GPT-5.6 Sol vs Qwen 3.6: Measured Summit vs Apache-2.0 Open Weights (2026)

GPT-5.6 Sol and Qwen 3.6 are separated by the widest independent intelligence gap in this comparison series and one of the widest price gaps. On the Artificial Analysis Intelligence Index v4.1 — a third-party benchmark, not a vendor claim — OpenAI's Sol scores 59 and Alibaba's Qwen 3.6 Plus scores 40, a 19-point difference. On price the ranking inverts hard: Qwen 3.6 Plus costs $0.325 per million input tokens and $1.95 per million output tokens against Sol's $5.00 and $30.00, making Sol roughly 15 times more expensive on input and roughly 15 times more expensive on output. Sol wins measured capability; Qwen 3.6 wins economics and openness. One caveat frames the whole piece: the Qwen model that carries that 40 score, Qwen 3.6 Plus, is closed — the open weights belong to smaller, separate models in the same family.

Quick Verdict

The one-sentence version: pick GPT-5.6 Sol when you need the highest independently measured intelligence available and the budget to pay for it; pick Qwen 3.6 when a 19-point index gap costs less than a 15-times token bill, or when open weights under Apache 2.0 are a strategic requirement you can accept a smaller model to satisfy.

Sol is the narrow overall winner on the axis this comparison is built around — measured capability — and it is not a close call on that axis. A 19-point spread on the Artificial Analysis Intelligence Index v4.1 (59 against 40) is the widest independent gap between any target and challenger we have compared, and Sol adds an independent AA Coding Index score of 80 that ranks first on that index. But Qwen 3.6's counter-argument is arithmetic, not rhetoric: at $0.325 input and $1.95 output per million tokens against $5.00 and $30.00, you can run roughly 15 times as much work for the same money — and the family ships open weights under Apache 2.0, the most permissive license on the market.

• Best independently measured intelligence: GPT-5.6 Sol (59 vs 40 on the AA Intelligence Index v4.1)
• Best independently measured coding: GPT-5.6 Sol (AA Coding Index 80, ranked first; Qwen 3.6 Plus is not measured on that index)
• Best price: Qwen 3.6 (about 15 times cheaper on both input and output)
• Best for openness and control: Qwen 3.6 (open weights under Apache 2.0 — but via the 27B and 35B-A3B variants, not the Plus tier)
• Best for long-context work: GPT-5.6 Sol, marginally (1.05M tokens against 1M)
• Best for multimodal input: Qwen 3.6 (native text, image, and video)
• Narrow overall winner: GPT-5.6 Sol — on capability. Flip the priority to cost or open weights and Qwen 3.6 becomes the rational default without hesitation.

How We Compared Them

This is a research-led comparison, not a hands-on bake-off. We have not run either model in production at ThePlanetTools, and we say so up front rather than dressing up desk research as a side-by-side trial. What we did instead was impose three rules that most model comparisons quietly skip.

Rule one: independent scores and vendor scores are never mixed. Every number in this piece carries a label. An independent score comes from a third-party evaluator that runs the model itself — here, Artificial Analysis, which publishes the Intelligence Index and the Coding Index. A vendor self-reported score comes from the company that sells the model, running its own harness on its own model. Both can be useful. They are not the same kind of evidence, and a comparison that silently blends them is telling you something it has not actually established.

Rule two: we only put two numbers head to head when they come from the same benchmark, at the same version, under the same evidence regime. That rule is what makes the intelligence comparison in this piece clean and the coding comparison in this piece one-sided in a way we have to explain rather than score. Both the target and the challenger have an Artificial Analysis Intelligence Index score on version 4.1 of the index, so 59 against 40 is a genuine apples-to-apples measurement. Coding is not so lucky, and we devote a full section below to exactly why.

Rule three — specific to Qwen and easy to get wrong: Qwen 3.6 is a family, not a single model, and the pieces are not interchangeable. The member that carries the independent Artificial Analysis score of 40 is Qwen 3.6 Plus, Alibaba's closed, proprietary API flagship. The open weights that make the openness argument belong to different, smaller models — Qwen3.6-27B and Qwen3.6-35B-A3B — released under Apache 2.0. Throughout this article, whenever a figure of 40 appears it means Qwen 3.6 Plus specifically, and whenever we discuss self-hosting or open weights we mean the 27B and 35B-A3B variants specifically. Attributing Plus's score to the open models, or the open models' license to Plus, would be the single most misleading move available here, and we do not make it.

Pricing was taken from each vendor's published API rates rather than from search snippets or aggregator listings, which drift. One drift is worth flagging: Artificial Analysis lists Qwen 3.6 Plus at $0.50 input and $3.00 output per million tokens via one access path, while Alibaba's own Model Studio (DashScope) endpoint quotes $0.325 input and $1.95 output. We use the vendor-direct rate throughout, because it is the price you actually pay through Alibaba, and we note the discrepancy so you are not surprised by a different number on an aggregator. All prices in this piece are quoted in US dollars per million tokens.

Meet Both Models

GPT-5.6 Sol — the measured summit

GPT-5.6 Sol is OpenAI's top-tier model in the GPT-5.6 family, and as of this writing it sits at or near the top of the independent leaderboards that matter. It scores 59 on the Artificial Analysis Intelligence Index v4.1 and 80 on the AA Coding Index, where it ranks first — both third-party measurements, neither an OpenAI claim. It carries a 1.05-million-token context window, and it is priced accordingly: $5.00 per million input tokens, $0.50 per million cached input tokens, and $30.00 per million output tokens.

Sol is the flagship of a three-model family. Below it sit GPT-5.6 Terra (AA Intelligence 55, AA Coding 77, at half Sol's price) and GPT-5.6 Luna (AA Intelligence 51, AA Coding 75, cheaper still). That laddering matters for this comparison, because "GPT-5.6" is not one price point — and if Sol's rate is what disqualifies it for your workload, the answer may be a different rung of the same ladder rather than a different vendor entirely. The only model with a higher independent Intelligence Index score than Sol in our current set is Claude Fable 5 at 60, one point ahead.

Sol is closed. You reach it through OpenAI's API. There are no weights to download, no self-hosting path, and no fine-tuning of the base model itself.

Qwen 3.6 — a family, not a model

Qwen 3.6 is Alibaba's model family, released on April 2, 2026, and understanding it means keeping two things separate that vendors and write-ups routinely blur together.

The first is Qwen 3.6 Plus, the closed, proprietary API flagship. This is the member that has been independently measured: it scores 40 on the Artificial Analysis Intelligence Index v4.1. It is multimodal — native text, image, and video input — carries a 1-million-token context window with a maximum output of 65,536 tokens, and is priced aggressively at $0.325 per million input tokens and $1.95 per million output tokens through Alibaba's Model Studio. Like Sol, Plus is closed: no downloadable weights.

The second is the open-weight branch: Qwen3.6-35B-A3B, a sparse Mixture-of-Experts model with about 3 billion active parameters, and Qwen3.6-27B, a dense model. Both ship under an Apache 2.0 license — the most permissive terms in the market, with no monthly-active-user ceiling and no field-of-use restrictions. These are the models you can download, self-host, and fine-tune freely. They are also smaller and separate models: they do not carry Plus's independent Artificial Analysis score, and Plus does not carry their open license. The Qwen family therefore offers something neither Sol nor most closed frontier models can — genuine open weights — but it offers them through a different, lighter model than the one that posts the headline intelligence number.

This split is the single most important structural fact in the comparison, and we return to it in the openness section, because it changes what "Qwen wins on openness" actually buys you.

Head-to-Head at a Glance

The table splits four rows to three in Qwen 3.6's favor, and that count is deliberately misleading — which is exactly why we are pointing it out rather than letting it stand. Counting rows treats a 19-point independent intelligence gap as one unit of evidence and a license choice as another unit of the same size. They are not the same size. Sol's rows are about how good the model is; Qwen's rows are about what it costs and who controls it. Both matter. Neither is settled by a row count, and any comparison that hands you a tally instead of a judgment is dodging the question.

Intelligence: The Cleanest Signal We Have

The Artificial Analysis Intelligence Index v4.1 is the one measurement in this comparison that is both independent and version-matched, which makes it the closest thing to a referee's scorecard either model has. GPT-5.6 Sol scores 59. Qwen 3.6 Plus scores 40. Nineteen points.

That gap deserves to be sized honestly, because "19 points" is abstract. On the same v4.1 index, Claude Fable 5 scores 60, GPT-5.6 Terra scores 55, and GPT-5.6 Luna scores 51. Sol at 59 is at the summit. Qwen 3.6 Plus at 40 sits well below the entire GPT-5.6 family — below even Luna, the family's cheapest rung, by eleven points. So the honest framing is not "Qwen 3.6 Plus is a bit behind the flagship." It is: Qwen 3.6 Plus's independently measured intelligence is a full tier below every model in the GPT-5.6 lineup, and this is the widest independent capability gap in this comparison series. Pretending otherwise to keep the piece balanced would be dishonest.

What that gap does not tell you is equally important. An index score is a composite across many evaluations; it is a strong signal of general reasoning capability and a weak signal of how a model will behave on your specific workload. A 19-point index gap says Sol will hold up better on hard, multi-step, ambiguous reasoning than Qwen 3.6 Plus will. It does not say Sol is 48% better at classifying your support tickets, summarizing your documents, or writing your CRUD endpoints — tasks where both models are likely well past the threshold of "good enough," and where the difference you actually feel is latency and cost, not ceiling. The rule of thumb we would apply: the harder and less structured the task, the more the 19 points matter; the more routine and well-specified the task, the more the price ratio matters.

Coding: An Asymmetry of Evidence, Not a Score Gap

This is the section where most comparisons of these two models go wrong, so we are going to be pedantic about it.

Sol has an independent coding score. Qwen 3.6 Plus does not. And the coding figures that do exist on the Qwen side belong to the open-weight variants, not to the Plus tier that this comparison scores on intelligence. Those are three separate facts, and keeping them separate is the whole job here.

GPT-5.6 Sol's coding figure is an AA Coding Index score of 80, which is independent — Artificial Analysis, a third-party evaluator, ran the model and ranked it first on that index. That is the strongest coding evidence anywhere in this comparison, and it is referee-verified rather than self-reported.

Qwen 3.6 Plus, the closed flagship, has no independent coding score and no published coding benchmark of its own. The coding numbers that circulate for "Qwen 3.6" are not Plus's numbers at all. They belong to the two open-weight models: Alibaba self-reports a SWE-bench Verified result of 77.2% for the dense Qwen3.6-27B and 73.4% for the sparse Qwen3.6-35B-A3B. Both are vendor self-reported, and — this is the part that matters — both describe the open-weight models, which are smaller and architecturally different from the Plus tier we are comparing on intelligence.

So there are two independent reasons we will not line Qwen's coding figures up against Sol's, and either one alone would be disqualifying:

• Different benchmarks. The AA Coding Index is a composite index maintained by a third-party evaluator; SWE-bench Verified is a specific benchmark measuring an agent's ability to resolve real software issues. A score on one and a score on the other are two measurements of different things on different scales. Subtracting one from the other produces a number that looks precise and means nothing.
• Different evidence regimes, and different models. Sol's index score is a referee's measurement of the model we are actually comparing. Qwen's SWE-bench figures are a competitor's self-report about different, smaller models than the one carrying the 40 intelligence score. Vendor self-reported benchmarks are not worthless, but they are systematically optimistic — the company chooses the harness, the scaffold, the prompt count, and whether to publish at all — and here they also describe the wrong tier of the family. Putting them next to Sol's independent, first-ranked index score would imply a symmetry that does not exist on any dimension.

Here is what we can actually say, with the labels attached. Sol has a first-ranked independent coding score for the exact model in this comparison. Qwen 3.6 Plus has no coding evidence of its own at all. The open-weight Qwen models have vendor self-reported SWE-bench Verified figures that read well, but they are a different product, measured by their own maker. The honest answer to "which one codes better?" is therefore: Sol has better evidence, for the right model, and it is not close on the evidence. If coding quality is the deciding factor and you cannot run your own evaluation, Sol is the better-evidenced choice. If you can run your own evaluation on your own repository, do that instead of trusting any of these numbers — and if you are eyeing the open Qwen models specifically for self-hosted coding, evaluate those models, not Plus, because they are what you would actually deploy.

Pricing: Where Qwen 3.6 Pulls Away

Price is the axis where the ranking inverts, and it inverts violently.

The multiple to internalize is that it is roughly 15 times on both ends. Most matchups trade a cheaper input rate for a costlier output rate or vice versa; Qwen 3.6 Plus is simply cheaper across the board by more than an order of magnitude. Output tokens dominate the bill on almost every generative workload — code generation, long-form writing, agent traces, chain-of-thought reasoning — and Sol charges about 15 times more for them. Put concretely: a workload that costs $200 per month in Qwen 3.6 Plus output tokens costs roughly $3,000 per month in Sol output tokens. That is not a rounding difference you optimize away with better prompts; it is a different line item on a different budget.

One detail sharpens the point. Sol's cached input rate — its best-case input price — is $0.50 per million tokens. Qwen 3.6 Plus's regular, uncached input rate is $0.325 per million tokens. In other words, Qwen's ordinary input price undercuts Sol's most heavily discounted input price by about a third. There is no configuration of caching in which Sol's input economics catch Qwen's.

Two honesty notes keep this from being a pure rout. First, tokenizers differ across vendors, so the same text does not necessarily produce the same token count on both models, and the real cost ratio on your actual prompts may not track the headline per-token ratio exactly. Measure on your own workload before you commit to a number. Second, a cheaper model that fails costs more than an expensive model that succeeds. If a task requires two Qwen 3.6 Plus attempts plus a human fixing the output, the 15-times advantage erodes fast. The price gap is real and enormous; it only converts into savings on workloads where both models actually succeed. Establishing that both models succeed on your workload is your job, and it is the single highest-leverage hour of evaluation you can spend here.

Context Window and Architecture

Context is close to a tie. Sol's window is 1.05 million tokens; Qwen 3.6 Plus's is 1 million. That is a marginal edge for Sol — about 5%, or roughly fifty thousand tokens — and for almost every real workflow it is a distinction without a difference. Both models comfortably hold a large codebase, a long document set, or an extended agent trace in working memory. Unlike the intelligence and price axes, where the gap is decisive, here you should treat the two as functionally equivalent and let other factors decide. If your prompts genuinely live at the 1-million-token boundary, Sol's extra headroom is real; below that ceiling, it is a spec-sheet footnote.

Architecturally, the family designs diverge in what they disclose and how they are built. Qwen 3.6 Plus is a closed, multimodal model — it accepts text, image, and video input natively — but Alibaba does not publish its internals, which is standard for a proprietary flagship. The open-weight branch is more legible by construction: Qwen3.6-35B-A3B is a sparse Mixture-of-Experts design that activates only about 3 billion parameters per token, which is precisely what lets a self-hoster run it on modest hardware, while Qwen3.6-27B is a conventional dense model. Sol's internals are not disclosed either, which is standard practice for a closed frontier model and not a criticism, but it does mean that everything you know about how Sol works, you know from its outputs, its price, and independent measurement.

Openness, Apache 2.0, and the Tier Trap

Qwen 3.6's open weights are its second genuine strategic advantage, and for a certain kind of buyer they are worth more than the price gap. But this is exactly where the family split stops being pedantry and starts being the decision, so read this section slowly.

The open weights ship under an Apache 2.0 license — more permissive than the Modified MIT and custom "community" licenses attached to most open-weight competitors, with no monthly-active-user threshold, no field-of-use carve-outs, and full rights to modify, redistribute, and deploy commercially. If a permissive open license is a hard requirement — for data residency, air-gapped deployment, regulatory exposure, or simply an exit option if a vendor's pricing shifts — Qwen 3.6 offers one of the cleanest available, and Sol cannot compete on this axis by construction. A closed model is a different product category.

Here is the trap. The open weights are Qwen3.6-27B and Qwen3.6-35B-A3B — not Qwen 3.6 Plus. Plus, the model that posts the independent Artificial Analysis score of 40, is closed and API-only, exactly like Sol. So the moment you choose Qwen 3.6 for its openness, you are no longer running the model this comparison scored on intelligence. You are running a smaller, separate model whose only public capability evidence is a vendor self-reported coding benchmark. That may be entirely fine — the open models are well-regarded and the sparse 35B-A3B in particular is cheap to serve — but you should choose them with your eyes open about what you are and are not getting. You do not get the 40-scoring Plus model with a downloadable-weights bonus; you get a different model that happens to share a family name and a license you like.

The cost of open freedom is also operational. Self-hosting even a lightweight MoE means GPUs, serving infrastructure, and a team to keep it healthy; "open" is not "free." For most small teams, the realistic version of the openness advantage is not self-hosting at all — it is the optionality of self-hosting, plus a competitive hosted market for the open models that keeps prices honest. If you are not actually going to run your own inference, the practical Qwen 3.6 you will use is Plus, through Alibaba's API, and its advantage over Sol collapses cleanly to one thing: it is about 15 times cheaper, at a measured intelligence tier below the entire GPT-5.6 lineup.

Winner by Category

Best independently measured intelligence: GPT-5.6 Sol

59 against 40 on the AA Intelligence Index v4.1 — a 19-point, independently measured, version-matched gap. This is the cleanest evidence in the comparison and it is not close. Qwen 3.6 Plus scores below every model in the GPT-5.6 family on this index, including the cheapest one.

Best-evidenced coding: GPT-5.6 Sol

Sol holds an independent AA Coding Index score of 80, ranked first on that index, for the exact model being compared. Qwen 3.6 Plus has no coding evidence of its own; the SWE-bench Verified figures that exist for the Qwen family are vendor self-reported and describe the smaller open-weight variants, not Plus. Sol wins on the quality of the evidence and the fact that it applies to the right model — which is the only comparison available here.

Best price: Qwen 3.6

$0.325 input and $1.95 output per million tokens against $5.00 and $30.00. About 15 times cheaper on both ends — and the open-weight variants remove the per-token cost entirely if you self-host. On high-volume generative workloads this is the axis that writes the check.

Best openness and control: Qwen 3.6

Open weights under Apache 2.0 — the most permissive license in the field — self-hostable and fine-tunable, with a genuine exit option. The asterisk is unavoidable: that openness lives in the 27B and 35B-A3B models, not in the Plus tier that carries the intelligence score. Sol cannot compete here by design regardless.

Best long-context work: GPT-5.6 Sol, marginally

1.05 million tokens against 1 million — a real but small edge, roughly 5%. Treat context as a near-tie and let other factors decide unless your prompts genuinely press the 1-million-token ceiling.

Best multimodal input: Qwen 3.6

Qwen 3.6 Plus accepts text, image, and video input natively. If your workload feeds the model images or video rather than text alone, this is a concrete Qwen advantage worth weighing against the intelligence gap.

Narrow overall winner: GPT-5.6 Sol

On the axis this comparison is built around — measured capability — Sol wins, and it wins by the largest independent margin in the series. It has the higher independent intelligence score, the better-evidenced coding, and a marginal context edge. That is the verdict. But it is a verdict scoped to capability, and the scope is doing real work: at about 15 times the token price, Sol has to earn that gap on your specific workload, and on a great many workloads it will not.

Pros and Cons of Each

GPT-5.6 Sol

What stands out:

• Highest independently measured intelligence in this comparison: 59 on the AA Intelligence Index v4.1, 19 points clear of Qwen 3.6 Plus
• First-ranked independent coding score (AA Coding Index: 80) — referee-verified, not self-reported, and for the exact model being compared
• 1.05-million-token context window, a marginal edge over Qwen 3.6 Plus's 1 million
• Cached input at $0.50 per million tokens softens context-heavy agent loops, even if it never catches Qwen's rate
• Part of a priced ladder — Terra and Luna offer the same family at lower rungs if Sol's rate is prohibitive

Where it falls short:

• About 15 times more expensive than Qwen 3.6 Plus on both input and output — and output dominates most real bills
• Closed weights: no self-hosting, no fine-tuning of the base model, no exit option, full per-token vendor exposure
• Text-only where Qwen 3.6 Plus is natively multimodal across text, image, and video
• Architecture undisclosed, so capability claims rest entirely on outputs and third-party measurement
• No open-weight option at any tier, so it cannot satisfy an Apache-style open-license requirement

Qwen 3.6

What stands out:

• About 15 times cheaper than Sol on both input and output; even Qwen's uncached input undercuts Sol's cached input
• Open weights under Apache 2.0 — the most permissive license on the market — via the Qwen3.6-27B and Qwen3.6-35B-A3B variants
• Qwen 3.6 Plus is natively multimodal, accepting text, image, and video input
• 1-million-token context window, effectively level with Sol
• Sparse 35B-A3B variant activates only about 3 billion parameters per token, making self-hosting cheap by open-weight standards
• A free tier plus pay-as-you-go API pricing means evaluation costs little to nothing

Where it falls short:

• 19 points behind Sol on the independent AA Intelligence Index v4.1 (40 against 59) — the widest capability gap in this series, and below every model in the GPT-5.6 family
• No independent coding score for Plus at all; the SWE-bench Verified figures belong to the smaller open-weight models and are vendor self-reported
• The openness advantage and the intelligence score live in different models: Plus (40, closed) is not the model you self-host, and the open models are not the model that posts the 40
• Self-hosting still requires real GPU infrastructure and an ops team; open is not free to run
• Alibaba is a China-based lab, which is a jurisdictional consideration some Western buyers must weigh regardless of the model's quality

When to Pick Which

Pick GPT-5.6 Sol if...

Your workload lives at the hard end of the difficulty curve and the model's ceiling is what constrains you. Sol is the right call when tasks are genuinely difficult, open-ended, or multi-step; when a 19-point independent intelligence advantage translates into fewer failed runs and less human cleanup; when coding quality is central and you want the model with independent, first-ranked evidence rather than a self-reported number from a different model; or when your per-request volume is low enough that a roughly 15-times token premium is a rounding error against the salary of the person waiting on the output. In short: pay for Sol when intelligence is the bottleneck. If the price is the only thing stopping you, look at GPT-5.6 Terra (AA Intelligence 55) before you look at a different vendor — it may buy you most of the capability at a fraction of the rate.

Pick Qwen 3.6 if...

The bill is the constraint, or an Apache-2.0 open license is a requirement rather than a preference. Qwen 3.6 is the better choice when you run high-volume generative workloads where a 15-times token-price difference compounds into serious money; when your tasks are well-specified enough that both models clear the quality bar and the ceiling never binds; when you feed the model image or video input and want native multimodality; or when you need to self-host for data residency or strategic reasons — in which case you deploy the open 27B or 35B-A3B variant, not Plus. The honest caveat: you are accepting a measurably lower capability ceiling, and if you choose Qwen for its open weights you are running a different, smaller model than the one that scores 40. Do not tell yourself otherwise — 40 against 59 is a real gap, and the right response is to verify on your own workload that the gap never bites, not to pretend it is not there.

Or run both

The most economically rational answer for many teams is not one model but a router. Send the hard, ambiguous, high-stakes requests to Sol, where the 19-point capability advantage earns its price, and send the high-volume, well-specified, repetitive execution to Qwen 3.6 Plus, where the 15-times cost advantage compounds. This is the dominant 2026 production pattern for a reason: it is the only configuration where you pay the premium exactly where it converts into value. For adjacent matchups, our GPT-5.6 Sol vs Claude Sonnet 5 and GPT-5.6 Sol vs DeepSeek V4 comparisons cover the neighboring decisions, and our best AI coding tools of 2026 roundup places both models in the wider field.

Frequently Asked Questions

Is GPT-5.6 Sol or Qwen 3.6 the smarter model?

GPT-5.6 Sol, and the gap is large. On the Artificial Analysis Intelligence Index v4.1 — an independent third-party benchmark, not a vendor claim — Sol scores 59 and Qwen 3.6 Plus scores 40. That 19-point difference is the widest independent capability gap in this comparison series. Qwen 3.6 Plus scores below every model in the GPT-5.6 family on this index, including the cheapest one. Both scores are measured on the same version of the index, which is what makes them directly comparable.

How much cheaper is Qwen 3.6 than GPT-5.6 Sol?

Substantially, and on both ends. Qwen 3.6 Plus charges $0.325 per million input tokens and $1.95 per million output tokens. GPT-5.6 Sol charges $5.00 per million input tokens and $30.00 per million output tokens. That makes Sol roughly 15 times more expensive on input and roughly 15 times more expensive on output. Since output tokens dominate the bill on most generative workloads, a workload costing $200 per month in Qwen 3.6 Plus output tokens would cost roughly $3,000 per month in Sol output tokens.

Which Qwen model does the score of 40 refer to?

Qwen 3.6 Plus specifically — Alibaba's closed, proprietary API flagship. Qwen 3.6 is a family, and only the Plus member has been independently measured on the Artificial Analysis Intelligence Index v4.1, where it scores 40. The open-weight members of the family, Qwen3.6-27B and Qwen3.6-35B-A3B, do not carry that score; they are smaller, separate models. Whenever this article cites 40, it means Qwen 3.6 Plus and nothing else.

Can I self-host Qwen 3.6?

You can self-host the open-weight members of the family — Qwen3.6-27B (dense) and Qwen3.6-35B-A3B (sparse Mixture-of-Experts, about 3 billion active parameters) — both released under an Apache 2.0 license that lets you download, run, modify, and fine-tune them freely. You cannot self-host Qwen 3.6 Plus, which is closed and API-only, exactly like GPT-5.6 Sol. This is the key catch: the model with the independent intelligence score is not the model you can self-host, and the models you can self-host are smaller and separate.

Which model is better at coding?

GPT-5.6 Sol has the better evidence, and it applies to the right model. Sol holds an independent AA Coding Index score of 80 and ranks first on that index, measured by the third-party evaluator Artificial Analysis, for the exact model being compared. Qwen 3.6 Plus has no independent coding score and no coding benchmark of its own. The SWE-bench Verified figures that circulate for "Qwen 3.6" describe the open-weight variants, are vendor self-reported by Alibaba, and do not apply to the Plus tier. We therefore do not put them head to head with Sol's independent score, because they measure different things, under a weaker evidence regime, on different models.

Why don't you compare Qwen's SWE-bench figures against Sol's AA Coding Index score?

Two reasons, either of which alone would be disqualifying. First, they are different benchmarks measuring different things on different scales, so the difference between them is not a meaningful quantity. Second, they come from different evidence regimes and different models: Sol's AA Coding Index score is an independent third-party measurement of the model in this comparison, while Qwen's SWE-bench Verified figures are self-reported by Alibaba about the smaller open-weight models, not the Plus tier that carries the intelligence score. Presenting the two as a head-to-head row would imply a symmetry of rigor, and a sameness of model, that neither exists.

Which model has the larger context window?

GPT-5.6 Sol, but only marginally: 1.05 million tokens against Qwen 3.6 Plus's 1 million — roughly a 5% edge, about fifty thousand tokens. For almost every real workflow this is a distinction without a difference, and both models comfortably hold a large codebase or long document set in working memory. Treat context as a near-tie between these two and let intelligence, price, and openness decide, unless your prompts genuinely press the 1-million-token boundary.

Is Qwen 3.6 multimodal?

Yes. Qwen 3.6 Plus accepts text, image, and video input natively, which is a concrete advantage over GPT-5.6 Sol if your workload involves feeding the model visual or video content rather than text alone. It also carries a maximum output length of 65,536 tokens. If native multimodal input matters to your use case, weigh it against the 19-point intelligence gap rather than treating the decision as intelligence-versus-price alone.

What license are Qwen 3.6's open weights under?

Apache 2.0, applied to the Qwen3.6-27B and Qwen3.6-35B-A3B models. Apache 2.0 is the most permissive widely used open license: no monthly-active-user ceiling, no field-of-use restrictions, and full rights to modify, redistribute, and deploy commercially. That is genuinely more permissive than the Modified MIT and custom community licenses attached to many open-weight competitors. Note again that this license covers the open variants only — Qwen 3.6 Plus, the tier with the independent intelligence score, is closed.

Is GPT-5.6 Sol worth about 15 times the price?

It depends entirely on whether intelligence is your bottleneck. If your tasks are hard, open-ended, or multi-step and a capability ceiling is causing failed runs and human cleanup, then a 19-point independent intelligence advantage and a first-ranked independent coding score can easily be worth the premium — a failed cheap run costs more than a successful expensive one. If your tasks are well-specified and both models clear the quality bar, you are paying about 15 times more for headroom you never use. The way to find out is to run a representative evaluation on your own workload, not to reason from either model's benchmark scores.

Why is Qwen 3.6 Plus priced differently on Artificial Analysis than on Alibaba?

Because the two figures come from different access paths. Artificial Analysis lists Qwen 3.6 Plus at $0.50 input and $3.00 output per million tokens via one route, while Alibaba's own Model Studio (DashScope) endpoint quotes $0.325 input and $1.95 output. We use the vendor-direct rate throughout this article because it is what you pay through Alibaba, and we flag the difference so you are not surprised by a higher number on an aggregator. Either way, Qwen 3.6 Plus remains far cheaper than GPT-5.6 Sol.

Should I use both models together?

For many teams, yes — and it is often the most economically rational configuration. Route hard, ambiguous, high-stakes requests to GPT-5.6 Sol, where the 19-point independent capability advantage earns its price, and route high-volume, well-specified, repetitive execution to Qwen 3.6 Plus, where the roughly 15-times cost advantage compounds into real savings. A router is the only configuration in which you pay the premium exactly where it converts into value, rather than across your whole workload indiscriminately.

Final Verdict

GPT-5.6 Sol is the winner on measured capability, and the margin is the largest in this series. Fifty-nine against forty on the Artificial Analysis Intelligence Index v4.1 is not a close reading of a noisy benchmark — it is a 19-point, independently measured, version-matched gap that places Qwen 3.6 Plus below every model in the GPT-5.6 lineup, including the cheapest one. Add a first-ranked independent coding score (AA Coding Index: 80) for the exact model in play and a marginal context edge, and the capability verdict writes itself. If the model's ceiling is what constrains your work, Sol is the answer and the rest of this article is a footnote.

But the price gap is not a footnote, and pretending it is would be the easy dishonesty of this piece. Qwen 3.6 Plus costs $0.325 per million input tokens and $1.95 per million output tokens against Sol's $5.00 and $30.00 — about 15 times cheaper on both ends — and the family ships open weights under Apache 2.0, the most permissive license in the field, through its 27B and 35B-A3B variants. For a large share of real production workloads, the tasks are well-specified enough that a 19-point index gap never binds, and in those cases paying 15 times more is buying headroom you will never touch.

The one thing we will not let you walk away believing is that Qwen 3.6 hands you both its intelligence score and its open weights in a single model. It does not. The 40-scoring model, Plus, is closed; the open weights belong to smaller, separate models. That split is not a technicality — it is the difference between "a cheaper frontier-adjacent API" and "a model I can own and run myself," and only one of those describes any given deployment. So the real question is not "which model is better" — Sol is, on every axis that measures capability. The question is whether the capability you are buying is capability you actually need, and which Qwen you would actually run. Run a representative slice of your own workload through both, count the failures, and let the failure rate — not the leaderboard — decide which side of that trade you are on.

Last compared: July 2026. Qwen 3.6 was released April 2, 2026 by Alibaba as a model family; Qwen 3.6 Plus is the closed API flagship, while Qwen3.6-27B and Qwen3.6-35B-A3B are open-weight models under Apache 2.0. This is a research-led comparison; we have not run either model in production at ThePlanetTools. Intelligence Index figures (59 and 40) and the Coding Index figure (80) are from Artificial Analysis and are independent third-party measurements, taken on version 4.1 of the index; the 40 is Qwen 3.6 Plus specifically. Separately, and never as a head-to-head against Sol's independent Coding Index score, the SWE-bench Verified figures for the Qwen open-weight models are vendor self-reported by Alibaba and describe those smaller models, not the Plus tier. Pricing verified from each vendor's published API rates at the time of writing (Qwen 3.6 Plus via Alibaba Model Studio) and quoted in US dollars per million tokens.