Skip to main content
The Human Bit

Model comparison · Reviewed decision guide

GPT-6 Sol vs Claude Opus 5.5

GPT-6 Sol or Luna, or Claude Opus 5.5: choose by the job and the review

A grounded comparison of OpenAI's GPT-6 Sol and Luna against Claude Opus 5.5, Anthropic's current model for long-running agentic coding and knowledge work, covering effort controls, long context, coding, documents, tool use, cost, migration breaks and the human test that should decide.

Useful for: People choosing a model for difficult analysis, coding, document work or a production workflow where quality, cost and review effort all matter

Reviewed
26 Sept 2026
Recheck
By
Evidence
12 named sources

Verdict by job

Pick the job in front of you

Start with GPT-6 Sol

Sol is the natural candidate when web search, file search, code execution, shell, computer use, MCP or the Work and Codex surfaces are part of the required route, at roughly half Opus 5.5's list price.

Switch when: Move to Opus 5.5 when the task remains weak after the OpenAI workflow is well specified, especially on long horizon code or source heavy reasoning where Anthropic reports stronger sustained autonomy.

Check before deciding

Confirm that the tool access is truly required and that each permission is narrower than the task, not merely convenient.

The answer in 30 seconds

Start with the work. Use the product name second.

GPT-6 Sol, GPT-6 Luna and Claude Opus 5.5 are all serious choices for complex professional work, and all three replaced a named predecessor on the same day, 22 September 2026. Sol is OpenAI's coding and agentic workhorse, priced at roughly half Opus 5.5's rate; Luna is OpenAI's fastest, cheapest current model for high-volume tasks; Opus 5.5 is Anthropic's current model for long-running agentic coding and knowledge work, which Anthropic says performs at Claude Fable 5.1's level on most work while costing 40% less than Opus 5. There is no honest universal winner. Run the same real task, at sensible effort, with the same evidence, and score the usable result rather than the first impression.

When the answer is close: run the same representative task in both. Keep the inputs, time and acceptance criteria constant, then compare the corrected result and the human effort needed to trust it.

One distinction that prevents a bad comparison

Read this before comparing

This page compares OpenAI's GPT-6 Sol and Luna with Anthropic's Claude Opus 5.5. GPT-6 Sol is the coding and agentic model in this pairing; Luna is its faster, cheaper sibling for high-volume work; GPT-6 Astra sits above both as OpenAI's flagship and has its own comparison against Claude Fable 5.1. Claude Opus 5.5 is Anthropic's current Opus, replacing Opus 5, with Claude Sonnet 5 as the practical lower-cost default beneath it and Claude Fable 5.1 above it for the hardest reasoning and longest-running work. If you are migrating existing code from GPT-5.6 Sol or Claude Opus 5, treat this as a migration decision with real breaking changes to test, not a routine version bump.

Best fit

What each option is genuinely useful for

Starting orientations, not permanent rankings. The real workflow and review burden still decide.

OpenAI coding and agentic model

GPT-6 Sol

OpenAI's model for complex coding and agentic workflows: long reports, code, research and jobs that take many steps, where it can search the web, open files, run code and use other software on its own. Released 22 September 2026 as the successor to GPT-5.6 Sol at roughly half its price. You reach it through ChatGPT's reasoning settings, ChatGPT Work, Codex or the API, depending on your plan. Its faster, cheaper sibling GPT-6 Luna suits high-volume, lower-stakes work in the same family.

A strong starting point when (4)
  • Long jobs where the model has to search the web, open files, run code and use other software on its own, including MCP connections (a standard way of plugging the model into other software)
  • Coding, design and knowledge work that needs several steps and competing constraints handled together
  • Workflows already built around ChatGPT, Work, Codex or the OpenAI API, or a migration off GPT-5.6 Sol chasing a lower price
  • High-volume, lower-stakes work routed to GPT-6 Luna instead, once you have confirmed quality holds against GPT-5.6 Luna
Look elsewhere when (3)
  • Simple drafting, classification or lookup work that GPT-6 Luna or another lighter model can finish reliably
  • A vague high stakes request with no evidence pack, review method or definition of success
  • Long context requests sent without regard to the 2x input and cache rate, and 1.5x output rate, applied above 272,000 input tokens for the full request

Anthropic's current model for long-running agentic work

Claude Opus 5.5

Anthropic's current model for long-running agentic coding and knowledge work, released 22 September 2026 as the successor to Opus 5. It reads a very large set of documents in one go (a one-million-token context window, where tokens are the short pieces of text a model reads), keeps thinking on at all times rather than letting you switch it off, and Anthropic says it performs at Claude Fable 5.1's level on most work while costing 40% less than Opus 5.

A strong starting point when (4)
  • Long coding jobs that touch many files, code review, and multi-hour audits or migrations run with parallel subagents and little oversight
  • Deep reasoning across large document or code contexts where the task can justify Opus-level spend
  • Workflows already using Claude, Claude Code, Cowork or the Claude API, including a deliberate migration off Opus 5
  • Dense charts, diagrams, screenshots and computer use, where Anthropic reports it reading detail at its lowest effort setting that Opus 5 needed its highest effort to match
Look elsewhere when (3)
  • Routine professional work that Claude Sonnet 5 already completes to the required standard
  • Code written for Opus 5 that disables thinking, forces tool use, or sends the older computer_20251124 tool: all four now break without a migration pass
  • Prompts that leave scope open when the model should make a narrow change or produce a concise answer

Side by side

Compare the differences that change the result

How to read the evidence in each row

GPT-6 Sol and Claude Opus 5.5Reviewed product documentation and named practitioner evidence where included.

Choose byThe Human Bit editorial orientation, not a provider claim or independent test result.

Human checkpointThe verification or judgement a person still needs before relying on the route.

11 differences. Open a row for the full detail.

Official roleTreat those statements as provider positioning.
GPT-6 Sol
OpenAI describes GPT-6 Sol as built for complex coding and agentic workflows, with stronger factual reliability and clearer communication than GPT-5.6 Sol, built on the advances behind GPT-6 Astra brought into a faster, more affordable model. GPT-6 Luna is its most efficient sibling for focused, high-volume tasks on the same foundation.
Claude Opus 5.5
Anthropic positions Opus 5.5 for long-running agentic coding and knowledge work and recommends it as the starting point for most workloads, saying it performs at Claude Fable 5.1's level on most work while costing 40% less than Opus 5.
Choose by
Treat those statements as provider positioning. Translate them into a task with inputs, constraints, expected output and a scorecard before choosing.
Human checkpoint
What exact failure in a cheaper model is the stronger model expected to remove, and how will you observe that improvement?
Context and outputAll three can hold a very large source pack.
GPT-6 Sol
The API model pages publish a 1,050,000 token context window and a 128,000 token maximum output for both GPT-6 Sol and GPT-6 Luna.
Claude Opus 5.5
The model overview publishes a 1,000,000 token context window and 128,000 token maximum output for the synchronous Messages API, extendable to 300,000 output tokens on the Message Batches API with a beta header.
Choose by
All three can hold a very large source pack. Choose by retrieval quality, source traceability, total token use and the workflow around the model, not the headline context number.
Human checkpoint
Which material genuinely affects the answer, and can you remove irrelevant context before paying a model to repeatedly reason over it?
Knowledge dateA later cutoff can help with background knowledge, but it does not replace live sources for a current decision or company specific fact.
GPT-6 Sol
OpenAI publishes a 20 April 2026 knowledge cutoff for GPT-6 Sol and 18 May 2026 for GPT-6 Luna. Current facts still require search or supplied evidence.
Claude Opus 5.5
Anthropic publishes June 2026 as both the reliable knowledge cutoff and training data cutoff for Claude Opus 5.5. Current facts still require search or supplied evidence.
Choose by
A later cutoff can help with background knowledge, but it does not replace live sources for a current decision or company specific fact.
Human checkpoint
Is the answer based on model memory, a live source or a document you supplied, and does the final output make that distinction visible?
API priceA headline rate does not settle task cost.
GPT-6 Sol
OpenAI lists US$2 per million input tokens and US$10 per million output for GPT-6 Sol (cached input US$0.20), and US$0.10 input and US$0.50 output for GPT-6 Luna (cached input US$0.01). Prompts above 272,000 input tokens are priced at 2x the input and cache rate and 1.5x output for the full request; fast mode is 2x the applicable rates; batch and flex are half of standard.
Claude Opus 5.5
The published standard rate is US$4 per million input tokens and US$20 per million output tokens, with a US$0.20 cache read rate, US$5 five-minute and US$8 one-hour cache write rates, and a 50% batch API discount.
Choose by
A headline rate does not settle task cost. Sol's list price is roughly half Opus 5.5's, and Luna's is a small fraction of either, but reasoning volume, tool calls, retries, context size and human correction can dominate the final bill.
Human checkpoint
What is the total cost of one accepted result, including failed attempts and review time, rather than the rate printed beside one million tokens?
Effort controlsStart at each model's own default, then sweep one level down and one up on the same task rather than carrying over a setting tuned for a different model.
GPT-6 Sol
GPT-6 Sol and GPT-6 Luna both expose none, low, medium (default), high, xhigh and max reasoning effort in the API; ChatGPT, Work and Codex can expose a narrower set depending on plan. Luna's ceiling is documented as reaching Max but not a further Ultra tier that a higher-end sibling may carry.
Claude Opus 5.5
Opus 5.5 defaults to medium effort on the API, a step down from Opus 5's high default, because thinking is always on and cannot be disabled at any level. Anthropic reports that Opus 5.5 at medium matches or beats Opus 5 at high on coding and knowledge-work evaluations, with effort names not corresponding to the same amount of thinking across the two models.
Choose by
Start at each model's own default, then sweep one level down and one up on the same task rather than carrying over a setting tuned for a different model. Keep the lowest level that meets the review standard consistently.
Human checkpoint
Are you asking for more effort because the task is harder, or because the first prompt failed to state the goal, evidence and constraints clearly?
Coding and agentic workUse the model inside the coding or agent surface that can see the right repository context, run the tests and show a reviewable diff.
GPT-6 Sol
OpenAI's stated aim for Sol is complex coding and agentic workflows with stronger factual reliability and clearer communication than GPT-5.6 Sol. It is reachable through ChatGPT Work, Codex and API tools including hosted_shell, apply_patch, computer_use and mcp.
Claude Opus 5.5
Anthropic reports that at its default medium effort, Opus 5.5 matches or beats Opus 5 at high effort on multistep repository work, in fewer steps and tokens, and sustains long-running autonomous work such as multi-hour audits and migrations with parallel subagents better than Opus 5. Early testers reported stronger code review with fewer false alarms.
Choose by
Use the model inside the coding or agent surface that can see the right repository context, run the tests and show a reviewable diff. The harness often matters as much as the model, and Anthropic's own comparisons here are its own testing, not an independent result.
Human checkpoint
Did the change preserve architecture, security, data handling and test intent, or did it merely make the visible failure disappear?
Documents and visual materialUse a representative source pack and require traceable extraction before comparing polished outputs.
GPT-6 Sol
Sol and Luna support text and image inputs and sit inside OpenAI workflows for files, data analysis, documents, spreadsheets, presentations and design work.
Claude Opus 5.5
Anthropic reports Opus 5.5 reads charts, diagrams and screenshots more accurately than Opus 5 without extra tooling, including position-dependent detail such as which boxes a flowchart arrow connects, and is more reliable at computer use over many steps at a lower effort setting than Opus 5 needed.
Choose by
Use a representative source pack and require traceable extraction before comparing polished outputs. A good looking document can hide a weak reading of the evidence, and a vendor's own testing is a reason to test on your material, not a substitute for it.
Human checkpoint
Which figure, clause, visual relationship or exception would cause harm if the model misread it, and where is the manual check?
Tools and product surfaceChoose the system that has the right tools with the narrowest permissions and the clearest audit trail.
GPT-6 Sol
The Responses API for both models supports web_search, file_search, image_generation, code_interpreter, hosted_shell, apply_patch, skills, computer_use, mcp and tool_search. Product availability varies across ChatGPT, Work and Codex.
Claude Opus 5.5
Opus 5.5 drops forced tool use, which now returns an error, and on the Claude API and Google Cloud it no longer accepts the earlier computer_20251124 computer-use tool; both worked on Opus 5. Server, client, file and caching tools carry over otherwise.
Choose by
Choose the system that has the right tools with the narrowest permissions and the clearest audit trail. If you are migrating from Opus 5, test forced tool use and computer_20251124 call sites specifically rather than assuming a like-for-like swap.
Human checkpoint
What can the model reach, what can it change and what evidence will show exactly which tool or source produced each result?
Response behaviourControl output length, scope, stopping conditions and, for Opus 5.5, the thinking display setting explicitly.
GPT-6 Sol
GPT-6 Sol is designed to follow complex intent and use more reasoning when needed, but higher effort can still produce a longer or more elaborate route than the task deserves.
Claude Opus 5.5
Between tool calls, Opus 5.5 now writes short progress-update text that returns empty by default unless the integration sets a display option to receive it, so a client built for Opus 5 that only renders plain text blocks can look silent through a long agentic turn until that setting is added.
Choose by
Control output length, scope, stopping conditions and, for Opus 5.5, the thinking display setting explicitly. Do not use effort as a substitute for a clear deliverable contract.
Human checkpoint
Does the output contain the result the user needs, or is capability being displayed through extra explanation, exploration and unrequested work?
Independent evidenceUse available accounts to design a better test, not to inherit somebody else's winner.
GPT-6 Sol
The public record used here does not yet contain an equally matched independent test of GPT-6 Sol or Luna against Claude Opus 5.5.
Claude Opus 5.5
Early write-ups comparing the two are mostly aggregator and SEO-style comparison blogs restating each provider's own published benchmark numbers, not independent hands-on testing to the standard this page otherwise requires. The absence of a genuinely independent result is a reason to test, not permission to infer a winner from a blog restating a vendor's own scores.
Choose by
Use available accounts to design a better test, not to inherit somebody else's winner. Their codebase, prompt, tools and tolerance for correction may not resemble yours, and a restated benchmark number is not a substitute for either.
Human checkpoint
Are you relying on a reported comparison because it matches your workflow, or because it confirms the product you already wanted to choose?
Migration and production decisionA production choice needs repeatable tests, cost ceilings, timeout and retry rules, source and permission controls, and a rollback path.
GPT-6 Sol
Pin the exact model ID (gpt-6-sol or gpt-6-luna) and effort, record tools and settings, and account for the 272,000 token long-context surcharge, rate limits and model availability. GPT-5.6 Sol and Luna remain available during the rollout, so a side-by-side migration test is possible before switching.
Claude Opus 5.5
Pin claude-opus-5-5 and effort, and test the four breaking changes from Opus 5 before shipping: thinking cannot be disabled, forced tool use errors, thinking blocks are tied to the model and conversation that produced them, and computer_20251124 is not accepted on the Claude API or Google Cloud. Size max_tokens generously, since thinking still counts toward it even when its text is not returned.
Choose by
A production choice needs repeatable tests, cost ceilings, timeout and retry rules, source and permission controls, and a rollback path. A successful demo, or a vendor's own migration note, is not the same as your own regression test.
Human checkpoint
Can the team reproduce the accepted result and explain what happens when the model is unavailable, refuses, times out or changes behaviour?

Real workflows

Try the same job through both routes

Each route opens to its steps and a starter brief to copy or adapt with Bit.

Representative task

Make a complex change across a real codebase

A feature touches several files, existing behaviour must be preserved and the change needs tests, review and a clear boundary rather than a large speculative rewrite.

GPT-6 Sol route4 steps and a starter brief
  1. Use Codex or a bounded repository tool and provide the issue, relevant architecture, acceptance criteria and commands that prove success.
  2. Ask Sol to inspect before editing and to identify the smallest coherent change, affected files and risks before implementation begins.
  3. Run the real test suite and inspect the diff, then ask for a correction only against observed failures rather than inviting a new design.
  4. Escalate effort only when the model understands the task but repeatedly misses a complex dependency or trade off.

Starter brief

Work on this repository task: [issue]. The required outcome is [acceptance criteria]. Preserve [behaviour and constraints]. First inspect the relevant files and explain the smallest coherent plan, including assumptions and risks. Do not edit outside [scope] without asking. Implement the change, run [commands], show the resulting diff and explain any test or requirement that remains unresolved. Treat passing tests as necessary but not sufficient: check security, data handling, error paths and backwards compatibility before declaring completion.

Claude Opus 5.5 route4 steps and a starter brief
  1. Use Claude Code or a bounded repository context, set effort explicitly at medium rather than carrying over a setting tuned for Opus 5, and state the exact scope.
  2. Ask for a plan tied to files, interfaces and tests, then require a pause before any architectural expansion or unrelated cleanup.
  3. Let the model implement and self check, and set a display option for its progress updates so a long agentic turn does not look silent partway through.
  4. Lower or raise effort only after measuring whether the task still passes the same acceptance and review criteria at the new default.

Starter brief

Complete this bounded repository task: [issue]. Success means [acceptance criteria]. The allowed scope is [files or modules], and these behaviours must not change: [constraints]. Inspect first, then propose a file level plan. Implement only the agreed plan. Do not refactor adjacent code or add features unless they are required for correctness and you explain why. Run [commands], review the diff for edge cases and security, and finish with a concise account of what changed, what was tested and what still needs a human decision.

How to choose: Start with the coding surface your team can audit and reproduce. Sol is compelling inside the OpenAI tool and Codex ecosystem at roughly half the price. Opus 5.5 is a strong route for long horizon multi file work, and Anthropic reports it needs less effort than Opus 5 did for the same result. The deciding evidence is the reviewed diff, tests and correction effort on your repository, not either provider's own benchmark.

What you still need to check

  • Confirm the plan respects the existing architecture before implementation creates momentum.
  • Review permissions, secrets, data boundaries and any command the agent can execute.
  • Inspect the diff for scope creep and silent behaviour changes even when tests pass.
  • Require a rollback path and a human owner before production deployment.

The Human Bit

A model comparison becomes useful only when a person defines useful.

Model launches encourage broad rankings. Real work is narrower. The Human Bit is the discipline that turns a launch claim into a bounded task, a fair test and an accountable decision.

Test the real job

A benchmark or another person's coding task may reveal capability, but it cannot settle performance on your documents, tools, quality standard and failure consequences.

What representative task can these models complete with the same inputs, tools, time and acceptance criteria?

Hold the conditions still

Changing model, prompt, effort, tools and source pack together produces a story rather than a comparison.

Which one variable are you changing, and which settings must be recorded so the result can be reproduced?

Score the usable result

A first response may look impressive while requiring substantial factual correction, file repair or code review before it can be used.

How much human work remains before this output is accepted in the real workflow?

Price the whole task

Token rates omit reasoning volume, cache behaviour, tool calls, repeated attempts, long context multipliers and the cost of human review.

What did one accepted result cost in money, elapsed time and correction effort?

Protect the boundary

A more agentic model can do more useful work and create a larger blast radius when files, commands, apps or external actions are available.

What may the model inspect and change, and which action must wait for explicit human approval?

Treat a migration as a real test

A named successor can carry breaking changes, as Opus 5.5 does from Opus 5, or a halved list price with unproven quality, as Sol and Luna do from their GPT-5.6 predecessors. A vendor's own migration note is a checklist, not a verdict.

Which specific breaking change or price assumption have you tested on your own workload, rather than accepted from the provider's page?

Keep judgement visible

The model can map evidence and trade offs, but the final choice often depends on risk tolerance, relationships, ethics and organisational reality.

Which part of the final recommendation is a human value judgement rather than a model discoverable fact?

Limits and uncertainty

What this comparison does not prove

Where to verify rather than rely on this guide (6)
  • The Human Bit has not completed a controlled internal test suite across GPT-6 Sol, GPT-6 Luna and Claude Opus 5.5. This page separates official facts, Anthropic's own reported testing and editorial decision guidance rather than presenting an invented winner.
  • Early third-party write-ups comparing GPT-6 Sol and Claude Opus 5.5 are largely SEO-style aggregator blogs restating each provider's own benchmark numbers rather than independent hands-on testing; treat them accordingly.
  • ChatGPT subscriptions, Claude subscriptions, APIs and cloud platforms are different commercial and operational surfaces. Published API rates do not tell you the price or allowance of a consumer task.
  • Model behaviour changes with effort, prompt, tools, context, product harness and safety systems. A result from ChatGPT, Claude, Codex or Claude Code is not a model only result.
  • OpenAI and Anthropic can update availability, aliases, limits and pricing. Pin exact model identifiers in production and recheck this page by the review date.
  • Safety systems and refusals can affect legitimate work. Test relevant edge cases without trying to remove safeguards or granting broader access to work around them.