Skip to main content
The Human Bit
Ask Bit

Model comparison

GPT-5.6 Sol or Claude Opus 5: choose by the job and the review

A grounded comparison of two high capability models for difficult professional work, including effort controls, long context, coding, documents, tool use, cost and the human test that should decide.

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

Apply this to my workAll comparisons

Read this before comparing

This page compares GPT-5.6 Sol with Claude Opus 5. GPT-5.6 is a family: Sol is the flagship, Terra balances capability and cost, and Luna is the fastest and lowest cost tier. ChatGPT also offers Sol Pro for the most difficult product workflows. Claude has Sonnet 5 as the practical lower cost default and Fable 5 above Opus for workloads that need Anthropic's highest available capability. Do not turn a family name into one model or assume the most expensive option should be the default.

What each option is really for

These are starting orientations. The workflow and review still decide.

OpenAI flagship reasoning model

GPT-5.6 Sol

A frontier model for complex professional work across coding, research, science, computer use, design and end to end knowledge work, available through ChatGPT reasoning, Work, Codex and the API depending on plan.

Strong starting point for

  • Complex work that benefits from OpenAI tools such as web search, file search, code execution, shell, computer use or MCP connections
  • 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
  • Tasks where reasoning effort can be swept from a sensible default to a deeper pass and measured

Poor fit when

  • Simple drafting, classification or lookup work that GPT-5.5 Instant, Terra, 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 higher price applied above the published input threshold

Anthropic complex work model

Claude Opus 5

Anthropic's current model for complex agentic coding and enterprise work, with adaptive thinking, a million token context window and model specific prompting guidance for long horizon tasks.

Strong starting point for

  • Complex agentic coding, multi file implementation, code review and long horizon work
  • 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
  • Tasks that benefit from strong vision and document understanding alongside careful written output

Poor fit when

  • Routine professional work that Claude Sonnet 5 already completes to the required standard
  • A migration from Opus 4.8 that ignores thinking being on by default and the resulting token budget changes
  • Prompts that leave scope open when the model should make a narrow change or produce a concise answer

Compare the parts that change the work

Each row ends with the decision rule and the human check that prevents a feature list from becoming a recommendation.

QuestionGPT-5.6 SolClaude Opus 5Choose byHuman checkpoint
Official roleOpenAI positions Sol as the flagship of the GPT-5.6 family for complex professional work. In ChatGPT it powers Medium, High and Extra High reasoning on eligible plans, while Sol Pro powers Pro.Anthropic positions Opus 5 for complex agentic coding and enterprise work, with the largest gains over Opus 4.8 in deep reasoning, long horizon tasks and agentic execution.Treat those statements as provider positioning. Translate them into a task with inputs, constraints, expected output and a scorecard before choosing.What exact failure in a cheaper model is the stronger model expected to remove, and how will you observe that improvement?
Context and outputThe API model page publishes a 1,050,000 token context window and a 128,000 token maximum output. Large prompts above 272,000 input tokens use a higher rate for the full request.The model overview publishes a 1,000,000 token context window and 128,000 token maximum output for the synchronous Messages API. Long context is the default rather than a separate variant.Both 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.Which material genuinely affects the answer, and can you remove irrelevant context before paying a model to repeatedly reason over it?
Knowledge dateOpenAI publishes a 16 February 2026 knowledge cutoff for GPT-5.6 Sol. Current facts still require search or supplied evidence.Anthropic publishes May 2026 as both the reliable knowledge cutoff and training data cutoff for Claude Opus 5. Current facts still require search or supplied evidence.The later cutoff can help with background knowledge, but it does not replace live sources for a current decision or company specific fact.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 priceThe published standard rate is US$5 per million input tokens and US$30 per million output tokens, with separate cached input, long context and tool charges.The published standard rate is US$5 per million input tokens and US$25 per million output tokens, with separate caching, batch, fast mode and platform conditions.The five dollar output difference does not settle task cost. Reasoning volume, tool calls, retries, context size and human correction can dominate the final bill.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 controlsGPT-5.6 supports a broad effort ladder through its product and API surfaces. ChatGPT exposes Medium, High and eligible higher settings, while Work and Codex can expose more family and effort choices.Opus 5 supports low, medium, high, xhigh and max effort. High is the API and Claude Code default. Effort controls thinking and tool use, not reliably the visible answer length.Start at a sensible middle or default level, then sweep one level down and one up on the same task. Keep the lowest level that meets the review standard consistently.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 workOpenAI emphasises software engineering, computer use and end to end professional workflows. Sol can operate with a wide set of first party tools in the API and is available through Codex and Work.Anthropic gives Opus 5 detailed guidance for multi file work, refactors, code review, self correction, subagents and long horizon tasks. It can narrate more and expand scope unless asked to stay bounded.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.Did the change preserve architecture, security, data handling and test intent, or did it merely make the visible failure disappear?
Documents and visual materialSol supports text and image inputs and sits inside OpenAI workflows for files, data analysis, documents, spreadsheets, presentations and design work.Anthropic highlights Opus 5 improvements in vision, chart, document and diagram understanding, alongside long context and Office file workflows through Claude and Cowork.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.Which figure, clause, visual relationship or exception would cause harm if the model misread it, and where is the manual check?
Tools and product surfaceThe API model supports tools including web search, file search, image generation, code interpreter, shell, computer use and MCP. Product availability varies across ChatGPT, Work and Codex.Opus 5 supports server and client tools, files, PDFs, vision, caching and batch workflows. The migration guide notes that web fetch and Priority Tier are not available on Opus 5.Choose the system that has the right tools with the narrowest permissions and the clearest audit trail. Do not choose a model first and then force the workflow around its missing surface.What can the model reach, what can it change and what evidence will show exactly which tool or source produced each result?
Response behaviourGPT-5.6 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.Anthropic warns that Opus 5 can produce longer user facing responses, narrate agentic work, over verify when prompted redundantly and expand scope on open ended instructions.Control output length, scope and stopping conditions explicitly. Do not use effort as a substitute for a clear deliverable contract.Does the output contain the result the user needs, or is capability being displayed through extra explanation, exploration and unrequested work?
Independent evidenceOne early independent account found Sol competent on difficult coding but not clearly better than a competing Claude model, and showed that reasoning effort can create a large cost spread on the same prompt.The public record used here does not yet contain an equally matched independent Opus 5 task result. The absence is a reason to test, not permission to infer the opposite conclusion.Use independent 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.Are you relying on a reported experience because it matches your workflow, or because it confirms the product you already wanted to choose?
Production decisionPin the exact API model and effort, record tools and settings, and account for long context pricing, rate limits, safety behaviour and model availability.Pin the model ID and effort, size max tokens for thinking plus output, test migration behaviour and account for retention, cloud route and unsupported features.A production choice needs repeatable tests, cost ceilings, timeout and retry rules, source and permission controls, and a rollback path. A successful demo is not the same decision.Can the team reproduce the accepted result and explain what happens when the model is unavailable, refuses, times out or changes behaviour?

See how the same work changes by route

These are complete starting workflows, not prompt examples without preparation or review.

Workflow

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-5.6 Sol route

  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 route

  1. Use Claude Code or a bounded repository context and state the exact scope, because Opus can usefully explore but can also widen an open ended task.
  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, but keep verification instructions concise so its own checking does not turn into repeated work.
  4. Lower or raise effort only after measuring whether the task still passes the same acceptance and review criteria.

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.

Decision rule: Start with the coding surface your team can audit and reproduce. Sol is compelling inside the OpenAI tool and Codex ecosystem. Opus 5 is a strong route for long horizon multi file work. The deciding evidence is the reviewed diff, tests and correction effort on your repository.

The Human Bit checks

  • 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.

Workflow

Analyse a decision with conflicting evidence

A strategic, financial or operational decision has competing objectives, incomplete evidence and a real cost if a polished model answer hides the trade off.

GPT-5.6 Sol route

  1. Provide the evidence pack, decision criteria, constraints and consequences, then ask Sol to map the disagreement before recommending anything.
  2. Use High effort only when the task has enough complexity to justify it and require citations or source references for material claims.
  3. Ask for a sensitivity analysis showing which assumption changes the decision rather than one confident ranking.
  4. Run the same brief at a lower effort or with a baseline model to see whether deeper reasoning materially changes the accepted result.

Starter brief

Analyse [decision] using only the attached evidence and any approved sources I name. The decision criteria are [criteria], the constraints are [constraints] and the cost of a wrong choice is [consequence]. First map the claims, disagreements, missing evidence and assumptions. Then compare the options, show how the recommendation changes under plausible assumptions and identify the evidence that would reverse it. Do not turn uncertainty into a score without explaining the judgement behind the score. Finish with a decision memo and a separate verification checklist for me.

Claude Opus 5 route

  1. Give Opus the complete source pack and ask it to separate facts, interpretations, stakeholder positions and unresolved questions.
  2. Use high effort as the default and constrain the output length separately so deep reasoning does not become an unnecessarily long memo.
  3. Ask it to argue the strongest case against its initial recommendation and expose which value judgement decides the remaining tie.
  4. Compare the result with Sonnet 5 to learn whether Opus reduces a real failure or only adds explanation and token use.

Starter brief

Help me make [decision] from this evidence pack. Separate direct evidence, interpretation, stakeholder preference and missing information. Compare [options] against [criteria] and show the trade offs without hiding them inside a single score. State the strongest case against your preferred option, the assumptions doing the most work and the evidence that would change the result. Keep the final memo to [length], but include a separate audit trail that points each consequential claim to the source and identifies the human judgement that remains.

Decision rule: Either model can support difficult synthesis. Sol offers a broad OpenAI tool route and flexible effort choices. Opus is a strong long context reasoning candidate. The recommendation should come from which model produces a more traceable decision with fewer consequential corrections on the same evidence.

The Human Bit checks

  • Set the criteria and their importance before the model sees the options.
  • Check whether the evidence represents affected people and not only available documents.
  • Separate factual uncertainty from a genuine value or risk trade off.
  • State the final decision and accepted downside in an accountable human voice.

Workflow

Synthesize a very large document set

The work spans many reports, policies, transcripts or technical documents and needs a coherent output without losing exceptions, version conflicts or source traceability.

GPT-5.6 Sol route

  1. Create a document inventory and remove obsolete, duplicate or irrelevant files before using the large context window.
  2. Ask Sol to build a source map and retrieval plan rather than immediately summarising the entire pack.
  3. Analyse in stages, with extracted findings and source references reviewed before the final synthesis is produced.
  4. Watch the 272,000 token pricing threshold and compare a retrieval based route when sending the whole pack repeatedly becomes wasteful.

Starter brief

These files form a source set about [subject]. Do not summarise them immediately. First inventory the files, identify dates, versions, authority, duplicates and conflicts, and propose how to answer [question] efficiently. Use the minimum relevant material for each finding. For every consequential point, cite the exact file and section. Distinguish direct statements, synthesis and unresolved ambiguity. Finish with [deliverable] plus a source audit that shows which documents were used, ignored or treated as superseded and why.

Claude Opus 5 route

  1. Use the million token context deliberately and define document roles, versions and the question before asking for synthesis.
  2. Ask Opus to preserve exceptions and contradictions and to keep interpretation separate from extracted text.
  3. Control visible answer length explicitly while allowing the model enough max tokens for thinking and tool work.
  4. Compare the accepted result with Sonnet on a representative subset before making Opus the default for every large pack.

Starter brief

Read this document set as one governed source pack. First identify each document's role, date, authority and relationship to other versions. Then answer [question] while preserving exceptions and contradictions. Point every material conclusion to the exact source location and say when a conclusion is interpretive rather than explicit. Keep the final output to [length], with a separate appendix containing the source map, unresolved conflicts and the passages a reviewer must inspect directly.

Decision rule: Both models publish roughly a million token context and large output capacity. Opus is a natural candidate for long context reading. Sol may fit better when the documents sit inside a wider tool, retrieval or deliverable workflow. Test source recall and traceability, not only whether the model accepts the upload.

The Human Bit checks

  • Remove stale versions and label authoritative documents before analysis.
  • Spot check the beginning, middle and end of the source set rather than only cited highlights.
  • Inspect exceptions, definitions and footnotes that can reverse a broad summary.
  • Keep qualified legal, financial or safety interpretation with the responsible specialist.

Workflow

Create a professional deliverable from mixed evidence

The output is not merely an answer. It is a document, report, spreadsheet or presentation that must survive review, editing and use in another application.

GPT-5.6 Sol route

  1. Use ChatGPT Work or an API tool workflow and provide the source material, template, audience and objective for the finished file.
  2. Approve the proposed structure and calculations before the model spends time polishing the deliverable.
  3. Ask for source notes, assumptions and unresolved items to travel with the output rather than disappear behind formatting.
  4. Open the deliverable in its real application and verify formulas, charts, layout, links and accessibility.

Starter brief

Create an editable [document, spreadsheet or presentation] for [audience] using the attached sources and template. The outcome is [decision or use]. First propose the structure, calculations and evidence plan for approval. After approval, create the deliverable without inventing missing content. Attach source references to every material claim or figure, preserve unresolved items visibly and include a review sheet for facts, formulas, visual hierarchy, accessibility and the action the audience is expected to take.

Claude Opus 5 route

  1. Use Claude or Cowork when the deliverable requires substantial source reading, careful narrative or coordinated file work.
  2. Constrain the scope and desired file characteristics explicitly so Opus does not expand the brief into adjacent work.
  3. Review the content and evidence before reviewing design, because polished formatting can make an unsupported claim feel settled.
  4. Measure whether Opus reduces corrections enough to justify it over Sonnet for this recurring deliverable.

Starter brief

Build an editable [document, spreadsheet or presentation] from the attached material for [audience]. The finished output must achieve [outcome] and follow [template or conventions]. First show the proposed narrative, structure and evidence mapping. Once approved, create the file, keep claims and figures traceable to their sources and mark gaps rather than filling them. Finish with a concise handover describing what was created, what needs human judgement and what must be checked in the final application.

Decision rule: The model choice matters less than the complete file workflow. Sol fits naturally with OpenAI Work and tool use. Opus fits naturally with deep source reading and Claude file or Cowork workflows. Compare the final editable artifact and the corrections needed to make it usable.

The Human Bit checks

  • Approve the structure and evidence before visual polish begins.
  • Open and test the file outside the AI interface.
  • Check formulas, linked data, chart scales, citations and accessibility separately.
  • Use human taste and organisational context to decide what deserves emphasis and what should be removed.

A practical chooser

Start somewhere sensible, then move when the real work gives you evidence.

Start with GPT-5.6 Sol

A flagship model inside OpenAI tools and products

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.

Move when: Move to Opus when the task remains weak after the OpenAI workflow is well specified, especially on long horizon code or source heavy reasoning.

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

Start with Claude Opus 5

Complex agentic coding or a long multi file task

Anthropic explicitly positions Opus 5 for complex agentic coding and provides detailed guidance for long horizon scope, subagents, self correction and review.

Move when: Move to Sol when the OpenAI coding harness, tools or task specific results produce a cleaner diff, stronger tests or lower correction cost.

Human check: The accepted unit is a reviewed change that fits the codebase, not the amount of autonomous activity the model completed.

Either can fit

A million token source pack

Both publish around a million tokens of context and 128,000 output tokens. The practical question is source recall, traceability, cost and the surrounding document workflow.

Move when: Switch when one model repeatedly loses exceptions, misattributes sources or needs more corrective prompting on a representative pack.

Human check: Clean and govern the source set first, because a large context window magnifies bad document hygiene as efficiently as good evidence.

Start with Claude Opus 5

A lower API output token rate between these two

The published base output rate is US$25 per million for Opus 5 and US$30 for Sol, while both list US$5 input.

Move when: Move when total task cost reverses the rate card because of reasoning volume, retries, tools, long context pricing or human correction.

Human check: Measure accepted results per dollar and hour rather than multiplying a headline rate by an imagined token count.

Either can fit

A difficult task with adjustable reasoning depth

Both expose meaningful effort controls. The correct setting is the lowest one that meets the task's review standard consistently.

Move when: Change model or effort when a controlled sweep shows a material difference in correctness, correction time or cost, not when the answer merely sounds more elaborate.

Human check: Keep prompt, inputs and scorecard constant across the comparison so effort is the variable actually being tested.

Start with Claude Opus 5

A migration from Claude Opus 4.8

Opus 5 is the named successor at the same base token rates, but thinking and tool behaviour introduce operational changes that require a real migration test.

Move when: Consider Sol or Sonnet when Opus 5 does not reduce failures enough to justify longer responses, more reasoning tokens or integration changes.

Human check: Rebaseline max tokens, effort, cost, tool availability, output length and refusal behaviour rather than treating the model ID as the only change.

Either can fit

Ordinary professional work at sustainable cost

Neither flagship should be the automatic first choice. GPT-5.5 Instant, GPT-5.6 Terra or Luna, and Claude Sonnet 5 may already meet the need faster and more cheaply.

Move when: Escalate only when the lighter route fails a consequential requirement or the cost of a mistake justifies a deeper pass from the start.

Human check: Write down the failure that earns escalation so capability does not become an expensive habit.

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 both 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?

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?

What this comparison does not prove

Useful guidance stays honest about access, testing and the conditions that can change the result.

  • The Human Bit has not completed a controlled internal GPT-5.6 Sol versus Claude Opus 5 test suite. This page separates official facts, one available independent observation and editorial decision guidance rather than presenting an invented winner.
  • 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.

Continue with the real work

Work playbook

Research a topic with reliable sources

Turn a broad question into a bounded, source-linked briefing you can open up, check and argue with.

Work playbook

Compare difficult options

Get the criteria, trade-offs and assumptions out into the open, and keep the final call with the person who has to live with it.

Work playbook

Understand a file or spreadsheet

Look inside an unfamiliar document or dataset, surface its quality problems, and check the number by hand before you rely on it.

Work playbook

Create a presentation from source material

Turn the notes, documents and data you already have into an editable first deck, while the argument and the evidence stay in your hands.

Product comparison

ChatGPT or Claude: start with the work, not the brand

A practical comparison of the two products through real work, including research, writing, files, ongoing projects, finished deliverables and the judgement that still belongs to you.

Agentic workflow comparison

ChatGPT Work or Claude Cowork: delegate the process without losing control

A detailed guide to longer AI work across files, apps, research and finished deliverables, with practical permission boundaries, approval points and recurring workflow examples.