Pick DeepSeek V4 for near-frontier coding at ~1/12 the cost or open mit-licensed weights you can self-host. 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 V4 (DeepSeek) and Qwen3.6 27B (Alibaba) are two of the models people most often weigh against each other in 2026. DeepSeek V4 is china's open-weight price earthquake — near-frontier capability at roughly a twelfth of GPT-5.5's cost. 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, context window and coding benchmarks — each quantified below from the models' real specs.
Key differences
Context window: DeepSeek V4 holds 3.8× more — 1M (~1,500 pages) vs 256K (~393 pages). But effective recall usually fades long before the advertised ceiling, so the bigger number only helps if the model reasons over it.
Coding: DeepSeek V4 leads SWE-Bench Verified by 3.4 points (80.6% vs 77.2%) — a real edge on hard, real-world software tasks.
Specifications
Spec
DeepSeek V4
Qwen3.6 27B
Provider
DeepSeek (China)
Alibaba (China)
Released
April 24, 2026
April 22, 2026
Context window
1M (~1,500 pages)
256K (~393 pages)
Price (in/out)
$0.435/$0.87 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
80.6%
77.2%
MRCR v2 @ 1M
Not published
Not published
Who wins what
Near-frontier coding at ~1/12 the cost: DeepSeek V4 — It scores 80.6% on SWE-Bench Verified against Qwen3.6 27B's 77.2% — a 3.4-point edge on real repository work.
Open MIT-licensed weights you can self-host: DeepSeek V4 — China's open-weight price earthquake — near-frontier capability at roughly a twelfth of GPT-5.5's cost — and it leads SWE-Bench Verified 80.6% to 77.2%.
No long-context surcharge: DeepSeek V4 — Its 1M window holds about 3.8× more than Qwen3.6 27B's 256K in a single prompt.
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 — DeepSeek V4 is comparatively weak here — trails the very best on hardest agentic coding
Dense, so quality per gigabyte of VRAM is high: it fits one consumer GPU when quantised: Qwen3.6 27B — Qwen3.6 27B lists dense, so quality per gigabyte of VRAM is high: it fits one consumer GPU when quantised among its strengths; DeepSeek V4 does not.
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 V4 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 V4's $0.435/$0.87 per 1M tokens.
Largest single-prompt input: DeepSeek V4 — Its 1M window is about 3.8× larger than Qwen3.6 27B's 256K, fitting roughly 1,500 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 V4, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases: DeepSeek V4 — Larger 1M window fits more in one prompt.
Anyone whose priority is near-frontier coding at ~1/12 the cost: DeepSeek V4 — 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 V4: where it fits
China's open-weight price earthquake — near-frontier capability at roughly a twelfth of GPT-5.5's cost. Released April 24, 2026 by DeepSeek, it is built for near-frontier coding at ~1/12 the cost, open MIT-licensed weights you can self-host, no long-context surcharge, and highest LiveCodeBench result.
Its trade-offs are real: trails the very best on hardest agentic coding, and text/code focused, less multimodal. At $0.435 in / $0.87 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 V4 and Qwen3.6 27B overlap enough that the right pick depends on your specific job. Qwen3.6 27B costs less per token; DeepSeek V4 holds the larger context; and each leads in its own area — DeepSeek V4 for near-frontier coding at ~1/12 the cost, 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 V4 or Qwen3.6 27B better for coding?
On SWE-Bench Verified, DeepSeek V4 scores 80.6% and Qwen3.6 27B scores 77.2% — DeepSeek V4 has the measurable edge.
Which is cheaper, DeepSeek V4 or Qwen3.6 27B?
Qwen3.6 27B is cheaper — $0.435/$0.87 per 1M tokens vs Open weight (self-host / free).
Which has the bigger context window?
DeepSeek V4 — 1M vs 256K, about 3.8× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both DeepSeek V4 and Qwen3.6 27B together?
Yes — a multi-model platform like LumiChats gives you DeepSeek V4, 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 V4 or Qwen3.6 27B?
DeepSeek V4 — released April 24, 2026, about 2 days after Qwen3.6 27B.
DeepSeek V4 vs Qwen3.6 27B
DeepSeek · China | Alibaba · China · Updated June 2026
Quick verdict
Pick DeepSeek V4 for near-frontier coding at ~1/12 the cost or open mit-licensed weights you can self-host. 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 V4 (DeepSeek) and Qwen3.6 27B (Alibaba) are two of the models people most often weigh against each other in 2026. DeepSeek V4 is china's open-weight price earthquake — near-frontier capability at roughly a twelfth of GPT-5.5's cost. 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, context window and coding benchmarks — each quantified below from the models' real specs.
Key differences at a glance
▸Context window: DeepSeek V4 holds 3.8× more — 1M (~1,500 pages) vs 256K (~393 pages). But effective recall usually fades long before the advertised ceiling, so the bigger number only helps if the model reasons over it.
▸Coding: DeepSeek V4 leads SWE-Bench Verified by 3.4 points (80.6% vs 77.2%) — a real edge on hard, real-world software tasks.
Side-by-side specs
Spec
DeepSeek V4
Qwen3.6 27B
Provider
DeepSeek (China)
Alibaba (China)
Released
April 24, 2026
April 22, 2026
Context window
1M (~1,500 pages)
256K (~393 pages)
Price (in/out)
$0.435/$0.87 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
80.6%
77.2%
MRCR v2 @ 1M
Not published
Not published
Who wins what
Near-frontier coding at ~1/12 the cost
DeepSeek V4
It scores 80.6% on SWE-Bench Verified against Qwen3.6 27B's 77.2% — a 3.4-point edge on real repository work.
Open MIT-licensed weights you can self-host
DeepSeek V4
China's open-weight price earthquake — near-frontier capability at roughly a twelfth of GPT-5.5's cost — and it leads SWE-Bench Verified 80.6% to 77.2%.
No long-context surcharge
DeepSeek V4
Its 1M window holds about 3.8× more than Qwen3.6 27B's 256K in a single prompt.
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
DeepSeek V4 is comparatively weak here — trails the very best on hardest agentic coding
Dense, so quality per gigabyte of VRAM is high: it fits one consumer GPU when quantised
Qwen3.6 27B
Qwen3.6 27B lists dense, so quality per gigabyte of VRAM is high: it fits one consumer GPU when quantised among its strengths; DeepSeek V4 does not.
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 V4 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 V4's $0.435/$0.87 per 1M tokens.
Largest single-prompt input
DeepSeek V4
Its 1M window is about 3.8× larger than Qwen3.6 27B's 256K, fitting roughly 1,500 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 V4, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases
→ DeepSeek V4
Larger 1M window fits more in one prompt.
Anyone whose priority is near-frontier coding at ~1/12 the cost
→ DeepSeek V4
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 V4: where it fits
China's open-weight price earthquake — near-frontier capability at roughly a twelfth of GPT-5.5's cost. Released April 24, 2026 by DeepSeek, it is built for near-frontier coding at ~1/12 the cost, open MIT-licensed weights you can self-host, no long-context surcharge, and highest LiveCodeBench result.
Its trade-offs are real: trails the very best on hardest agentic coding, and text/code focused, less multimodal. At $0.435 in / $0.87 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 V4 and Qwen3.6 27B overlap enough that the right pick depends on your specific job. Qwen3.6 27B costs less per token; DeepSeek V4 holds the larger context; and each leads in its own area — DeepSeek V4 for near-frontier coding at ~1/12 the cost, 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 V4 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.
On SWE-Bench Verified, DeepSeek V4 scores 80.6% and Qwen3.6 27B scores 77.2% — DeepSeek V4 has the measurable edge.
Which is cheaper, DeepSeek V4 or Qwen3.6 27B?
Qwen3.6 27B is cheaper — $0.435/$0.87 per 1M tokens vs Open weight (self-host / free).
Which has the bigger context window?
DeepSeek V4 — 1M vs 256K, about 3.8× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both DeepSeek V4 and Qwen3.6 27B together?
Yes — a multi-model platform like LumiChats gives you DeepSeek V4, 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 V4 or Qwen3.6 27B?
DeepSeek V4 — released April 24, 2026, about 2 days after Qwen3.6 27B.
Specifications and benchmarks reflect publicly reported figures as of June 2026 and may change as providers release updates. Always verify on your own workload.