GPT-6.1 Sol Pro vs GPT-6 Sol Pro: Same Rate Card, One Line Apart

Two weeks separate the releases and almost nothing separates the bills, which is why the matchup between GPT-6.1 Sol Pro and GPT-6 Sol Pro is short and unusually precise. Both names resolve to the same kind of thing — a base model plus reasoning.mode set to pro in the Responses API — so neither has its own identifier, its own rate card or its own surcharge. GPT-6.1 Sol Pro is GPT-6.1 Sol, released September 29, 2026, running in pro mode. GPT-6 Sol Pro is GPT-6 Sol, released September 22, 2026, running in the same mode. Both expose 1,050,000 tokens of context and 128,000 tokens of maximum output, both bill $2.00 per million input tokens and $10.00 per million output tokens, and both price the mode's extra work at those same standard rates. What actually differs is one line of the meter, one rung of the effort ladder, and how much independent evidence exists behind either configuration.

Both names are the same word for the same switch

Start with the mechanism, because it is the reason this comparison has so few rows. OpenAI sold Pro as a product in the GPT-5 era — gpt-5-pro, gpt-5.2-pro, gpt-5.4-pro, gpt-5.5-pro, o1-pro, o3-pro — each with its own identifier and its own premium meter, up to $150.00 input and $600.00 output per million tokens on o1-pro. Then the mechanism changed and the vocabulary did not. Pro became a value you set on the flagship, priced at the flagship's own rates, and OpenAI's deprecations page says so in its own words: when those old meters retire through October and December 2026, the recommended substitute is gpt-5.6-sol with reasoning.mode: pro — a model plus a mode, not a successor product.

The vendor's public API schema agrees. Search it for GPT-6-era identifiers and four turn up: gpt-6-astra, gpt-6-sol, gpt-6-luna and gpt-6.1-sol. No pro variant appears among them, and no pro variant appears on the price list either. What appeared, within hours of the 6.1 tier going live, were pro listings for the whole family in third-party catalogues — 6.1 Sol Pro, 6 Sol Pro, 6 Luna Pro, 5.6 Sol Pro — each one quoting its base model's card. That is a catalogue row being dressed with a name, not a vendor shipping a product, and it is why the two subjects of this comparison are a mode on two different base models rather than two deployments.

What the two configurations actually differ on

Here is every dimension that separates them, both sides on one line:

• What the name resolves to — gpt-6.1-sol with reasoning.mode: "pro" versus gpt-6-sol with reasoning.mode: "pro"; neither vendor nor catalogue publishes a -pro identifier for either
• Release date of the base model — September 29, 2026 versus September 22, 2026, seven days apart
• Input price — $2.00 per million tokens on both, so pro mode's extra input volume costs the same per unit on either tier
• Output price — $10.00 per million tokens on both, and this is the rate the mode's additional reasoning tokens bill at, since pro mode changes volume and not price
• Cached input — $0.10 per million on 6.1 Sol against $0.20 on 6 Sol; the only published line where the two rate cards disagree
• Cache writes — $2.50 per million tokens on both
• Long-prompt repricing — past 272,000 input tokens the whole request reprices at 2× input and cache rates and 1.5× output on both, which puts 6.1 Sol at $4.00 input, $0.20 cached, $5.00 cache write and $15.00 output per million, against $4.00, $0.40, $5.00 and $15.00 on 6 Sol — the cache discount survives the repricing intact
• Context and output ceiling — 1,050,000 tokens of context and 128,000 maximum output tokens on both
• Reasoning effort ladder — low, medium (the default), high, xhigh and max on 6.1 Sol, with none and minimal unsupported; the same ladder on 6 Sol with none still available
• Documented knowledge cutoff — April 30, 2026 on the 6.1 tier; we did not verify 6 Sol's on the pages we read, so treat that row as unsettled rather than as a difference
• Independent evaluation — a published third-party page for GPT-6 Sol, none at all for GPT-6.1 Sol, and no evaluation of a pro configuration for either

Read the list and the shape of the decision falls out. One row is a real published difference — the cached-input rate, which halves. One row is a capability you lose rather than gain — not being able to pair the mode with effort: none on the newer tier, which matters to anyone using that combination to keep latency down on routing-style tasks. Everything else is identical, including the repricing rule that catches any request that grows past 272,000 tokens.

The evidence gap, stated plainly

This is where the comparison stops being symmetric. GPT-6 Sol has a real third-party read: the neutral board this blog uses as its external reference scores it 47.5 on its Intelligence Index, ranked 12th in a 145-model sample, and our own routes carry a measured performance record for the standard configuration — a median latency near 1.24 seconds, roughly 65 tokens per second, and a 0.16% error rate across about 167 million tokens of traffic. GPT-6.1 Sol has none of that. The board's page for the 6.1 slug returns a 404 and the string does not appear in the live leaderboard, so every score you see quoted for it today originates with the vendor.

Two cautions on that, and both cut against over-reading what we do have. The measured latency figure belongs to the standard configuration; pro mode performs more model work before returning a single final answer, so it is slower by construction and that number does not transfer to a pro run. And no third-party board publishes a separate evaluation of a pro configuration on any model in this family — the board measures the configuration it measures and does not break the mode out. So the honest state of the record is a measured 6 Sol in standard mode against a vendor-described 6.1 tier, with the pro mode on both sides resting on the vendor's description alone.

Three cases, and what each one should do

• You already run pro mode on GPT-6 Sol and it earns its keep. Stay on gpt-6-sol for now and keep the workload you measured it on. The 6.1 tier halves the cached-input rate, which on a reused prefix is real money at volume, but the mode itself is documented against the 6.1 identifier in one place — the reasoning guide's worked example — and nowhere on that model's own page. Wait for the vendor's per-model documentation to catch up before moving a path you have already validated
• You have never measured pro mode at all. Start on the 6.1 tier in standard mode, then run one pro arm beside it at the same reasoning.effort. You get the cheaper cache line on the arm you will keep, the newer snapshot, and a measurement of the mode's multiplier on your own traffic rather than a borrowed expectation
• Your traffic is dominated by a stable prefix — instructions, tool schemas, a long system prompt. This is the one case where the 6.1 tier wins on price before any measurement, because the only differing published line is the one you pay most often. Do the arithmetic on your own prefix volume: at $0.10 against $0.20 per million cached tokens, the saving scales with how much of your input is a repeat

None of those three cases is settled by a benchmark, and that is the point. The mode is identical on both sides, the headline rate is identical on both sides, and the decision turns on a cache rate, an effort-ladder restriction, and whether you need someone else's number before you move.

Running both configurations from one key

The practical reason to run this comparison instead of reasoning about it is that the two arms differ by a model string and one parameter, so the experiment costs almost nothing to stand up. OrcaRouter routes openai/gpt-6-sol at OpenAI's own list price with nothing added — $2.00 input, $0.20 cached, $10.00 output, with the 272K tier published at $4.00 and $15.00 — and passes the vendor's repricing rule through as written rather than replacing it with a rule of its own. Because there is no markup on top of the vendor's card, a rate change or a cache-price change shows up on the route the day OpenAI publishes it, with no second contract to renegotiate. Neither the 6.1 tier nor a pro listing of it is on those routes today — we checked the public catalogue on September 30, 2026 and both openai/gpt-6.1-sol and openai/gpt-6.1-sol-pro return model not found — so the arm you can call right now is the 6 Sol one, in standard or pro mode, on the same endpoint and the same key you would use for the newer tier when it lands.

Run the three-arm test on your own hard requests and hold reasoning.effort constant so one variable moves at a time: gpt-6-sol in standard mode, gpt-6-sol with pro mode on, and — once it is routable — gpt-6.1-sol at the same effort. Read total billed tokens from the usage object rather than the response body, since the mode's extra reasoning lands in a field the response text never shows. The pro run against its standard twin gives you the multiplier on your traffic, which will not match anyone else's. Keeping the third arm as a regression test after the decision costs one string change, and a routing rule that sends the genuinely hard requests to the pro configuration while the bulk stays standard is a line in a routing DSL rather than a re-architecture — which is the version of this decision that survives the next model refresh.

Bottom line

Strip the names away and this is one configuration on two adjacent snapshots, seven days apart, sharing a rate card down to the repricing threshold. The newer snapshot halves the cached-input rate and gives up effort: none; the older one keeps that rung and comes with a third-party evaluation, a measured latency record and a pro mode the vendor documents against a model page rather than only in a guide example. If your traffic reuses a stable prefix at volume, the 6.1 tier is the cheaper default and you should say so on the cache line rather than on the headline. If your pro-mode path is already measured and working, there is no published evidence that justifies moving it this week. Either way, treat the two catalogue names as one switch on two base models — because that is the only reading that survives a look at the vendor's own price list.

The arm you can call today is the 6 Sol one, standard or pro, and it is listed in the public catalogue at OpenAI's list price with nothing added, on one endpoint and one key.