GPT-6 Sol vs DeepSeek V4 Pro: A Twelve-Point Index Gap for Four Times the Price

Here is the arithmetic nobody puts on a launch page. DeepSeek V4 Pro scores 36 on Artificial Analysis's Intelligence Index. GPT-6 Sol scores 48. GPT-6 Sol costs $2.00 per million input tokens and $10.00 per million output. DeepSeek V4 Pro costs $1.32 and $3.96 on the same board — and its own published rate card, converted, comes to roughly $0.42 and $0.87. So the twelve-point gap between GPT-6 Sol and DeepSeek V4 Pro is bought at somewhere between one and a half and four times the price depending on whose rate card you read, and the model on the cheaper side is the one you can download and run yourself.

That is the entire decision, and it is genuinely closer than the scoreboard suggests. DeepSeek V4 Pro shipped on August 13, 2026 as an MIT-licensed mixture-of-experts model with 1.6 trillion total parameters and 49 billion active per token. GPT-6 Sol shipped on September 22, 2026 as a closed-weight frontier model at half the price of its own predecessor. One is a commodity you can host; the other is a capability you rent. Both carry a million-token context window. Both are reasoning models. Neither is obviously the wrong purchase.

The number that decides this

Start with the thing both vendors agree on: context. DeepSeek V4 Pro carries a 1M context window with a 384K maximum output. GPT-6 Sol is listed at 872,000 tokens by Artificial Analysis against a 1.05M ceiling with a 128K output cap elsewhere in OpenAI's materials. DeepSeek gives you three times the output room and a comparable input window.

Then the thing only one of them has: a long-context repricing cliff. GPT-6 Sol's $2/$10 holds below 272,000 input tokens. Above that, the entire request reprices — input roughly doubles to $4 and output rises by half to about $15. DeepSeek V4 Pro has no equivalent step; its published rates apply across the window, with a peak and off-peak schedule introduced on August 17 that changes the price by time of day rather than by context length.

Put those together and the comparison inverts at the top end. A DeepSeek V4 Pro request carrying 400K tokens of context costs its normal rate. The same request on GPT-6 Sol costs roughly double the headline. If your workload is long-context by nature — document analysis, large-repo agents, extended transcript reasoning — the effective price gap between these two models is much wider than $2 against $1.32 suggests.

Where DeepSeek V4 Pro is genuinely competitive

DeepSeek's agentic and coding results are not a budget compromise. On the numbers the vendor published:

• Terminal-Bench 2.1 — DeepSeek V4 Pro 87.9

• Toolathlon-Verified — DeepSeek V4 Pro 74.1

• DeepSWE — DeepSeek V4 Pro 62.7

• SWE-bench Verified — DeepSeek V4 Pro 80.6

• AA Intelligence Index — DeepSeek V4 Pro 36, ranked 8th of 114 measured models

• Output speed — DeepSeek V4 Pro 67.8 tokens/sec

• License — MIT, open weights, self-hostable

The Index ranking deserves a second look. Thirty-six points places DeepSeek V4 Pro eighth out of 114 models on the board, which is a strong position for a model with downloadable weights and an MIT license. The gap to GPT-6 Sol is real, but it is a gap between "very good" and "frontier," not between "usable" and "not."

The MIT license is the part with no GPT-6 Sol equivalent. If your constraint is data residency, air-gapped deployment, a per-token cost that goes to zero at high utilization, or simply not wanting a vendor deprecation to be a migration event, DeepSeek V4 Pro is the only one of these two that answers the question. GPT-6 Sol is API-only and will remain so.

Where GPT-6 Sol pulls away

Twelve Index points is a wide margin at this end of the table. GPT-6 Sol ranks 18th of 212 measured models against DeepSeek's 8th of 114, and it reaches that position with a set of vendor-published agentic results that DeepSeek does not contest:

• AutomationBench — GPT-6 Sol 33.2% at $0.27 per task

• Agents' Last Exam — GPT-6 Sol 56.4%

• DeepSWE v1.1 — GPT-6 Sol 68.8%

• OSWorld 2.0 — GPT-6 Sol 60.5%

• Output speed — GPT-6 Sol 104.4 tokens/sec, against DeepSeek V4 Pro's 67.8

Those are vendor-reported and OpenAI ran its own harness, so treat them as claims rather than measurements. Two caveats are worth carrying forward. OpenAI used published competitor scores in several rows instead of re-running the competitor's model, and its Agents' Last Exam chart point for Claude Fable 5.1 excludes Opus 5 fallbacks that fired on roughly 40% of tasks. Neither caveat touches the DeepSeek comparison directly, but they are the reason to hold the whole table loosely.

The independent read is more measured than either vendor's. Artificial Analysis found GPT-6 Sol roughly halves cost per task against GPT-5.6 Sol while producing a mix of gains and regressions — including regressions on GDPval-AA v2.1. A model that trades capability in some places for a large cost reduction is a model that changes what you can afford to run, not one that lifts the ceiling everywhere.

And GPT-6 Sol's latency profile is a genuine liability. Time to first token is 107.18 seconds against a board median of 3.87. That is the slowest first token on the board by a wide margin. DeepSeek V4 Pro is not a fast model either, but it is not in that territory, and for any interactive surface the difference is disqualifying in one direction only.

Open weights versus a rented frontier

This is the dimension where the two models are not really competing. DeepSeek V4 Pro is a 1.6T-parameter MoE with 49B active parameters, MIT-licensed, downloadable. That means a fixed cost that amortizes to nothing at volume, a deployment you control, and a model that cannot be deprecated out from under you. It also means you own the serving stack, the GPU bill, and every regression.

GPT-6 Sol is the opposite trade. You pay per token forever, you get frontier capability without owning hardware, and OpenAI can change the price, the rate card, or the availability with a blog post — as it did on September 22 when it halved the price of its own flagship. There is no version of GPT-6 Sol you can pin.

The right way to think about it is not "which is better" but "which risk do you prefer." Vendor risk against operational risk. For a startup with no infrastructure team and volatile traffic, the API is obviously correct. For an organization with existing GPU capacity, a compliance boundary, or a workload whose volume makes per-token pricing absurd, the open weights win on economics alone, and twelve Index points is a price worth paying.

If you want to stop choosing

There is a version of this decision that does not require committing. DeepSeek V4 Pro is on OrcaRouter at DeepSeek's list price, with the pass-through model that means a DeepSeek rate change — including the peak and off-peak schedule — is reflected on our side the same day rather than after a reseller renegotiates. GPT-6 Sol is not in our catalogue as of this writing; it is reachable through OpenAI's own API, so a route that spans both means one key for DeepSeek V4 Pro and a direct integration for OpenAI.

That split is worth having for a specific reason: these two models fail differently. An open-weight deployment fails when your hardware does; an API fails when the vendor does. Automatic failover across providers is what turns those into a degraded afternoon rather than an outage, and being able to move a route between a cheap high-throughput model and an expensive careful one by editing a config rather than a code path is what lets you follow the price curve instead of betting on it.

Who should pick which

Pick DeepSeek V4 Pro if you have infrastructure, a compliance boundary, or a volume problem. It is MIT-licensed, it is eighth of 114 on the independent index, it carries 384K of output room, and its rate card has no context cliff. It is the correct answer for anyone whose binding constraint is cost at scale or control over deployment, and the twelve-point gap to GPT-6 Sol is the price of that.

Pick GPT-6 Sol if you need the frontier and your context stays under 272K tokens. It scores 48 against 36, it generates tokens about 50% faster, and at $2/$10 it is the cheapest OpenAI has ever made its top model. Just know what you are buying: a hundred-second wait for the first token, a long-context tier that doubles the rate, and a model whose independent assessment is a mix of gains and regressions rather than a clean step up.

And if the answer is "both, depending on the request," that is not indecision. It is the correct architecture for a market where two vendors halved their flagship prices within six weeks of each other and neither has stopped moving.