picker, that is not necessarily an account fault.
The practical answer by task
| Your task | First model to test | When to test another model |
|---|---|---|
| Multi-file coding, debugging, code review, or a tool-using development agent | gpt-6.1-sol | Move to Astra if repeated runs miss architectural constraints or the cost of a bad change is high. Test Opus 5.5 if the Claude toolchain or its long-running agent behavior is important to your workflow. |
| Long reports, complex PDFs, policy analysis, or document production | gpt-6.1-sol for a cost-sensitive baseline | Test Opus 5.5 on the same documents if writing quality, instruction retention, or your Claude-based process matters. Use Astra for unusually difficult, high-stakes synthesis. |
| Multi-step business workflows using tools | gpt-6.1-sol | Escalate to Astra when missed constraints can trigger costly actions. Test Opus 5.5 when your tools and orchestration are already built around the Claude API. |
| Difficult scientific or open-ended research | gpt-6-astra as the premium OpenAI candidate | Keep Sol as the lower-rate control. Include Opus 5.5 only under the same evidence, tools, and acceptance rubric. |
| High-volume work with deterministic checks | gpt-6.1-sol, if all three models are the comparison set | This article does not re-evaluate Luna. For the broader GPT-6 family route, see GPT-6 Astra vs Sol vs Luna. |
The decision rule is simple: choose the least expensive model that repeatedly clears your acceptance bar, then reserve a stronger or different model for observable failure cases.
What is actually comparable
The three options overlap on coding, documents, tool use, and long context, but their product surfaces and reasoning controls are not identical.
| Model | Exact API model ID | Provider positioning | Context and output | Reasoning behavior |
|---|---|---|---|---|
| GPT-6.1 Sol | gpt-6.1-sol | OpenAI positions it as near-Astra performance for complex coding, computer use, and professional work at a lower rate. | 1,050,000-token context; 128,000 maximum output tokens | Selectable effort: low, medium, high, xhigh, and max; none and minimal are not supported. OpenAI recommends the Responses API for tool calling. |
| GPT-6 Astra | gpt-6-astra | OpenAI’s most capable model for its most demanding work. | 1,050,000-token context; 128,000 maximum output tokens | Selectable effort from low through max. |
| Claude Opus 5.5 | claude-opus-5-5 | Anthropic positions it for long-running agentic coding and knowledge work. | 1,000,000-token context; 128,000 maximum output tokens | Adaptive thinking is always on; the default effort is medium, and effort controls depth, latency, and cost. |
These labels are useful for selecting a first candidate. They are not evidence that one provider’s word “most capable” is directly comparable with another provider’s “long-running agentic coding.” The systems use different harnesses, tools, system prompts, effort controls, and billing details.
What the official evaluations do—and do not—show
The GPT-6.1 Sol announcement reports several useful comparisons. Read them as evidence about the tested setups, not as a universal leaderboard.
| Official evaluation | What it tests | OpenAI’s published conclusion | What it does not establish |
|---|---|---|---|
| DeepSWE v1.1 | Long-horizon software-engineering tasks in real codebases | GPT-6.1 Sol matched GPT-6 Astra in the shown setup at roughly one-fifth of the cost. | It does not prove equal performance on your language, repository, test suite, or agent harness. |
| GDP.pdf | Answers grounded in complex professional PDFs with tables, charts, diagrams, and fine print | OpenAI reported Sol above Opus 5.5 with fallbacks at less than half the tested cost per task. | It is not a general writing-quality test and does not replace review on your own documents. |
| AutomationBench | End-to-end business workflows using many tools | OpenAI reported Sol above Opus 5.5 at medium effort and at roughly one-third of the tested cost. | Tool definitions, permissions, retries, and failure handling may differ from your production workflow. |
| Terminal-Bench Science 0.1 | Scientific work using code and terminal tools | OpenAI reported Astra as the highest-scoring model in the comparison, while Sol had a much lower tested cost per task. | It does not mean Astra is the best value for every research task, nor that the same result transfers to a different environment. |
OpenAI also states that its GPT evaluations may be run in a research environment or through the API, where system prompts, tools, and effort can differ from production ChatGPT. Competitor results may come from public reports rather than the same production surface. That is why a provider benchmark should guide your test design, not end the selection process.
Anthropic’s Opus 5.5 model page gives specifications, pricing, intended workload, and platform availability. It does not provide a neutral, same-harness comparison against both GPT-6.1 Sol and GPT-6 Astra for your task. The public evidence therefore supports a shortlist, not a universal winner.
API price comparison on one billing basis
The following rates were verified on October 1, 2026. They are public API list prices in USD per 1 million tokens. They do not include tool-call charges, storage, regional processing, fast modes, retries, human review, or negotiated contracts.
| Model | Standard input | Cached input or cache read | Cache write | Output | Long-context rule |
|---|---|---|---|---|---|
gpt-6.1-sol | $2.00 | $0.10 | $2.50 | $10.00 | Above 272K input tokens, OpenAI charges 2× input and cache rates and 1.5× output for the full request. |
gpt-6-astra | $10.00 | $1.00 | $12.50 | $50.00 | The same OpenAI long-context multiplier applies above 272K input tokens. |
claude-opus-5-5 | $4.00 | $0.20 | $5.00 for a 5-minute cache; $8.00 for a 1-hour cache | $20.00 | Anthropic says Claude 4.6 and later include the full 1M context window at standard per-token rates. |
Cache semantics are not perfectly interchangeable. OpenAI lists a cache-write rate and cached-input rate; Anthropic offers cache writes with different lifetimes plus cache reads. Compare the actual sequence of writes, reads, and expirations in your application rather than treating one cache column as identical across providers.
A transparent no-cache example
Assume one run uses 200,000 input tokens and 20,000 output tokens, with no cache, no tools, and no retries. The OpenAI requests remain below the 272K long-context threshold.
- GPT-6.1 Sol:
0.2 × $2 + 0.02 × $10 = $0.60 - Claude Opus 5.5:
0.2 × $4 + 0.02 × $20 = $1.20 - GPT-6 Astra:
0.2 × $10 + 0.02 × $50 = $3.00
This only shows published token charges for an identical token mix. It says nothing about acceptance rate, output length, latency, tool fees, or how many attempts each model needs.
Do not mix subscription and API costs
| Access path | Subscription or membership | Minimum funding | Introductory credit | Usage charges |
|---|---|---|---|---|
| OpenAI API for Sol or Astra | ChatGPT and the API have separate billing systems; a ChatGPT subscription is not the API bill. | No universal minimum is stated on the cited model pages; account and contract terms can differ. | No universal first-month credit is promised on the cited pages. | Model tokens plus any applicable tools, storage, regional processing, or speed tier. |
| Claude API for Opus 5.5 | A paid Claude chat plan does not include Claude API or Console access. | Anthropic says most Console organizations use prepaid usage credits; check the amount shown in your billing account. | No universal first-month credit is promised in the cited billing article. | Model tokens plus any applicable features and platform-specific charges. |
Because account-level funding and contract terms are not a single public number, the safe conclusion is narrower: Sol has the lowest published standard token rates of these three; that does not automatically make it the lowest total cost per accepted task.
Why GPT-6.1 Sol is missing from ordinary ChatGPT Chat
OpenAI separates Chat, ChatGPT Work, Codex, and the API. They are not four names for the same model picker.
| Surface | GPT-6.1 Sol | GPT-6 Astra | Claude Opus 5.5 |
|---|---|---|---|
| Ordinary ChatGPT Chat | Not yet available as of October 1, 2026 | Do not infer access from the API name; check the current selector and plan | Not applicable |
| ChatGPT Work | Available to supported Plus, Pro, Business, Enterprise, and Edu users; workspace controls can still matter | Officially available; enterprise access may depend on admin settings | Not applicable |
| Codex | Available; the model list can depend on plan, client version, and workspace settings | Available on supported access paths | Not applicable |
| OpenAI API | Use gpt-6.1-sol | Use gpt-6-astra | Not applicable |
| Claude API and supported cloud platforms | Not applicable | Not applicable | Use claude-opus-5-5; availability and IDs can differ on partner platforms |
| claude.ai and Claude apps | Not applicable | Not applicable | The model page documents its use in Claude apps, but selector access can depend on the current plan and rollout |
If Sol is absent from ordinary Chat, switching browsers or repeatedly refreshing is unlikely to solve the product-surface mismatch. Open Work or Codex if your plan supports them, or use the API model ID. If a supported surface still does not show the model, then check the app version, workspace policy, rollout status, and account entitlement.
Run a task acceptance test instead of copying a leaderboard
A small controlled evaluation is more useful than arguing about one global score. Build it before changing your default route.
1. Choose representative tasks
Use 10 to 20 tasks that resemble real work, not polished demos. A practical set could contain:
- four coding tasks: a bug fix, a multi-file feature, a code review, and a failing-test diagnosis;
- three document tasks: a complex PDF answer, an evidence-based summary, and a structured deliverable that must follow a template;
- three workflow tasks: tool selection, a multi-step operation, and a failure-recovery case.
Include normal cases, boundary cases, and at least one task where the model should stop or ask for clarification.
2. Define acceptance before running
For code, use tests, lint, security checks, and a review checklist. For documents, define required claims, prohibited claims, citations, table fields, and formatting. For workflows, specify allowed tools, confirmation points, side-effect limits, and the final state.
Do not change the rubric after seeing a favored model’s answer.
3. Hold the environment constant
Use the same input files, task instructions, tools, permissions, maximum output, and timeout. Record the exact model ID. Reasoning controls are not identical across providers, so compare a documented default run first, then treat each tuned effort level as a separate experiment.
4. Repeat and record all attempts
One successful run can be luck. Run each important task several times and record:
- first-pass acceptance;
- final acceptance after permitted retries;
- input, cached, cache-write, and output tokens;
- tool calls and external fees;
- elapsed time to an accepted result;
- human review or repair time;
- failure category, not just pass or fail.
5. Calculate cost per accepted task
Use this production-oriented formula:
Cost per accepted task = all model and tool charges across attempts + human rework cost, divided by accepted results.
Astra can justify its higher list rate if it prevents an expensive failure or materially raises acceptance. Opus 5.5 can justify its rate if it completes your long-running Claude-based workflow with fewer interventions. Sol should remain the default when it clears the same bar with lower total cost.
Final routing recommendation
Use this route until your own measurements overturn it:
- Start with GPT-6.1 Sol for everyday complex coding, document analysis, and tool-using workflows. It has the lowest published standard token rates in this three-model set and OpenAI reports near-Astra results on several launch evaluations.
- Escalate to GPT-6 Astra when the task is unusually hard, ambiguous, scientific, or costly to get wrong—and only after defining the acceptance gain that would justify the higher rate.
- Run Claude Opus 5.5 as a real contender, not a decorative third option, when you use the Claude API or Claude-oriented tooling, or when long-running agentic coding and knowledge work dominate the workload.
- Do not treat the ordinary Chat picker as the availability source for Sol. Use ChatGPT Work, Codex, or the API while OpenAI says the model is not yet in Chat.
- Promote a model by task class, not by reputation. The winner for repository changes may not be the winner for PDFs, business automation, or research.
The most useful first action is to freeze 10 representative tasks and their acceptance rules, then run all three models on the same evidence and tools. That turns provider claims into a routing decision you can defend.
