Claude Opus 5 vs GPT-5.6: Sol, Terra and Luna Compared

Should the agent you are wiring up this week call claude-opus-5 or gpt-5.6? The second name is not a model. It is an alias for one of three SKUs sitting on three different price cards.

Short answer: under 272,000 input tokens the two flagships sit within 7% on price; above it, Claude Opus 5 costs about half what GPT-5.6 Sol does. We ran all four models on the same four checks on July 24, 2026 and priced every axis against both vendors' docs.

Sol, Terra and Luna are one generation in three sizes

OpenAI shipped all three on July 9, 2026, and its model reference maps them onto the old size suffixes: Sol to "the unsuffixed tier of earlier GPT-5 families," Terra to "the mini model tier," Luna to "the nano model tier." Three SKUs of one generation, not three generations.

The bare gpt-5.6 string is an alias. OpenAI's changelog routes it to gpt-5.6-sol, so a config naming gpt-5.6 bills at the family's top rate.

What they share matters more than what they differ on. Identical on all three model pages:

• Context window 1,050,000 tokens; max output 128,000
• Knowledge cutoff February 16, 2026
• Text and image in, text out; no fine-tuning; no dated snapshots
• The same effort ladder and the same "Fast" speed rating

Price and the reasoning rating are the only documented differences. A "Default" tag sits on each page inside its own tier, so seeing it on Terra does not make Terra the family default.

Anthropic ships one SKU on the other side. Claude Opus 5 arrived on July 24, 2026 as claude-opus-5, a pinned snapshot with no date suffix, listed at $5 input and $25 output per million tokens, with a 1M window that is both the default and the maximum.

Coding: four mechanical checks, four clean passes

We sent four tasks with machine-checkable pass conditions through one OpenAI-compatible gateway, using the same user prompt for every model:

• Bugfix: repair kth_smallest, which both mutated the caller's list through an in-place sort() and carried an off-by-one index
• Strict JSON: fixed key order, self-consistent count and checksum
• Reasoning: the largest total unreachable with only 7-packs and 12-packs (the Frobenius number, 7×12−7−12 = 65)
• Refactor: add validation without changing the signature

All four models went 4/4: sorted() or a copy with the index moved to k-1, parseable JSON in the required key order, 65, and a clean refactor. This is one run per model and task pair, not a benchmark, and correctness did not separate them at this size.

Vendor-reported numbers carry the broader claims, none independently reproduced. Anthropic's launch materials put Opus 5 within 0.5% of Fable 5's peak CursorBench 3.2 score at max effort for half the per-task cost, a self-run relative figure. OpenAI's GPT-5.6 launch page returns 403 to automated fetches, so Sol's coding scores belong to whoever published them:

Hands-on verdicts diverge from the scoreboards. One developer comparing the two at High effort posted on July 24 that "GPT 5.6 Sol High is still better at coding than Claude Opus 5 High."

Anthropic's migration guidance adds a detail worth acting on: Opus 5 verifies its own work, so inherited prompts that append a verification step now push it into over-verifying and should be deleted.

Instruction-following: where the formats diverged

Two checks were instruction tests in disguise: the JSON task required items in lowercase alphabetical order with a checksum of 14 and no extra keys; the refactor demanded a raised ValueError plus zero comments and zero docstrings, which models routinely break by documenting their work. All four passed both.

Output shape did diverge. Sol and Luna wrapped the answer as LaTeX, returning oxed{65}, where Opus 5 and Terra returned a plain number. A regex pulling the last integer survives both forms; a parser expecting a bare numeral or a JSON field does not, and defaults are what you inherit when you swap claude-opus-5 for gpt-5.6 and change nothing else.

Token efficiency: 1,182 output tokens against Sol's 464

Two configuration defaults are the plausible drivers of the spread below, and neither is quality. Opus 5 ships with thinking on and effort defaulting to high in the API and Claude Code, so a bare request is already a reasoning request. GPT-5.6 defaults to medium.

Visible completion tokens across the four tasks, with cost at official output rates. One request per pair, no retries, default settings:

Read those costs with two limits. The gateway reported prompt tokens between 62 and 4,471 for the same user prompt on GPT models while Claude stayed inside 592 to 640, so the requests were not identical on the wire: no input-side figure is meaningful, and the gap cannot be attributed cleanly.

OpenAI's reasoning guide also withholds reasoning summaries unless you opt in, and GPT-5.6 bills those hidden tokens as output, so the three GPT costs understate the invoice. Correctness is the comparable signal here; tokens and latency are directional.

The chart also carries Opus 4.8 and Sonnet 5, Claude-side baselines from the same run. Opus 5 was slowest of the six at 24.6s across the four tasks, against Sol 20.3s, Terra 22.7s and Luna 18.4s. Opus 4.8 defaults to thinking off and finished in 11.8s, which is what makes the gap configuration rather than capability.

Artificial Analysis, evaluating Opus 5 on release day, described its effort setting as spanning a wider token-usage range than models from other labs, so its cost is more tunable than the rate card implies.

Price: the 272K cliff, in dollars

One published sentence decides the price half of Claude Opus 5 vs GPT-5.6. At the headline rate the flagships look like a rounding error apart: Opus 5 is $5/$25 per million tokens, Sol is $5 input and $30 output, making Opus 5 17% cheaper on output and identical on input. That holds up to 272,000 input tokens and no further.

All three GPT-5.6 model pages carry the same line: "Prompts with >272K input tokens are priced at 2x input and 1.5x output for the full request." It hits the whole request, not only the tokens above the line. Cross 272K by one token and Sol bills $10/$45 for the entire call, where Opus 5's 1M window stays at $5/$25 end to end.

Take one agentic request with 20,000 output tokens and vary the input, at OpenAI's published rates and Anthropic's standard rates, both read on July 25, 2026:

Below the boundary the flagships sit 7% apart. Above it Sol costs 1.96x Opus 5 at 400K input and 1.98x at 900K, so a hundred requests of the 400K shape is $250 against $490.

Terra is the shape worth noticing. Above 272K it pays Opus 5's identical $5 input rate, so their whole gap is the output line, $2.50 per million: five cents on this 20k-output request, at 300K and 900K alike, and scaling with output rather than input. The boundary is a discrete step in the rate card, not the slope an evenly spaced axis draws.

Batch tiers, tokenizers and where you buy

Discounted tiers preserve the ratio on that 400K request:

• Anthropic Batch API: Opus 5 at $2.50/$12.50, bringing the request to $1.25
• OpenAI Batch and Flex: 50% off standard, so Sol's long-context rate becomes $5/$22.50, or $2.45 for the same call

Per-token rates are not comparable across vendors. Anthropic's pricing documentation notes Claude models from 4.7 onward, Opus 5 included, use a newer tokenizer producing roughly 30% more tokens for the same text, the ratio varying by content and language. A document counting 400,000 GPT-5.6 tokens counts nearer 520,000 for Opus 5, moving that row's input line from $2.00 to $2.60. Still under Sol's $4.00, but tokenize your own prompts before committing.

Where you buy matters too. AIReiter resells Claude below list, with Opus 4.8 at $1.56/$7.76 per million tokens, around 69% under official; its catalogue tops out at Fable 5 and Opus 4.8, so Opus 5 is not callable there yet. Inside Anthropic's own lineup, Fable 5 at $10/$50 against Opus 5 at $5/$25 is the break-even question.

Context: both say 1M, one charges a flat rate for it

Opus 5's window is 1,000,000 tokens, simultaneously the default and the maximum, so there is no smaller-context variant to select by accident. All three GPT-5.6 variants carry 1,050,000, a 5% larger window. Max output is 128,000 on both sides, with one asymmetry: on Anthropic's Batch API an output-300k-2026-03-24 beta header lifts Opus 5's ceiling to 300,000.

Sol will accept a 900K-token prompt and charge the long-context rate for the whole request, leaving roughly 74% of its advertised window above the cheap rate. Capability at length is a separate question: Vellum's write-up puts Luna at 41.3% on MRCR against Sol's 91.5% and Terra's 89.6%, a reason to keep the nano tier off long-context work whatever it costs. Anthropic also gives Opus 5 a knowledge cutoff of May 2026, where all three GPT-5.6 model pages state February 16, 2026.

Reasoning controls: one ladder, two knobs

Opus 5 needs no beta header for any effort level, and thinking is on by default. Switching it off has a hard edge: thinking: {"type": "disabled"} is accepted only at effort high or below and returns a 400 at xhigh or max, which Anthropic flags as a breaking change from earlier models.

Those two mode values are all OpenAI's reasoning guide documents. The API exposes no ultra value, and the mechanism behind Pro mode is not described. The July 9 changelog lists persisted reasoning, max effort and Pro mode as additions shipping with the family.

The practical asymmetry: GPT-5.6 can stop reasoning entirely for latency-critical calls, which Opus 5 cannot do above high effort, while Opus 5 offers a wider dynamic range inside reasoning. One developer tracking both settled on a single cross-vendor ladder: Luna high, Sol medium, then Opus medium, high and xhigh.

Availability, rate limits and fine-tuning

Opus 5 is callable on the Claude API, Amazon Bedrock as anthropic.claude-opus-5, Google Cloud, Microsoft Foundry, claude.ai, Claude Code and Claude Cowork, and it is the new default on Claude Max. Anthropic's fast mode, at $10/$50 for roughly 2.5x throughput, exists only on the Claude API.

All three GPT-5.6 variants are OpenAI API products, and the free API tier carries neither Sol nor Terra. Sol's documented limits climb from 500 RPM at Tier 1 to 15,000 at Tier 5, Terra sharing that ceiling. None of the three supports fine-tuning or exposes a dated snapshot, so pinning a version means pinning the plain ID and accepting in-place updates.

What to point at what

Check input size first: it is the one axis with a published discontinuity on it. Caching, batch eligibility and output volume then move the total.

Under 272K the flagships are close enough that tie-breakers decide, and above it list-price economics favour Opus 5 over Sol by roughly 2x. That advantage does not extend to the whole family: Terra above the cliff costs $2.45 against Opus 5's $2.50 on the same request, so the long-context saving is a Sol comparison rather than a GPT-5.6 comparison.

• Claude Opus 5: large prompts, one flat rate across a 1M window, knowledge through May 2026, or inference that has to run on Bedrock, Google Cloud or Foundry.
• GPT-5.6 Terra: the volume default, since it undercuts Opus 5 at both ends of the range.
• GPT-5.6 Sol: when pro mode or none effort is the thing you need. It earns its premium on controls, not on any correctness edge we could measure.
• GPT-5.6 Luna: shallow high-volume work, kept off long-context tasks.

Claude Opus 5 vs GPT-5.6 resolves into one decision per input-size bracket, and both vendors publish where that boundary sits.

FAQ

Is Claude Opus 5 better than GPT-5.6 for coding?

On our four mechanical checks Opus 5, Sol, Terra and Luna all scored 4/4, so correctness did not separate them at that size, and developers running both at High effort disagree. Cost is firmer: above 272K input tokens Opus 5 runs about half the price of Sol.

What is the difference between GPT-5.6 Sol, Terra and Luna?

Price and a qualitative reasoning rating. OpenAI's docs map Sol to the old unsuffixed tier, Terra to mini and Luna to nano, at $5/$30, $2.50/$15 and $1/$6 per million tokens below 272K input. Context window, max output, knowledge cutoff, modalities and the effort ladder are identical on all three.

Which AI is better, GPT or Claude?

It splits by axis, and the widest published gap sits on one neither vendor advertises. benchlm.ai records an AA-Omniscience hallucination rate of 35.9% for Claude Opus 4.8 against 88.8% for GPT-5.6 Sol, while scoring Sol ahead on math, knowledge and agentic categories. Those are the closest published pairing; no aggregator carries Opus 5 or Terra data yet. If wrong answers cost more than missing ones, that ordering outranks any coding score.

Should I use GPT-5.6 Terra instead of Sol?

For most volume, yes. Practitioners routing by depth on r/codex report landing near Terra for 75% of work, Luna 15% and Sol 10%. Artificial Analysis argues the middle tier is dominated, since some Luna or Sol setting usually matches it at equal or lower cost, so test Terra against both neighbours before assuming the middle is safe.

Does GPT-5.6 have an ultra reasoning mode?

The API exposes reasoning.mode with two documented values, standard and pro, alongside reasoning.effort from none to max. Pro mode arrived in the July 9, 2026 changelog. No ultra value appears in OpenAI's reasoning guide or on the model pages.

Further reading

• The best Claude model for coding
• Fable 5 vs GPT-5.5