GPT-6 vs Qwen 3.8 Max: One Takes Video, One Writes Longer

GPT-6 Sol and Qwen3.8 Max charge the same input rate — $2.00 per million tokens, both — and then diverge on two axes that a price table alone will not surface. Qwen3.8 Max, hosted in general availability since 3 August 2026, is the only model of the two that accepts video: its input modalities are text, image and video, where GPT-6 Sol takes text, image and file. GPT-6 Sol, which the vendor shipped on 22 September 2026, is the only one of the two with a published output ceiling, 128,000 tokens, and the only one with a consumer surface behind it — the 7 October ChatGPT rollout that put the model in front of more than 1.2 billion weekly users.

So the sorting question is not which is stronger. It is whether your input is video, and whether your output is long.

Video is the first fork

Most model comparisons reduce to price and benchmark, and this one has a hard structural difference that makes those secondary. If your pipeline ingests video — surveillance footage, recorded meetings, sports or lecture material, product demo reels — Qwen3.8 Max can take the clip inside the request. Its card lists video as an input modality alongside text and image, up to the million-token window, which for video means frame sampling plus transcript in a single call rather than a separate extraction stage. GPT-6 Sol cannot. Its modalities are text, image and file; there is no video input on the card, and no amount of prompt engineering substitutes for it.

That single line decides the shortlist for a video pipeline, and it is not visible on a rate card. Where the two models are genuinely interchangeable is text and image work, and that is where the price and benchmark comparison below applies.

One honesty note on the video claim: the modality is documented, but the schema is not. Neither vendor's catalogue entry spells out resolution, frame rate or clip-length limits, so "supports video" means the input type exists, not that any particular clip will be ingested at the quality you need. Test it against your own footage before you build on it.

The output side runs the other way

Flip the request around and the advantage inverts. GPT-6 Sol publishes a maximum output of 128,000 tokens per response. Qwen3.8 Max's card publishes a context window but no maximum-output figure, so a system that budgets against a hard output cap cannot read one off the vendor's data — the same gap that appears on several hosting-platform cards and is worth treating as unknown rather than unlimited.

What is published is the price asymmetry on the output side, and it is the reverse of the video story. Qwen3.8 Max bills $6.00 per million output tokens to GPT-6 Sol's $10.00 — a 40% discount on every token the model writes. A workload that is output-heavy, such as long-form generation or serialising a large structured artifact, is materially cheaper on Qwen3.8 Max per unit of work, and that is true regardless of the input price being identical.

Note also that Qwen3.8 Max's card lists a single input rate with no documented long-context tier, while GPT-6 Sol reprices the whole request above 272,000 input tokens to $4.00 in and $15.00 out. OpenAI's model page states the rule explicitly: prompts above that threshold are charged at 2x input and cache rates and 1.5x output for the full request. On a prompt past 272,000 tokens, GPT-6 Sol's effective input rate doubles to $4.00 while Qwen3.8 Max's stays at $2.00 — again inverting the apparent input tie.

What the independent board says

Artificial Analysis carries both models, and the composite index has GPT-6 Sol ahead while several individual tests go the other way. Every figure below is the evaluator's, not a vendor's.

• Intelligence Index: GPT-6 Sol 47.6, Qwen3.8 Max 45.4 — GPT-6 Sol ahead by about two points
• Coding index: Qwen3.8 Max 76.2 with a top-ten rank; GPT-6 Sol's coding profile is comparable but the Qwen entry carries a stronger rank
• GPQA Diamond: Qwen3.8 Max 92.8%, GPT-6 Sol's figure is the higher of the two on the same test family
• Humanity's Last Exam: Qwen3.8 Max 43.1%, GPT-6 Sol 47.9%
• Long-Context Recall: Qwen3.8 Max 80.3%, GPT-6 Sol 83.7%
• SciCode: Qwen3.8 Max 52.1%, GPT-6 Sol 57.6%
• Agentic tool use: Qwen3.8 Max 88.8% on terminal-bench v2.1 and 47.8% on tau-banking, both strong

The pattern is that GPT-6 Sol wins the general reasoning composites and the science-and-recall tests, while Qwen3.8 Max is the sharper coding and tool-use model. Two caveats apply and they are not decorative. First, these index values were evaluated on different dates, so two points between revisions is inside the movement a re-run produces, not a settled gap. Second, Qwen3.8 Max's published figures are the evaluator's runs on the model as served by Alibaba's endpoint, and the vendor also shipped a dated snapshot, Qwen3.8 Max (0902), on 2 September 2026 at the same $2.00 / $6.00 rate — if you are pinning a snapshot, confirm which one you are calling before you quote a score.

What OpenAI's 7 October rollout adds

The ChatGPT release is a distribution fact rather than a capability fact, but it changes what a reader means by "GPT-6". On 7 October 2026 OpenAI began rolling GPT-6 into ChatGPT under the Intelligent UI capability, with the Chat tab reaching Plus, Pro, Business and Enterprise first and Free and Go following from 8 October; enterprise availability depends on workplace admin settings. The models powering Work and Codex were left unchanged.

Two details matter for this comparison. The ChatGPT experience is powered by GPT-6 Sol for the paid consumer tiers — so the GPT-6 a billion-plus people meet is the same model you call over the API, not a separate checkpoint. And GPT-6 is a family, not a single model: GPT-6 Astra sits above Sol at $10.00 / $50.00 and GPT-6 Luna below at $0.10 / $0.50. When a comparison names "GPT-6" against Qwen3.8 Max without saying which tier, it is usually comparing against Sol by default, and against Astra when it wants the strongest case. Naming the tier is the difference between a fair comparison and a rhetorical one.

Cost, measured on shapes rather than rates

Because the input rates tie and the output rates diverge, the winner depends entirely on the ratio of tokens in to tokens out. Worked against each vendor's published rates, excluding caching:

• Video-and-transcript analysis (500K input, 6K output): Qwen3.8 Max ≈ $1.04, GPT-6 Sol not applicable — no video input
• Document analysis (250K input, 5K output): Qwen3.8 Max ≈ $0.53, GPT-6 Sol ≈ $0.55 before its 272K threshold applies, and higher once the whole request reprices
• Long-form generation (40K input, 80K output): Qwen3.8 Max ≈ $0.56, GPT-6 Sol ≈ $0.88
• Balanced agentic loop (60K input, 20K output): Qwen3.8 Max ≈ $0.24, GPT-6 Sol ≈ $0.32

The shape of the result is stable: Qwen3.8 Max is the cheaper model for the same token mix, because the input rates tie and its output rate is lower. GPT-6 Sol's counter-argument is not price at all — it is the reasoning composite, the consumer-surface validation, and a documented output ceiling that makes budgeting straightforward.

One operational caveat worth stating because the cards do not. Measured on OrcaRouter's playground over the first week of October 2026, the Qwen3.8 Max route showed a median first-token latency of about 5,809 ms and roughly 50 output tokens per second, with a low error rate; the GPT-6 Sol route in the same window measured faster throughput but a considerably higher error rate. These are shared-route observations, not vendor SLAs, and they move week to week — which is the argument for measuring your own traffic rather than trusting either model's card.

Running both under one key

The realistic answer for a team that has video work on one side and reasoning-heavy work on the other is that neither model wins outright and both belong in the stack. That is the case a gateway is for. On OrcaRouter both qwen/qwen3.8-max and GPT-6 Sol are live routes in the same catalogue behind one OpenAI-compatible API, at the provider's list price with 0% markup — so an Alibaba or OpenAI price move reaches you the same day rather than at your next invoice reconciliation, and there is no separate credential set or SDK path per vendor.

Two features do specific work here. Automatic failover keeps a pipeline alive when one provider's route degrades, which matters directly given how differently those two routes behaved in the same measurement window. And the routing DSL lets the request decide: send anything carrying a video input to Qwen3.8 Max, send long-context reasoning work to GPT-6 Sol, and let a predicate on input modality and request size make the choice instead of a hardcoded model id that has to be changed by hand when traffic shifts.

The short version: if your inputs include video, Qwen3.8 Max is the only one of the two that can take them, and it is the cheaper model on every token mix once you have the request. If your work is text and image reasoning with a need for long, bounded outputs and a validated consumer-grade default, GPT-6 Sol is the stronger card on the composite index and the one with a documented output cap. Where you need both, route — and let the request's modality be the routing key rather than a release cycle.