Pick DeepSeek R1 for open-weight reasoning model or transparent chain-of-thought. Pick Qwen3.6 27B 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 or dense, so quality per gigabyte of vram is high: it fits one consumer gpu when quantised. On a tight budget at scale, Qwen3.6 27B is the value pick.
DeepSeek R1 (DeepSeek) and Qwen3.6 27B (Alibaba) are two of the models people most often weigh against each other in 2026. DeepSeek R1 is the open-weight reasoning model that reset price expectations in early 2025. 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. They diverge most on price and context window — each quantified below from the models' real specs.
Key differences
Context window: Qwen3.6 27B holds 2× more — 256K (~393 pages) vs 128K (~192 pages). But effective recall usually fades long before the advertised ceiling, so the bigger number only helps if the model reasons over it.
Recency: Qwen3.6 27B is the newer model by about 15 months (released April 22, 2026), usually meaning fresher training data and capabilities.
Specifications
Spec
DeepSeek R1
Qwen3.6 27B
Provider
DeepSeek (China)
Alibaba (China)
Released
January 2025
April 22, 2026
Context window
128K (~192 pages)
256K (~393 pages)
Price (in/out)
$0.55/$2.19 per 1M tokens
Open weight (self-host / free)
Open weight?
Yes — self-hostable
Yes — self-hostable
Modalities
text, code
text, image, code
SWE-Bench Verified
Not published
77.2%
MRCR v2 @ 1M
Not published
Not published
Who wins what
Open-weight reasoning model: DeepSeek R1 — DeepSeek R1 lists open-weight reasoning model among its strengths; Qwen3.6 27B does not.
Transparent chain-of-thought: DeepSeek R1 — DeepSeek R1 lists transparent chain-of-thought among its strengths; Qwen3.6 27B does not.
Low cost: DeepSeek R1 — DeepSeek R1 lists low cost among its strengths; Qwen3.6 27B does not.
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 — 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 carries the larger 256K context.
Dense, so quality per gigabyte of VRAM is high: it fits one consumer GPU when quantised: 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 is the newer of the two.
Far stronger agentic work than its sparse sibling (59.3 against 51.5 on Terminal-Bench 2.0): Qwen3.6 27B — Qwen3.6 27B lists far stronger agentic work than its sparse sibling (59.3 against 51.5 on Terminal-Bench 2.0) among its strengths; DeepSeek R1 does not.
Lowest cost at scale: Qwen3.6 27B — Its weights are open, so at volume you pay for your own hardware instead of DeepSeek R1's $0.55/$2.19 per 1M tokens.
Largest single-prompt input: Qwen3.6 27B — Its 256K window is about 2× larger than DeepSeek R1's 128K, fitting roughly 393 pages in one prompt.
Which should you pick?
A cost-sensitive startup shipping high volume: Qwen3.6 27B — At Open weight (self-host / free) it undercuts DeepSeek R1, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases: Qwen3.6 27B — Larger 256K window fits more in one prompt.
Anyone whose priority is open-weight reasoning model: DeepSeek R1 — It is specifically built for that.
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 — That is its strongest area.
DeepSeek R1: where it fits
The open-weight reasoning model that reset price expectations in early 2025. Released January 2025 by DeepSeek, it is built for open-weight reasoning model, transparent chain-of-thought, low cost, and strong maths and code.
Its trade-offs are real: older than V4, smaller 128K context, and text/code focused. At $0.55 in / $2.19 out per million tokens, it sits in the budget price band.
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: 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.
The bottom line for this matchup
DeepSeek R1 and Qwen3.6 27B overlap enough that the right pick depends on your specific job. Qwen3.6 27B costs less per token; Qwen3.6 27B holds the larger context; and each leads in its own area — DeepSeek R1 for open-weight reasoning model, Qwen3.6 27B 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. Rather than crowning one, run the same hard task through both once and let the results decide.
Frequently asked questions
Is DeepSeek R1 or Qwen3.6 27B better for coding?
Public SWE-Bench figures are not available for DeepSeek R1, so the honest test is your own repository — run an identical real bug through both. By design, DeepSeek R1 leans toward open-weight reasoning model while Qwen3.6 27B leans toward 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, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, DeepSeek R1 or Qwen3.6 27B?
Qwen3.6 27B is cheaper — $0.55/$2.19 per 1M tokens vs Open weight (self-host / free).
Which has the bigger context window?
Qwen3.6 27B — 256K vs 128K, about 2× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both DeepSeek R1 and Qwen3.6 27B together?
Yes — a multi-model platform like LumiChats gives you DeepSeek R1, Qwen3.6 27B and 40+ others under one ₹69/day pass (about $1/day), so you can draft with one and cross-check with the other instead of buying two subscriptions.
Which is newer, DeepSeek R1 or Qwen3.6 27B?
Qwen3.6 27B — released April 22, 2026, about 15 months after DeepSeek R1.
DeepSeek R1 vs Qwen3.6 27B
DeepSeek · China | Alibaba · China · Updated June 2026
Quick verdict
Pick DeepSeek R1 for open-weight reasoning model or transparent chain-of-thought. Pick Qwen3.6 27B 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 or dense, so quality per gigabyte of vram is high: it fits one consumer gpu when quantised. On a tight budget at scale, Qwen3.6 27B is the value pick.
DeepSeek R1 (DeepSeek) and Qwen3.6 27B (Alibaba) are two of the models people most often weigh against each other in 2026. DeepSeek R1 is the open-weight reasoning model that reset price expectations in early 2025. 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. They diverge most on price and context window — each quantified below from the models' real specs.
Key differences at a glance
▸Context window: Qwen3.6 27B holds 2× more — 256K (~393 pages) vs 128K (~192 pages). But effective recall usually fades long before the advertised ceiling, so the bigger number only helps if the model reasons over it.
▸Recency: Qwen3.6 27B is the newer model by about 15 months (released April 22, 2026), usually meaning fresher training data and capabilities.
Side-by-side specs
Spec
DeepSeek R1
Qwen3.6 27B
Provider
DeepSeek (China)
Alibaba (China)
Released
January 2025
April 22, 2026
Context window
128K (~192 pages)
256K (~393 pages)
Price (in/out)
$0.55/$2.19 per 1M tokens
Open weight (self-host / free)
Open weight?
Yes — self-hostable
Yes — self-hostable
Modalities
text, code
text, image, code
SWE-Bench Verified
Not published
77.2%
MRCR v2 @ 1M
Not published
Not published
Who wins what
Open-weight reasoning model
DeepSeek R1
DeepSeek R1 lists open-weight reasoning model among its strengths; Qwen3.6 27B does not.
Transparent chain-of-thought
DeepSeek R1
DeepSeek R1 lists transparent chain-of-thought among its strengths; Qwen3.6 27B does not.
Low cost
DeepSeek R1
DeepSeek R1 lists low cost among its strengths; Qwen3.6 27B does not.
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
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 carries the larger 256K context.
Dense, so quality per gigabyte of VRAM is high: it fits one consumer GPU when quantised
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 is the newer of the two.
Far stronger agentic work than its sparse sibling (59.3 against 51.5 on Terminal-Bench 2.0)
Qwen3.6 27B
Qwen3.6 27B lists far stronger agentic work than its sparse sibling (59.3 against 51.5 on Terminal-Bench 2.0) among its strengths; DeepSeek R1 does not.
Lowest cost at scale
Qwen3.6 27B
Its weights are open, so at volume you pay for your own hardware instead of DeepSeek R1's $0.55/$2.19 per 1M tokens.
Largest single-prompt input
Qwen3.6 27B
Its 256K window is about 2× larger than DeepSeek R1's 128K, fitting roughly 393 pages in one prompt.
Which should you pick?
A cost-sensitive startup shipping high volume
→ Qwen3.6 27B
At Open weight (self-host / free) it undercuts DeepSeek R1, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases
→ Qwen3.6 27B
Larger 256K window fits more in one prompt.
Anyone whose priority is open-weight reasoning model
→ DeepSeek R1
It is specifically built for that.
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
That is its strongest area.
DeepSeek R1: where it fits
The open-weight reasoning model that reset price expectations in early 2025. Released January 2025 by DeepSeek, it is built for open-weight reasoning model, transparent chain-of-thought, low cost, and strong maths and code.
Its trade-offs are real: older than V4, smaller 128K context, and text/code focused. At $0.55 in / $2.19 out per million tokens, it sits in the budget price band.
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: 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.
The bottom line for this matchup
DeepSeek R1 and Qwen3.6 27B overlap enough that the right pick depends on your specific job. Qwen3.6 27B costs less per token; Qwen3.6 27B holds the larger context; and each leads in its own area — DeepSeek R1 for open-weight reasoning model, Qwen3.6 27B 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. Rather than crowning one, run the same hard task through both once and let the results decide.
Want both DeepSeek R1 and Qwen3.6 27B 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.
Public SWE-Bench figures are not available for DeepSeek R1, so the honest test is your own repository — run an identical real bug through both. By design, DeepSeek R1 leans toward open-weight reasoning model while Qwen3.6 27B leans toward 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, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, DeepSeek R1 or Qwen3.6 27B?
Qwen3.6 27B is cheaper — $0.55/$2.19 per 1M tokens vs Open weight (self-host / free).
Which has the bigger context window?
Qwen3.6 27B — 256K vs 128K, about 2× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both DeepSeek R1 and Qwen3.6 27B together?
Yes — a multi-model platform like LumiChats gives you DeepSeek R1, Qwen3.6 27B and 40+ others under one ₹69/day pass (about $1/day), so you can draft with one and cross-check with the other instead of buying two subscriptions.
Which is newer, DeepSeek R1 or Qwen3.6 27B?
Qwen3.6 27B — released April 22, 2026, about 15 months after DeepSeek R1.
Specifications and benchmarks reflect publicly reported figures as of June 2026 and may change as providers release updates. Always verify on your own workload.