Both are Alibaba models. Qwen3.6 27B is the newer, generally stronger default; reach for Qwen3.6 35B A3B when a specific cost or latency profile matters more than the latest capabilities.
Qwen3.6 27B and Qwen3.6 35B A3B are both Alibaba models, so the real question is not which lab to trust but which tier fits your workload and budget. Qwen3.6 27B is a dense 27B multimodal model with its family's best coding score — it beats a 397B mixture-of-experts, but costs more per token. Qwen3.6 35B A3B is a sparse 35B mixture-of-experts running on 3B active parameters — strong agentic coding at near-3B cost on consumer hardware. Since both come from the same lab, the comparison below focuses on the tier-and-cost trade-offs that actually separate them.
Key differences
Context window: both advertise 256K (~393 pages). Tie on paper — test on your own long inputs, since usable recall varies by model.
Coding: Qwen3.6 27B leads SWE-Bench Verified by 3.8 points (77.2% vs 73.4%) — a real edge on hard, real-world software tasks.
Specifications
Spec
Qwen3.6 27B
Qwen3.6 35B A3B
Provider
Alibaba (China)
Alibaba (China)
Released
April 22, 2026
April 16, 2026
Context window
256K (~393 pages)
256K (~393 pages)
Price (in/out)
Open weight (self-host / free)
Open weight (self-host / free)
Open weight?
Yes — self-hostable
Yes — self-hostable
Modalities
text, image, code
text, image, code
SWE-Bench Verified
77.2%
73.4%
MRCR v2 @ 1M
Not published
Not published
Who wins what
The best open coding score in its family — 77.2% on SWE-Bench Verified, beating Alibaba's own 397B mixture-of-experts at a fifteenth of the size: Qwen3.6 27B — It scores 77.2% on SWE-Bench Verified against Qwen3.6 35B A3B's 73.4% — a 3.8-point edge on real repository work.
Dense, so quality per gigabyte of VRAM is high: it fits one consumer GPU when quantised: Qwen3.6 27B — Qwen3.6 35B A3B is comparatively weak here — loses to its smaller dense sibling Qwen3.6 27B on every coding benchmark, despite more total parameters
Far stronger agentic work than its sparse sibling (59.3 against 51.5 on Terminal-Bench 2.0): Qwen3.6 27B — A dense 27B multimodal model with its family's best coding score — it beats a 397B mixture-of-experts, but costs more per token — and it leads SWE-Bench Verified 77.2% to 73.4%.
Extreme sparsity — only 3B of 35B parameters active per token, giving near-3B inference cost: Qwen3.6 35B A3B — Qwen3.6 27B is comparatively weak here — every parameter fires on every token, so it is slower and costlier per token than the sparse 35B
Runs at roughly 120 tokens per second on a single 24GB consumer GPU: Qwen3.6 35B A3B — Qwen3.6 27B is comparatively weak here — hosted output pricing is the harshest in its family, and provider input prices moved by roughly half in a single quarter
Apache 2.0 weights with a 256K native context, extensible to about 1M via YaRN: Qwen3.6 35B A3B — Qwen3.6 35B A3B lists apache 2.0 weights with a 256K native context, extensible to about 1M via YaRN among its strengths; Qwen3.6 27B does not.
Which should you pick?
Anyone whose priority is the best open coding score in its family — 77.2% on swe-bench verified, beating alibaba's own 397b mixture-of-experts at a fifteenth of the size: Qwen3.6 27B — It is specifically built for that.
Anyone whose priority is extreme sparsity — only 3b of 35b parameters active per token, giving near-3b inference cost: Qwen3.6 35B A3B — That is its strongest area.
Qwen3.6 27B: where it fits
A dense 27B multimodal model with its family's best coding score — it beats a 397B mixture-of-experts, but costs more per token. Released April 22, 2026 by Alibaba, it is built for the best open coding score in its family — 77.2% on SWE-Bench Verified, beating Alibaba's own 397B mixture-of-experts at a fifteenth of the size, dense, so quality per gigabyte of VRAM is high: it fits one consumer GPU when quantised, far stronger agentic work than its sparse sibling (59.3 against 51.5 on Terminal-Bench 2.0), and dense models fine-tune far more predictably than mixture-of-experts models do.
Its trade-offs are real: every parameter fires on every token, so it is slower and costlier per token than the sparse 35B, hosted output pricing is the harshest in its family, and provider input prices moved by roughly half in a single quarter, and its SWE-Bench score comes from Alibaba's internal scaffold rather than the standard public harness. As an open-weight model, its running cost is your own hardware rather than a per-token fee.
Qwen3.6 35B A3B: where it fits
A sparse 35B mixture-of-experts running on 3B active parameters — strong agentic coding at near-3B cost on consumer hardware. Released April 16, 2026 by Alibaba, it is built for extreme sparsity — only 3B of 35B parameters active per token, giving near-3B inference cost, runs at roughly 120 tokens per second on a single 24GB consumer GPU, apache 2.0 weights with a 256K native context, extensible to about 1M via YaRN, and preserves its reasoning across turns, which cuts the overhead of agentic loops.
Its trade-offs: loses to its smaller dense sibling Qwen3.6 27B on every coding benchmark, despite more total parameters, its SWE-Bench score comes from Alibaba's internal scaffold rather than the standard public harness, and all 35B parameters must stay resident in VRAM even though only 3B compute per token. As an open-weight model, its running cost is your own hardware rather than a per-token fee.
The bottom line for this matchup
Because Qwen3.6 27B and Qwen3.6 35B A3B come from the same lab (Alibaba), they share the same training philosophy and ecosystem — the decision is purely tier vs. cost. Qwen3.6 27B is the more capable, more recent option; the other earns its place only when its price or latency profile fits a specific job better. Most teams should default to Qwen3.6 27B and drop down only with a concrete reason.
Frequently asked questions
Is Qwen3.6 27B or Qwen3.6 35B A3B better for coding?
On SWE-Bench Verified, Qwen3.6 27B scores 77.2% and Qwen3.6 35B A3B scores 73.4% — Qwen3.6 27B has the measurable edge.
Which is cheaper, Qwen3.6 27B or Qwen3.6 35B A3B?
They are priced almost identically, so cost will not decide between them.
Which has the bigger context window?
Both advertise 256K (~393 pages). Remember advertised ≠ usable: recall typically degrades before the ceiling.
Should I upgrade from Qwen3.6 35B A3B to Qwen3.6 27B?
Since both are Alibaba models, the newer one (Qwen3.6 27B) is usually the better default unless you need a specific cost or latency profile from the other.
Which is newer, Qwen3.6 27B or Qwen3.6 35B A3B?
Qwen3.6 27B — released April 22, 2026, about 6 days after Qwen3.6 35B A3B.
Qwen3.6 27B vs Qwen3.6 35B A3B
Alibaba · China | Alibaba · China · Updated June 2026
Quick verdict
Both are Alibaba models. Qwen3.6 27B is the newer, generally stronger default; reach for Qwen3.6 35B A3B when a specific cost or latency profile matters more than the latest capabilities.
Qwen3.6 27B and Qwen3.6 35B A3B are both Alibaba models, so the real question is not which lab to trust but which tier fits your workload and budget. Qwen3.6 27B is a dense 27B multimodal model with its family's best coding score — it beats a 397B mixture-of-experts, but costs more per token. Qwen3.6 35B A3B is a sparse 35B mixture-of-experts running on 3B active parameters — strong agentic coding at near-3B cost on consumer hardware. Since both come from the same lab, the comparison below focuses on the tier-and-cost trade-offs that actually separate them.
Key differences at a glance
▸Context window: both advertise 256K (~393 pages). Tie on paper — test on your own long inputs, since usable recall varies by model.
▸Coding: Qwen3.6 27B leads SWE-Bench Verified by 3.8 points (77.2% vs 73.4%) — a real edge on hard, real-world software tasks.
Side-by-side specs
Spec
Qwen3.6 27B
Qwen3.6 35B A3B
Provider
Alibaba (China)
Alibaba (China)
Released
April 22, 2026
April 16, 2026
Context window
256K (~393 pages)
256K (~393 pages)
Price (in/out)
Open weight (self-host / free)
Open weight (self-host / free)
Open weight?
Yes — self-hostable
Yes — self-hostable
Modalities
text, image, code
text, image, code
SWE-Bench Verified
77.2%
73.4%
MRCR v2 @ 1M
Not published
Not published
Who wins what
The best open coding score in its family — 77.2% on SWE-Bench Verified, beating Alibaba's own 397B mixture-of-experts at a fifteenth of the size
Qwen3.6 27B
It scores 77.2% on SWE-Bench Verified against Qwen3.6 35B A3B's 73.4% — a 3.8-point edge on real repository work.
Dense, so quality per gigabyte of VRAM is high: it fits one consumer GPU when quantised
Qwen3.6 27B
Qwen3.6 35B A3B is comparatively weak here — loses to its smaller dense sibling Qwen3.6 27B on every coding benchmark, despite more total parameters
Far stronger agentic work than its sparse sibling (59.3 against 51.5 on Terminal-Bench 2.0)
Qwen3.6 27B
A dense 27B multimodal model with its family's best coding score — it beats a 397B mixture-of-experts, but costs more per token — and it leads SWE-Bench Verified 77.2% to 73.4%.
Extreme sparsity — only 3B of 35B parameters active per token, giving near-3B inference cost
Qwen3.6 35B A3B
Qwen3.6 27B is comparatively weak here — every parameter fires on every token, so it is slower and costlier per token than the sparse 35B
Runs at roughly 120 tokens per second on a single 24GB consumer GPU
Qwen3.6 35B A3B
Qwen3.6 27B is comparatively weak here — hosted output pricing is the harshest in its family, and provider input prices moved by roughly half in a single quarter
Apache 2.0 weights with a 256K native context, extensible to about 1M via YaRN
Qwen3.6 35B A3B
Qwen3.6 35B A3B lists apache 2.0 weights with a 256K native context, extensible to about 1M via YaRN among its strengths; Qwen3.6 27B does not.
Which should you pick?
Anyone whose priority is the best open coding score in its family — 77.2% on swe-bench verified, beating alibaba's own 397b mixture-of-experts at a fifteenth of the size
→ Qwen3.6 27B
It is specifically built for that.
Anyone whose priority is extreme sparsity — only 3b of 35b parameters active per token, giving near-3b inference cost
→ Qwen3.6 35B A3B
That is its strongest area.
Qwen3.6 27B: where it fits
A dense 27B multimodal model with its family's best coding score — it beats a 397B mixture-of-experts, but costs more per token. Released April 22, 2026 by Alibaba, it is built for the best open coding score in its family — 77.2% on SWE-Bench Verified, beating Alibaba's own 397B mixture-of-experts at a fifteenth of the size, dense, so quality per gigabyte of VRAM is high: it fits one consumer GPU when quantised, far stronger agentic work than its sparse sibling (59.3 against 51.5 on Terminal-Bench 2.0), and dense models fine-tune far more predictably than mixture-of-experts models do.
Its trade-offs are real: every parameter fires on every token, so it is slower and costlier per token than the sparse 35B, hosted output pricing is the harshest in its family, and provider input prices moved by roughly half in a single quarter, and its SWE-Bench score comes from Alibaba's internal scaffold rather than the standard public harness. As an open-weight model, its running cost is your own hardware rather than a per-token fee.
Qwen3.6 35B A3B: where it fits
A sparse 35B mixture-of-experts running on 3B active parameters — strong agentic coding at near-3B cost on consumer hardware. Released April 16, 2026 by Alibaba, it is built for extreme sparsity — only 3B of 35B parameters active per token, giving near-3B inference cost, runs at roughly 120 tokens per second on a single 24GB consumer GPU, apache 2.0 weights with a 256K native context, extensible to about 1M via YaRN, and preserves its reasoning across turns, which cuts the overhead of agentic loops.
Its trade-offs: loses to its smaller dense sibling Qwen3.6 27B on every coding benchmark, despite more total parameters, its SWE-Bench score comes from Alibaba's internal scaffold rather than the standard public harness, and all 35B parameters must stay resident in VRAM even though only 3B compute per token. As an open-weight model, its running cost is your own hardware rather than a per-token fee.
The bottom line for this matchup
Because Qwen3.6 27B and Qwen3.6 35B A3B come from the same lab (Alibaba), they share the same training philosophy and ecosystem — the decision is purely tier vs. cost. Qwen3.6 27B is the more capable, more recent option; the other earns its place only when its price or latency profile fits a specific job better. Most teams should default to Qwen3.6 27B and drop down only with a concrete reason.
Want both Qwen3.6 27B and Qwen3.6 35B A3B without two subscriptions? LumiChats gives you these plus 40+ models under one ₹69/day pass (about $1/day) — draft with one, cross-check with the other.
Is Qwen3.6 27B or Qwen3.6 35B A3B better for coding?
On SWE-Bench Verified, Qwen3.6 27B scores 77.2% and Qwen3.6 35B A3B scores 73.4% — Qwen3.6 27B has the measurable edge.
Which is cheaper, Qwen3.6 27B or Qwen3.6 35B A3B?
They are priced almost identically, so cost will not decide between them.
Which has the bigger context window?
Both advertise 256K (~393 pages). Remember advertised ≠ usable: recall typically degrades before the ceiling.
Should I upgrade from Qwen3.6 35B A3B to Qwen3.6 27B?
Since both are Alibaba models, the newer one (Qwen3.6 27B) is usually the better default unless you need a specific cost or latency profile from the other.
Which is newer, Qwen3.6 27B or Qwen3.6 35B A3B?
Qwen3.6 27B — released April 22, 2026, about 6 days after Qwen3.6 35B A3B.
Specifications and benchmarks reflect publicly reported figures as of June 2026 and may change as providers release updates. Always verify on your own workload.