This page compares two models on one job: turning a term sheet into clause-level draft language. It covers drafting to a stated fallback position, flagging a term the source never settles, spotting a defined term used inconsistently, and the prose quality.
Jul 30, 2026 · 12 min read
Claude Opus 5 is the default when a missed term is the expensive mistake. GPT-5.6 Sol is the better stylist and the cheaper run, which makes it a second pass rather than a first draft.
This page rests on stronger evidence than most model comparisons, because one independent benchmark tested both of these exact versions on contract drafting. Opus 5 completed 61.8 percent of its 34 drafting tasks fully correctly against 44.1 percent for Sol, where a single missed requirement fails the task1. That is close to how a real drafting instruction behaves: a clause that drops the fallback is wrong even if every other line is perfect.
Sol wins the parts a reader notices first. It scored 2.75 out of 3 for usefulness against 2.47, and the benchmark describes its drafts as clean and final-looking1. Term-sheet work has an asymmetric risk, though. Awkward wording is visible on the page, while a quietly invented cure period or a dropped fallback is not. Use Sol where the bargain is already locked and the job is language.
Your risk is a clause that reads well and changes the deal. Start from the model with the better checklist score and read the flags before the prose.
Draft with one model and polish with the other. The cost gap is real, roughly four times per task, so the split is cheaper than running the flagship twice.
Ask for bracketed markers on anything missing or contradictory, then handle those rows first. A model that drafts through a conflict looks finished but is not.
Structured output can hold a source-to-clause ledger that a script checks. Reject any clause with no source mapping rather than reviewing prose by hand.
This page compares the two models through their API in one neutral setup, on the three failure-sensitive parts of drafting from a term sheet.
Those parts are turning a commercial position and its stated fallback into precise clause language, marking a term as absent instead of filling the gap with plausible boilerplate, and noticing when a defined term is used inconsistently across the source material. They fail in different ways, which is why one verdict for all three would be less useful than a split.
Contract management platforms, clause libraries and document comparison tools are left out on purpose. They belong to the app around the model, so the same model behaves differently in a chat product, through the API, or inside a workspace. Judging them here would compare wrappers rather than drafting.
The model facts that actually affect drafting from a term sheet. Tool features are left out, since they change with the app around the model.
Figures from Anthropic and OpenAI documentation, checked July 30, 2026. Anthropic's pricing page confirms standard rates apply across the full context window for Claude 4.6 and later models, which covers Opus 5, so there is no separate long-context tier on the Claude side to budget for.
Read the evidence column closely. The drafting, usefulness and cost rows are measured on both exact versions, while the two reliability rows are directional: the missing-term row has no per-model breakdown at all, and the conflict row's only named leader is an older Claude version, not Opus 5.
Better-choice calls map to what the source actually evaluated. The benchmark is 34 drafting tasks, English-language and skewed to US and UK practice, single-turn, at default reasoning settings, graded with one model as judge, and it does not test repeated runs.
Score both models on term sheets your team has already turned into signed agreements, because you need a known answer to grade against. Then judge what a lawyer actually pays for: correct terms, visible gaps, flagged conflicts and less editing.
Include a term sheet with a primary position and a fallback, one missing a commercially important term, one using a defined term inconsistently, one that conflicts with the precedent, and one clause needing several linked definitions and cross-references.
Identical prompt, term sheet, precedent, drafting instructions and output limit on both models, with reasoning settings as close as the two APIs allow. Note where they cannot match, because that asymmetry shows up in the result.
The public benchmark runs each task once and does not measure variance, so repeat the matters that matter. Test in the API configuration the team will deploy, since chat products add their own system prompts and document handling.
Check the primary position, the exact fallback, every number, date, threshold, party and exception, then the flags for missing and conflicting terms. Remove the model names and have two lawyers score independently. A polished clause that changes the bargain fails.
Only one source here tests both exact versions against each other. Here is what each one helps judge and how much weight it deserves.
Both models are new: Sol became generally available on July 9, 2026 and Opus 5 shipped on July 24, 2026. Exact-version legal evidence is thin and the leaderboard does not publish individual prompts or outputs, so treat the figures as the best available rather than settled.
The two models need different instructions, and one rule binds both: never ask for finished clauses in the same call that first reads the term sheet.
Claude Opus 5 does best with a narrow scope, an explicit length and a clear rule for genuine ambiguity. Anthropic notes it can run longer than asked or widen a task beyond its requested scope unless constrained, and that it tends to check its own work without being told to repeatedly8. So cap the length, name the clause form, and give it a bracketed flag to use instead of a guess.
GPT-5.6 Sol needs a visible grounding step before any prose. Its failure mode is not weak writing, it is writing confidently past a missed requirement or an unflagged conflict1. Ask for a structured source ledger first, mark every item as supported, missing or conflicting, and only then let it draft from the supported rows. Structured output makes that ledger machine-checkable, and OpenAI's own model guidance is the place to check the current prompting advice5, 9.
A Claude Opus 5 prompt: bounded scope with flags
Draft the requested clauses using only the term sheet
and the precedent. The term sheet controls.
Apply each stated fallback exactly. Add no commercial terms.
If information is absent, insert [MISSING TERM: ...].
If sources conflict or a defined term is inconsistent,
stop that clause and insert [CONFLICT: ...].
Return three things, in this order:
the source issue list
the clause draft
a clause-to-source table
Keep commentary brief and stay inside the requested scope.A GPT-5.6 Sol prompt: ledger before prose
Stage 1 only. Return a structured source ledger listing
every party, amount, date, threshold, condition, exception,
defined term, primary position and fallback.
Mark each row SUPPORTED, MISSING or CONFLICTING.
Do not draft anything yet.
Stage 2, after the ledger is approved: draft only from
SUPPORTED rows. Use a bracketed flag for everything else.
After each clause, cite the ledger rows it implements.
Do not resolve a conflict yourself.The dangerous failure is the one that survives a read, because it looks finished. The useful question is what to change in the prompt or the workflow.
One question first. Is the bigger risk a wrong bargain or an untidy draft? Then follow the branch that matches most of your matters.
A starting point, not a rule. Score both on term sheets you have already closed.
If the expensive mistake is an incorrect bargain, an omitted fallback, an invented term or an unflagged inconsistency, choose Claude Opus 5 and still require a clause-by-clause check against the source ledger1. If the commercial position is already normalised and the remaining job is polished, well-structured language at a lower cost per run, choose GPT-5.6 Sol and forbid substantive changes1.
If the term sheet and the precedent conflict, start with Opus 5 and have a person resolve the conflict before the final draft. If defined terms are messy, use Opus 5 for the first issue pass and then run deterministic checks on capitalisation, definitions and cross-references, because that part does not need a model at all.
For a commercially important agreement, the strongest available workflow is Opus 5 for the first draft and issue list, then a separately prompted review pass or a lawyer comparing every clause with the ledger before anything circulates. And if the question is whether the clause is enforceable, neither model is the decision-maker: it prepares a draft and a list of issues, and a lawyer decides10.
One limit applies to Playgram rather than to the models. A team whose matters fall under a rule requiring the drafting to stay inside infrastructure it controls itself needs a self-hosted setup, and Playgram is a hosted workspace, so that specific requirement calls for something else.
Claude Opus 5 is the better default for drafting from a term sheet, because the exact-version evidence favours it on substance. GPT-5.6 Sol is the better stylist and the cheaper run, and its polish is exactly why its drafts need a stricter source check.
The evidence has real limits worth stating plainly. The decisive benchmark is 34 drafting tasks, English-language and skewed to US and UK practice, single-turn, run at default reasoning settings, graded with one model as judge, and it publishes neither the prompts nor the outputs1. The vendor evaluations use different task sets and configurations, so their numbers are not comparable with each other6, 7. Opus 5 is days old, so prices and rankings may move.
The safest final step is to test the shape of your own matters, not a generic prompt from the internet. A fair test needs the same setup for both models: the same term sheet and precedent, the same prompt and the same place to run them, so the result reflects the models and not the tool around them. In practice that is harder than it sounds, since most teams end up running one model in one app and the other in a different one, on two separate subscriptions, which tilts the comparison before the first clause comes back. The cleaner the setup, the more the difference you see is really Claude Opus 5 vs GPT-5.6 Sol, and not just which one happened to be easier to reach that day.
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