GPT-6.1 Sol Pro vs GPT-5.6 Sol Pro: One Generation Apart, Half the Rate Card

Set the names aside for a moment and look at what each one resolves to in a request, because that is where this matchup is decided. GPT-6.1 Sol Pro is GPT-6.1 Sol — released September 29, 2026 — running with reasoning.mode set to pro in the Responses API, and it bills the extra work at the model's own $2.00 input and $10.00 output per million tokens. GPT-5.6 Sol Pro is the same construction one generation earlier: GPT-5.6 Sol, shipped July 9, 2026, running with reasoning.mode: pro, billing its extra tokens at $4.00 input and $20.00 output. Neither vendor publishes a -pro identifier for either tier, so there is no pro rate card to compare and no pro surcharge to weigh. What you are actually comparing is two base rate cards, three months apart, whose headline prices differ by a factor of two — and whose cache rates differ by a factor of four. The mode is identical. The meter underneath it is not.

Why the older Pro meter is still in this comparison at all

The reason GPT-5.6 Sol Pro keeps coming up as a peer rather than a predecessor is that OpenAI genuinely sold Pro as a product in the GPT-5 era, and those identifiers still sit on the price list with their own premium meters: gpt-5.4-pro and gpt-5.5-pro at $30.00 input and $180.00 output per million tokens on short context, stepping to $60.00 and $270.00 past 272,000 input tokens. Those are real, separately priced products, and they are the reason "Pro" reads as an expensive word. The GPT-5.6 generation changed the mechanism without changing the vocabulary — pro became a mode of the flagship rather than a premium model beside it, priced at the flagship's own rates. So the practical effect of moving from a 5.6-era pro configuration to a 6.1-era one is not "a cheaper pro model." It is the same configuration, on a cheaper base.

• What it is, on both sides — a model plus reasoning.mode: "pro", not a separately trained or separately priced deployment

• Identifier you send — gpt-6.1-sol versus gpt-5.6-sol, with the mode carried in the reasoning object

• Standard input price — $2.00 per million tokens versus $4.00, so pro mode's extra tokens cost half as much on the newer tier

• Standard output price — $10.00 per million tokens versus $20.00, the same halving applied to the side of the bill pro mode inflates

• Cached input — $0.10 per million on 6.1 Sol versus $0.40 on 5.6 Sol, a fourfold difference that matters most in exactly the agent loops pro mode is built for

• Cache writes — $2.50 versus $5.00 per million tokens, again halved

• Long-prompt repricing — both reprice the whole request past 272,000 input tokens, at 2× input and cache rates and 1.5× output

• Context and output ceiling — 1,050,000 tokens and 128,000 max output tokens on both, with maximum input of 922,000 documented on each

• Knowledge cutoff — April 30, 2026 on 6.1 Sol against February 16, 2026 on 5.6 Sol

• Reasoning effort — low, medium, high, xhigh, max with none and minimal unsupported on 6.1 Sol, against the same ladder plus none on 5.6 Sol

• Pro mode documentation — demonstrated by name on gpt-6.1-sol in OpenAI's reasoning guide, against a family-level statement covering GPT-5.6 and GPT-6 with no per-model eligibility list

Two rows in that list are worth more than the rest combined, and neither is the headline price.

The cache line is where the two generations stop being comparable

Pro mode's cost arrives as volume, not as a surcharge: OpenAI's documentation says it "aggregates the model work performed to produce the final answer and bills those tokens at the selected model's standard token rates," and the vendor does not publish the multiplier. That makes the cost of a pro configuration a function of two things you cannot look up — how much extra work the mode does on your tasks, and what your tokens cost — and only the second one is published. On that axis the 6.1 tier wins every row by a clean factor of two, and the cached-input row by a factor of four.

Do the arithmetic on a customer-support agent that replays a fixed instruction and tool-schema prefix on every turn. Send 200 million input tokens a month, 95% of them prefix hits, and the cached portion bills at $0.10 per million on 6.1 Sol against $0.40 on 5.6 Sol — $19 against $76 for the same prefix, before a single pro-mode token is counted. Now add pro mode on top and the gap widens rather than closes, because the mode's extra reasoning lands on the output side at $10.00 against $20.00 per million. The one row that does not halve is the long-prompt repricing rule, which is identical on both tiers and applies the same 2× and 1.5× multipliers — so a workload that regularly crosses 272,000 input tokens pays a premium on both sides of the comparison, just a smaller one on the newer tier.

There is also a trap that belongs to the mode rather than to either model, and it will bite a naive cost estimator. Setting reasoning.effort to none while pro mode is on returns an error instead of quietly falling back, and the reasoning tokens pro mode burns are reported in the usage object under output_tokens_details.reasoning_tokens while never appearing in the response body. Any client that estimates spend from the returned text will under-count a pro configuration every single time.

Where the older generation still has a case

Cheaper is not the same as better, and the 5.6 generation has three advantages that a price table does not show. The first is maturity: gpt-5.6-sol has been generally available since July 9, 2026, its behaviour is settled, and it is the model OpenAI's own deprecations page points at when it retires the older Pro meters — which means the migration path off those $180-per-million meters lands on the 5.6 tier, not the 6.1 one. The second is promotion: OpenAI states that GPT-5.6 Sol's promotional pricing is available at least through November 21, 2026, so the $4.00 / $20.00 pair is a floor with a date on it rather than a list price. The third is independent evidence, and it is the one that matters most if you cannot run your own evaluation — Artificial Analysis has a full evaluation of GPT-5.6 Sol and no page at all for the 6.1 tier.

That independent read is a cost story rather than an intelligence story, which is the useful part. On the neutral board's Intelligence Index, GPT-5.6 Sol scores 47 at maximum reasoning effort, ranked 13th in a 145-model sample, with a Coding Index of 77.4 at rank 3 and a GPQA Diamond of 94.1%. Its cost per index task sits near $2.00 against $1.06 for GPT-6 Sol at the same setting — roughly double — and that ratio is the entire argument for the newer tier in one number. The 6.1 generation has no comparable figure published as of September 30, 2026: the board's model page for the 6.1 Sol slug returns a 404 and the string does not appear in the live leaderboard. So the honest comparison today is a measured 5.6 generation against a vendor-described 6.1 one, and no amount of table-building closes that gap.

Running both configurations from one key

This is the rare comparison where the experiment costs almost nothing to set up, because the two arms differ by a model string and a parameter. OrcaRouter routes openai/gpt-5.6-sol at OpenAI's own list price with nothing added — $4.00 input, $0.40 cached, $20.00 output, with the long-prompt tier published at $8.00 and $30.00 — and routes openai/gpt-6-sol at $2.00 / $0.20 / $10.00 with its own 272K tier at $4.00 and $15.00. Because the markup is zero and the vendor's price list is passed through as written, a promotional change or a rate cut on either tier shows up on the route the day OpenAI publishes it, with no second contract and no per-model re-onboarding. One OpenAI-compatible endpoint and one API key cover both arms of the test, so the only work is choosing a task set and reading the usage object.

Run it three ways on the same set of your own hard requests, holding reasoning.effort constant so only one variable moves: gpt-5.6-sol in standard mode, gpt-5.6-sol with pro mode on, and gpt-6.1-sol at the same effort. Compare task success, wall-clock latency and total billed tokens, reading the reasoning-token field rather than the response body. The pro run's token total against its standard twin is your multiplier on your traffic — it will not match anyone else's, because the design intent is that the extra work scales with how hard the request is. The 6.1 run is the same request body with one string changed, so keeping it as a regression test after the decision costs nothing.

Two things to hold in mind before you draw the conclusion. If the 6.1 identifier rejects the pro parameter rather than returning a result, that is itself a finding and you have learned it before it touched anything user-facing. And if the arm you are comparing against is the currently routable gpt-6-sol rather than a 5.6 configuration, remember the base rate cards are identical between 6.0 and 6.1 — the difference between those two is the cached-input line and the vendor's reported task results, where the difference between 5.6 and 6.1 is the whole rate card.

FAQ

Is GPT-5.6 Sol Pro a separate model with its own premium price? No — and this is the trap the name sets. OpenAI's price list carries genuine premium Pro meters, but they belong to earlier generations, at $30.00 input and $180.00 output per million tokens and above. For the GPT-5.6 family the vendor sells the flagship and exposes pro as a value of reasoning.mode in the Responses API, billed at that flagship's ordinary rates. A pro listing dated to the 5.6 generation and quoting $4.00 and $20.00 is quoting the flagship's card, which is how you can tell there is no second meter.

Which of the two should I put a hard, low-volume workload on? Neither answer is free, so the decision turns on what you can measure rather than what you can read. The newer tier is half the price on every published line and four times cheaper on cached input, but its pro mode has no independent evaluation behind it and its model page does not mention the mode at all. The older tier has a settled GA history and a full third-party evaluation, and costs twice as much per token. If your task set is cheap to run, the answer is whichever arm wins on your own requests at equal effort; if it is not, the older tier is the one whose behaviour you can at least check against someone else's numbers.

Does pro mode change what the model is good at? No. It performs more model work before returning a single final answer, which can improve reliability on difficult tasks and increases both latency and token usage — the weights are the same either way. If the metric you are trying to move is an accuracy or hallucination figure rather than a task-success rate on genuinely hard problems, the independent evidence on this family points at the model generation and the effort setting as the more reliable levers, not the mode.

The short version, and the one worth carrying out of this comparison: the pro mode is the constant and the rate card is the variable. Between these two configurations nothing about how the mode works has changed — the same aggregation of model work, the same standard-rate billing, the same unpublished multiplier, the same error if you try to pair it with effort: none. What changed in three months is that every published line of the meter underneath it halved, and the cached-input line quartered. That makes the newer tier the default choice on price for anyone already running a pro configuration, and it makes the older tier the default choice on evidence for anyone who needs a third-party number before moving. Neither of those is a reason to treat the names as two products.

Both arms of the test run from one OpenAI-compatible endpoint with the vendor's price list passed through as written, no markup added, and automatic failover between providers.