Claude Opus 5 vs GPT-5.6 Sol: Coding, Cost & Context

Anthropic shipped Claude Opus 5 on 24 July 2026 as its model "for complex agentic coding and enterprise work." OpenAI's GPT-5.6 Sol is the flagship of the Sol / Terra / Luna family and sits at a slightly lower sticker price. On paper the two look like near-twins: both carry roughly a million tokens of context, both cap output at 128K, both are reasoning models with an effort dial.

The differences that actually move a bill or a build decision are less obvious than a benchmark chart. They are: what happens to your rate card when a prompt gets long, how each vendor prices a cache read, which effort level each model defaults to, and how much of the published benchmark gap survives an identical harness. This page walks those, using only numbers pulled from Anthropic's and OpenAI's own documentation today.

What does each model actually cost?

Both vendors publish a full rate card rather than a single headline number. Here they are side by side, per 1M tokens, at standard (non-batch, non-fast) rates.

On short prompts, Sol is about 20% cheaper on both sides of the ledger. Both vendors price a cache read at exactly 10% of their own base input rate, and both discount batch work by 50%, so the ratio between them holds across every billing mode — right up until a prompt crosses 272K tokens.

Two footnotes worth knowing. Sol's $4 / $20 is promotional pricing, which OpenAI states is available "at least through November 21, 2026." And Anthropic's fast mode — a research preview that trades cost for output speed — is first-party Claude API only; it is not available on Bedrock, Google Cloud, or Microsoft Foundry.

Why does GPT-5.6 Sol get more expensive past 272K tokens?

This is the single biggest structural difference between the two, and most comparison pages miss it because it never shows up in a headline price.

OpenAI splits its rate card into short context and long context. A GPT-5.6 Sol request with more than 272,000 input tokens is billed at 2x input and 1.5x output for the entire request — not just the tokens past the threshold. Sol's effective rate jumps from $4 / $20 to $8 / $30.

Anthropic does not do this. Its documentation is explicit that Claude 4.6 and later models "include the full 1M token context window at standard pricing," and that "a 900k-token request is billed at the same per-token rate as a 9k-token request." Prompt caching and batch discounts apply at standard rates across the whole window.

The practical consequence: the cheaper model flips depending on how long your prompts are.

If your workload is chat-shaped — short prompts, many of them — Sol's sticker price wins and the threshold never fires. If your workload is repository-shaped, document-shaped, or a long agent trace that accumulates tool results, you will cross 272K regularly, and Opus 5 becomes the cheaper model on input by a comfortable margin. Work out which side of that line your median request sits on before you compare anything else.

How do the context windows and output limits compare?

Close enough to be a wash on paper, with one asymmetry at the top end.

Three of these rows matter more than they look. Opus 5's knowledge cutoff is about three months later than Sol's, which shows up on questions about tooling and library versions from spring 2026. Opus 5 supports up to 300K output tokens on the Message Batches API with a beta header, which is the one place either model breaks the shared 128K ceiling. And the two models default to different effort levels — Opus 5 to high, Sol to medium.

That last one quietly invalidates a lot of casual comparisons. If you benchmark both models with no effort parameter set, you are comparing Anthropic at high against OpenAI at medium, and paying accordingly. Set effort explicitly on both sides before you draw any conclusion about either quality or cost.

Sol also supports an effort level Opus 5 does not: none. If you have a genuinely non-reasoning workload — classification, extraction, routing — that is a real lever on the OpenAI side.

Which model is better at coding on an identical harness?

Vendor benchmark charts are run on vendor harnesses, which is why they rarely reproduce. The more useful number comes from Vals AI, which runs SWE-bench Verified on a minimal bash-tool-only agent harness — every model gets access to bash and nothing else, so the scaffolding is held constant across the field.

On that leaderboard, last updated 1 September 2026 across 86 evaluated models:

Claude Opus 5 holds the top position. But read the rest of the board before you treat that as decisive: seven of the 86 models evaluated score 95% or better, and Vals has archived the benchmark on the grounds that performance on it has saturated — new releases are no longer being tested against it.

That is the honest state of frontier coding benchmarks in September 2026. On discrete, well-scoped "fix this GitHub issue" tasks, the top of the field is separated by fractions of a point, and an open-weights model sits within a point of the leader. A benchmark that can no longer separate its top seven entrants should not be the thing that decides your model.

What that means practically: run the comparison on your own tasks. Benchmarks this saturated tell you which models are in the running, not which one to ship. If you are building the agent scaffolding those evals run inside, the AI coding agents guide covers the tooling layer.

How does Claude Opus 4.8 fit — is it still worth running?

Plenty of teams standardised on Opus 4.8 before Opus 5 shipped and are asking whether the upgrade is worth the migration. The pricing answer is unusually clean, because Opus 4.8 and Opus 5 cost exactly the same: $5 input, $25 output, $0.50 cache read, $2.50 / $12.50 on batch.

Same price, 8.4 points of same-harness SWE-bench Verified between them. There is no cost argument for staying on 4.8 — only a stability one, and Anthropic commits to keeping Opus 4.8 available rather than retiring it out from under you.

Two breaking changes make the migration non-trivial, though, and both bite code written against 4.8. On Opus 5, thinking is on by default — a request that omits the thinking parameter now thinks, where on 4.8 it did not. Since max_tokens caps thinking and response text together, a workload with a tightly sized max_tokens can start truncating mid-answer after nothing more than a model-string swap. And disabling thinking is only permitted at effort high or below; pairing thinking: disabled with xhigh or max returns a 400. Audit both before you flip the string. Full detail is in the Claude Opus 5 launch guide.

Where does GPT-6 Astra leave this comparison?

Astra launched on 3 September 2026 as OpenAI's most capable model, initially for enterprises in its Trusted Access Program with broader API and plan access following. It carries a 1,050,000-token context window, 128K max output, a 30 April 2026 knowledge cutoff, and effort levels from low to max — it does not support Sol's none setting.

It is priced a full tier above everything discussed here: $10 input / $50 output per 1M tokens on short context, rising to $20 / $75 above 272K input tokens. That is double Opus 5 on input and double on output, and 2.5x GPT-5.6 Sol on both.

OpenAI's own framing is worth quoting because it cuts against the sticker price: Astra "achieves stronger results while using substantially fewer output tokens — delivering a lower estimated API cost per task than earlier models despite its higher per-token pricing." Whether that holds on your workload is an empirical question about your token mix, not something you can read off a rate card.

Astra does not change the Opus 5 vs Sol decision, because it is not competing for the same budget. It changes the question above it: whether a task is valuable enough to justify a model that costs 2-2.5x the current flagship tier. The GPT-6 Astra vs Claude Opus 5 comparison takes that one on directly, and GPT-6 Astra vs GPT-5.6 Sol covers the within-OpenAI upgrade path.

Where does each model sit in its own vendor's lineup?

Neither of these is the top of its family, which matters if you are shopping by capability rather than by name.

On the Anthropic side, as with OpenAI's three-tier structure, Opus 5 is the middle of a four-model lineup. Claude Fable 5.1 sits above it at $10 / $50 for demanding reasoning and long-horizon agentic work — Anthropic's guidance is to reach for it "when your evals on Claude Opus 5 at higher effort still fall short." Claude Sonnet 5 sits below at $2 / $10, and Claude Haiku 4.5 at $1 / $5 with a 200K context window.

One pricing note that landed quietly: Claude Sonnet 5's $2 / $10 rate was announced as introductory pricing through 31 August 2026, and Anthropic has confirmed it is now the standard price — the scheduled increase to $3 / $15 on 1 September 2026 will not occur. If you costed a Sonnet 5 workload against a future $3 / $15, redo that model.

On the OpenAI side, Sol is the flagship of GPT-5.6, with Terra at $2 / $12 and Luna at $0.20 / $1.20 beneath it — and now Astra above the whole family.

Anthropic's own recommendation for anyone who does not want to run a bake-off: "If you're unsure which model to use, start with Claude Opus 5 for most workloads."

Which model should your team build on?

A decision rule that reflects what is actually verifiable in September 2026:

• Short prompts, high volume, cost-sensitive: GPT-5.6 Sol. It is ~20% cheaper across input, output, cache, and batch, the 272K threshold never fires, and its none effort level is a real lever on non-reasoning work. Note the promotional rate runs at least to 21 November 2026.
• Long prompts, big repositories, long agent traces: Claude Opus 5. Flat pricing across the full 1M window beats Sol's doubled input rate above 272K, and the gap widens the longer your context gets.
• Discrete coding tasks: either, honestly. Opus 5 tops the same-harness SWE-bench Verified board at 97.0%, but seven models clear 95% and the benchmark is saturated. Run your own eval.
• Already on Opus 4.8: upgrade. Identical price, 8.4 points of same-harness SWE-bench. Budget an afternoon for the thinking-on-by-default and effort-cap breaking changes.
• Genuinely at the frontier of what models can do: look above both — Claude Fable 5.1 or GPT-6 Astra. Both cost $10 / $50 on short context; Astra doubles above 272K, Fable 5.1 does not.

The one thing not to do is pick on a benchmark delta. The public coding evals no longer separate the top of this field, the pricing difference between Opus 5 and Sol is smaller than the difference either model's effort setting makes to your bill, and the largest single cost variable on this page — the 272K threshold — depends entirely on the shape of your prompts rather than on either model's quality.

Whichever you standardise on, the harder problem is usually the engineers wiring it into something that ships. Hire vetted remote developers through Codersera to build the evals, agent scaffolding, and integrations that turn a model choice into working software.

FAQ

Is Claude Opus 5 or GPT-5.6 Sol cheaper?

It depends on prompt length. Below 272K input tokens, GPT-5.6 Sol is cheaper at $4 / $20 per 1M against Opus 5's $5 / $25 — about 20% less across input, output, cache reads, and batch. Above 272K input tokens, Sol jumps to $8 / $30 for the whole request while Opus 5 stays flat, making Opus 5 the cheaper model on input.

Does Claude Opus 5 beat GPT-5.6 Sol on coding?

On Vals AI's SWE-bench Verified leaderboard, which runs every model on an identical bash-tool-only harness, Claude Opus 5 holds the top score at 97.00%. That result is worth less than it sounds: seven of the 86 models evaluated score 95% or better, and Vals has archived the benchmark because performance has saturated. Run a head-to-head on your own tasks.

Is Claude Opus 4.8 still worth using instead of Opus 5?

There is no cost reason to stay. Opus 4.8 and Opus 5 are priced identically at $5 input / $25 output per 1M tokens, and Opus 5 scores 97.00% against 4.8's 88.60% on Vals' same-harness SWE-bench Verified. The only reason to hold is migration risk: Opus 5 turns thinking on by default and rejects disabled thinking above high effort.

What is the 272K token threshold on GPT-5.6 Sol?

OpenAI prices GPT-5.6 Sol in two tiers. Requests with more than 272,000 input tokens are billed at 2x input and 1.5x output for the entire request, taking Sol from $4 / $20 to $8 / $30 per 1M tokens. Claude Opus 5 has no equivalent surcharge — its full 1M context window bills at one flat rate.

Is GPT-5.6 Sol's promotional pricing still available?

Yes. OpenAI's pricing page states that GPT-5.6 Sol's promotional pricing is available at least through 21 November 2026. The current $4 input / $20 output per 1M tokens on short context is that promotional rate, and batch ($2 / $10) and fast mode ($8 / $40) scale from it.

How does GPT-6 Astra compare to Claude Opus 5 on price?

GPT-6 Astra, launched 3 September 2026, costs $10 input / $50 output per 1M tokens on short context and $20 / $75 above 272K input tokens. That is double Claude Opus 5's $5 / $25 on short prompts and up to four times its input rate on long ones. OpenAI argues Astra's lower output-token usage narrows the gap on cost per completed task.

Do Claude Opus 5 and GPT-5.6 Sol default to the same reasoning effort?

No, and this trips up benchmarks. Claude Opus 5 defaults to high effort; GPT-5.6 Sol defaults to medium. Comparing them with no effort parameter set means comparing different amounts of reasoning compute at different costs. Set effort explicitly on both before drawing conclusions. Sol also supports a none level that Opus 5 does not.

Which has the larger context window?

GPT-5.6 Sol, marginally, at 1,050,000 tokens against Claude Opus 5's 1,000,000. Both cap synchronous output at 128,000 tokens. Opus 5 goes further in one place: it supports up to 300,000 output tokens on the Message Batches API behind a beta header.