GPT-6.1 Sol vs Qwen 4 Max: One of These Models Has a Price, a Context Window and a Score

Qwen 4 Max was announced on 2026-09-22 at Alib​aba's Apsara conference. GPT-6.1 Sol was released by Open​AI on 2026-09-29. Both are flagship-tier models from major labs, both announced within a week of each other, and if you search for a comparison between them you will find a great deal of writing that treats them as two products you can weigh against each other. They are not. GPT-6.1 Sol has a published price of $2.00 per million input and $10.00 per million output, a documented 1,050,000-token context window, an available API endpoint and an independent benchmark row. Qwen 4 Max has an announcement. As of 2026-10-12 there is no model card, no published price, no stated context window, no API identifier and no benchmark score attached to it — which means the comparison most people are looking for is not yet a comparison, it is a product announcement with a well-known model's name on the other side of the "vs".

Because that is the state of things, the useful version of this page does two jobs. It states plainly what Qwen 4 Max is and is not, so you do not plan around specifications that do not exist. And it runs the comparison you can actually run today — GPT-6.1 Sol against Qwen3.8 Max, the previous Qw​en flagship from 2026-08-03, which is priced, documented, scored and callable. All figures below come from Open​AI's model and pricing pages, our own catalogue records, and independent measurements from Artificial Analysis, read on 2026-10-12; vendor-run numbers are labeled as vendor-run wherever they appear.

What "announced" means for Qwen 4 Max right now

Alib​aba used Apsara to put a name and a position on the board: Qwen 4 Max is the new top of the Qw​en line. That is a real announcement from a lab that ships real models, and it is not nothing — it sets expectations about capability tier and tells you what the next Qw​en API will be called. What it does not do is answer any of the questions a comparison article is supposed to answer.

• Price — not published. No input rate, no output rate, no cached-token tier, no batch rate. You cannot compute a cost for a single call.

• Context window and maximum output — not stated. The previous flagship documented 1M tokens of context; nothing about Qwen 4 Max's limits has been published, so 1M is an assumption rather than a specification.

• Availability — no API identifier, no endpoint documentation, no indication of which platform serves it first. There is no model string to put in a request.

• Benchmarks — no vendor evaluation table, and no independent run. Artificial Analysis has no row for it. There is not a single measurement of this model in public hands.

• Weights and modality — undisclosed. Nothing has been said about open weights, and nothing has been said about whether the multimodal input that Qwen3.8 Max supports carries forward.

If you are holding a deployment decision that involves Qwen 4 Max, the honest state of play on 2026-10-12 is that you are holding a decision that cannot be made yet. The rest of this page is about the decision you can make.

GPT-6.1 Sol against the Qw​en model that exists

Qwen3.8 Max went live on 2026-08-03 and is the most recent Qw​en flagship with a complete public record: a published price, a documented context window, vendor benchmarks, an independent Intelligence Index row and a live endpoint. Against GPT-6.1 Sol it makes a genuinely close comparison on the numbers, which is what makes the empty space where Qwen 4 Max's card should be so conspicuous.

• Price — GPT-6.1 Sol $2.00 per million input and $10.00 per million output, with cached input at $0.10 and cache writes at $2.50. Qwen3.8 Max $2.00 per million input and $6.00 per million output. Same input rate, and the Qw​en model is 40% cheaper on output — the meter that dominates a reasoning workload.

• Context window — GPT-6.1 Sol 1,050,000 tokens; Qwen3.8 Max 1,000,000. Effectively the same class, and neither is the constraint in a realistic deployment.

• Maximum output — GPT-6.1 Sol documents 128,000 tokens; Qwen3.8 Max's documented ceiling is lower. This is the spec that bites when you are asking for a long structured artifact in one call rather than a long conversation.

• Independent Intelligence Index — GPT-6.1 Sol 52 at maximum reasoning effort; Qwen3.8 Max 45.4. Seven points, measured by the same instrument, on the same scale — which is the only kind of comparison between two models that is worth quoting.

• Terminal-Bench 4.0 — GPT-6.1 Sol 56.06 on the Artificial Analysis run; Qwen3.8 Max 38.9. A seventeen-point spread on the benchmark that measures whether a model can actually finish a task in a shell, and the largest single gap between these two models.

• Modalities — Qwen3.8 Max accepts text, images and video and ships with vision, tool use, JSON mode and reasoning toggles. GPT-6.1 Sol takes text, images and files, with tool calling on the Responses API and reasoning that cannot be turned off.

Where each one wins, stated plainly

The comparison above does not produce a single winner, and pretending otherwise would be the least useful thing this page could do. Three findings are worth pulling out of it.

On price per token, Qwen3.8 Max wins and it is not marginal. Identical input rates and a $6.00 output rate against GPT-6.1 Sol's $10.00 is a 40% cut on the line that matters most, because output tokens are what a reasoning model produces and what you are billed for most heavily. For a high-volume workload with short outputs, that difference is the entire procurement argument.

On capability at the frontier, GPT-6.1 Sol wins on both independent measures. An Intelligence Index of 52 against 45.4, and 56.06 against 38.9 on Terminal-Bench 4.0. Look at those two gaps together: the Qw​en model is closer to parity on general intelligence than it is on agentic execution. If your workload is chat, extraction, summarisation or straightforward generation, seven Index points is a real but survivable difference. If your workload is an agent that has to run commands, recover from errors and finish, seventeen points is a different category of model.

On everything else, the two vendors made different bets. Qwen3.8 Max is multimodal and has a reasoning switch you can turn off; GPT-6.1 Sol has a larger documented output ceiling and a context window that is 5% wider. Neither of these decides a purchase on its own, but both would appear as rows in a comparison table — and every one of them is a row that Qwen 4 Max cannot currently fill.

There is one more consideration that has nothing to do with either spec sheet, and it is the practical reason people end up looking at both models in the same week: they are not competing for the same slot. The Qw​en model's price advantage makes it the right answer for the high-volume tier, and GPT-6.1 Sol's Terminal-Bench margin makes it the right answer for the hard tail. Running both means two keys, two billing relationships and two sets of failure modes — which is precisely what a single endpoint with automatic failover across 200-plus models is for. Both GPT-6.1 Sol and Qwen3.8 Max are in our catalogue at provider list rates with 0% markup: what the vendor publishes is what a caller pays, and when a vendor cuts a price the catalogue row follows the same day rather than waiting on a reseller's update cycle.

How to read a benchmark gap you did not measure

One paragraph of method, because this page quotes two models measured weeks apart by a third party and it would be easy to read more into the numbers than they carry.

The Intelligence Index and Terminal-Bench 4.0 figures above for both models come from Artificial Analysis runs, not from either vendor, which is why they can be put side by side at all. But both models entered that instrument after their releases, at different times, and a benchmark that is revised inside the window between two evaluations stops being a clean comparison — the harness changes, not just the model. Treat the direction of each gap as reliable and the exact size as approximate. Treat the shape as the finding: Qwen3.8 Max is competitive on general reasoning and behind on agentic execution, and that pattern is large enough to survive whatever the exact numbers are.

What is not approximate is the price. Rates come from the two vendors' own published pages, they are quoted to the cent, and they are the part of this article you can verify against your own invoice.

What to watch, and when this page changes

This comparison has a short shelf life, which is the honest thing to say about it.

• If Alib​aba publishes a Qwen 4 Max model card with a price, a context window and an endpoint, this page is superseded and the GPT-6.1 Sol comparison moves to the new model. The Apsara announcement makes that likely rather than speculative — the question is when, not whether.

• If Qwen 4 Max turns out to be priced near Qwen3.8 Max's $2.00 / $6.00 while closing the agentic gap, it becomes the default answer for cost-sensitive work at the frontier tier, and GPT-6.1 Sol's case narrows to workloads where the extra Index points change the outcome.

• If it launches at a premium instead, the comparison above stands as written, with Qwen3.8 Max remaining the price play and GPT-6.1 Sol remaining the capability play.

In the meantime there are two models on this page you can call today, and both are in our catalogue — GPT-6.1 Sol at https://www.orcarouter.ai/models/openai/gpt-6.1-sol and Qwen3.8 Max at https://www.orcarouter.ai/models/qwen/qwen3.8-max, each at the provider's list price with no markup added. Qwen 4 Max is not something we serve, and when Alib​aba opens it up it will be on the vendor's own API and several third-party platforms first.