GPT-6 Sol Pro vs Claude Opus 5: The Effort Ladder Nobody Puts in the Table

GPT-6 Sol Pro and Claude Opus 5 are compared to each other constantly, and almost always at the wrong setting. The comparison that circulates is Claude Opus 5 at its top reasoning effort against GPT-6 Sol Pro at its top reasoning effort, which produces a three-point gap on the neutral index and a fivefold cost difference. Set both models to the configuration their own vendors ship as the default — and remember that "Pro" in the first name is a reasoning configuration, not a separate model — and the three-point gap disappears entirely. What remains is the cost difference, and it is the only part of the story that survives contact with a production bill.

This is not a trick of presentation. It is the direct consequence of two effort ladders that are not calibrated against each other, published by two vendors who benchmark against each other's older models.

What the two models actually are, before any comparison

GPT-6 Sol is OpenAI's mid-tier reasoning model, generally available since September 22, 2026, at $2.00 per million input tokens and $10.00 per million output tokens. There is no gpt-6-sol-pro model identifier in OpenAI's developer documentation — the page lists one Sol entry, a 1,050,000-token context window, a 922,000-token maximum input, a 128,000-token output cap, an April 20, 2026 knowledge cutoff, and a reasoning-effort ladder running none, low, medium, high, xhigh and max with medium as the default. "Pro" is a value of the reasoning mode, the same alias pattern the GPT-5.6 generation established and this one inherited. A third-party catalogue entry called GPT-5.6 Sol Pro does exist and now quotes $2.00 and $10.00 — GPT-6 Sol's numbers — which is a naming artefact, not a product.

Claude Opus 5 is a real, separately-priced model from Anthropic, generally available since July 24, 2026 at $5.00 input and $25.00 output per million tokens. Its context window is 1,000,000 tokens, its synchronous output cap is 128,000, and its own effort ladder — output_config.effort — runs low, medium, high, xhigh and max with high as the default. Thinking is adaptive and on by default, a first for the Opus family.

One more fact that shapes everything below, and that no existing comparison page carries: Anthropic's own lifecycle table lists Claude Opus 5 as Active (legacy), with a migration banner pointing to Claude Opus 5.5, which reached general availability on September 22, 2026 at $4.00 and $20.00. OpenAI's launch charts still benchmark against Opus 5, not 5.5. The model in the comparison is the previous-generation Opus.

The two price lines, line by line

Both are vendor lists — OpenAI's and Anthropic's own published numbers, passed through unchanged by the platforms that host them.

• Input — GPT-6 Sol $2.00 per million tokens vs Claude Opus 5 $5.00 per million tokens

• Output — GPT-6 Sol $10.00 per million tokens vs Claude Opus 5 $25.00 per million tokens

• Cache read — GPT-6 Sol $0.20 per million tokens vs Claude Opus 5 $0.50 per million tokens; both are a flat 10% of the uncached input rate

• Cache write — GPT-6 Sol $2.50 per million tokens at a single TTL vs Claude Opus 5 $6.25 at a five-minute TTL and $10.00 at a one-hour TTL; the longer TTL is an option OpenAI does not offer, and it is the tier most comparison tables accidentally quote, because it is the one that appears on the hosted catalogue cards

• Long-context handling — GPT-6 Sol reprices a request above its input threshold at a higher rate for the whole request; Claude Opus 5 applies no long-context surcharge at all, so a 900,000-token request bills at the same per-token rate as a 9,000-token one

• Batch — GPT-6 Sol has a batch endpoint at standard discounting vs Claude Opus 5's Message Batches API at 50% off both directions, and the Anthropic batch discount stacks with prompt caching

• Context window — GPT-6 Sol 1,050,000 tokens vs Claude Opus 5 1,000,000 tokens

• Maximum output — 128,000 tokens for both, with Claude Opus 5 raising that to 300,000 on the Message Batches API under the output-300k-2026-03-24 beta header

The batch-plus-caching stack is the least-discussed line on this list and the one that changes the most arithmetic. A 50% batch discount that combines with a cache read at a tenth of the input rate is a different offer from a 50% batch discount alone, and for long synthesis jobs — where the Anthropic 300,000-token batch output ceiling also applies — it closes a meaningful part of a gap that looks unbridgeable at list price.

The effort ladder is the actual comparison

Here is the number that should be in every one of these articles. On Artificial Analysis, whose harness is the only neutral one that publishes a full effort ladder for both models, Claude Opus 5 scores 51 at max effort and costs $5.86 per index task. At its default high setting it scores 48 and costs $3.61. GPT-6 Sol scores 48 at max effort and costs $1.06 — and 43 at high effort for $0.37.

Read those two lines together. Claude Opus 5 running at the effort setting Anthropic ships as the default lands on exactly the same index score as GPT-6 Sol running flat out. The gap that the launch coverage reported — three points — exists only if you compare each model at the top of its own dial, and the top of the dial is not where either model runs by default. Opus 5's advantage at max effort is real and it costs roughly five and a half times more per completed task to obtain.

Two honesty caveats belong attached to those figures, and both are missing from the pages that quote them.

• The index version moves. Opus 5's score has been published as 61, 63, 54 and 51 across successive revisions of the Artificial Analysis index since July. None of those is wrong; every one of them is wrong without its version label. The 51 above is the current board, and it is not comparable to a 61 quoted from a launch-day article.

• Effort ladders are not calibrated across vendors. A "high" on one model is not the same amount of thinking as a "high" on the other. Matching scores at nominally different settings is evidence about cost, not about a capability equivalence.

Where the independent record is thinner than it looks

Claude Opus 5 has eight weeks of third-party measurement behind it and a set of vendor-reported launch numbers that are clearly labelled as such — Frontier-Bench v0.1 at 43.3%, ARC-AGI-3 at 30.2%, GDPval-AA v2 at 1,861 Elo. Every one of those was measured against GPT-5.6 Sol or Claude Fable 5, not against GPT-6 Sol. There is no shared-harness result anywhere that puts Opus 5 and GPT-6 Sol head to head on Anthropic's own benchmarks, and Anthropic's system card concedes the model is not more capable overall than Claude Fable 5.

The independent record also carries a caveat in the other direction. On Artificial Analysis's AA-Omniscience evaluation, Opus 5's hallucination rate is 50%, up fourteen points from Opus 4.8 — the documented cause being that refusals fell from roughly 23% to 7%, so the model answers more questions and is wrong on more of them. Vals AI separately disclosed that Opus 5 and Fable 5 used Opus 4.8 as a refusal fallback during Terminal-Bench runs; reclassifying the nine affected passes as failures moves Opus 5 from 84.64% to 81.27%. These are not disqualifying findings, but a piece that argues Opus 5 is the more reliable choice needs them attached.

GPT-6 Sol's independent record is, this week, one number wide: the index score above, measured at max effort, eighteen months of model releases behind it in accumulated third-party testing. That asymmetry — eight weeks of scrutiny against one day — is the honest reason a buyer might still pay the fivefold premium, and it is a better reason than the three-point headline.

Where a routing layer changes the decision

Claude Opus 5 is on OrcaRouter's catalogue at $5.00 input, $25.00 output, a $0.50 cache read and a $10.00 cache write per million tokens, with a 1,000,000-token window, a 128,000-token output cap and both the /v1/chat/completions and /v1/messages endpoints — provider list pricing passed through with no markup on top, so an Anthropic price change is live on our side the same day it is live on theirs. The model card's cache-write figure is the one-hour TTL rate; Anthropic's five-minute tier is $6.25, and quoting one without saying which is how the two numbers end up looking like a contradiction.

GPT-6 Sol is not one of our routes, so nothing here is a claim about its price through us.

What a single endpoint actually buys in this matchup is the ability to hold both answers at once. The case for Opus 5 and the case for Sol are both effort-dependent, and effort is a per-request parameter, not a contract. A team that routes both models behind one key with automatic failover can send the work that needs Opus 5's max-effort ceiling to Opus 5 and the volume tier to Sol at medium effort, without a second integration or a second set of rate limits. The routing DSL composes that decision into the call rather than into a migration project — which matters here specifically, because the correct answer for this pair changes with the request, not with the quarter.

The decision rule

• Volume work at a known quality bar — GPT-6 Sol at medium or high effort. Forty index points at $0.25 per task is the cheapest credible line on this board, and the effort dial is a documented parameter, not a guess.

• Work that needs the top of the capability range and can pay for it — Claude Opus 5 at xhigh or max. Three index points over Sol's ceiling, at $4.88 to $5.86 per task, and the only one of the two with a 300,000-token batch output ceiling.

• Large-corpus batch jobs — Claude Opus 5, on the structural argument rather than the per-token one. No long-context surcharge, a batch discount that stacks with caching, and a one-hour cache TTL for prefixes that outlive a five-minute window.

• Anything where a wrong answer is expensive — neither, on the current record. Opus 5's hallucination rate moved up fourteen points at this release, and Sol's third-party record is one number wide.

• If the comparison you were shown was "Opus 5 max versus Sol max" — re-run it at default effort before you decide. That is where the decision actually gets made, and it is the one configuration the existing coverage never shows you.