Pick Kimi K2.7 Code for long-horizon agentic software engineering or token-efficient reasoning (~30% fewer than k2.6). Pick Qwen3.6 35B A3B for extreme sparsity — only 3b of 35b parameters active per token, giving near-3b inference cost or runs at roughly 120 tokens per second on a single 24gb consumer gpu. On a tight budget at scale, Qwen3.6 35B A3B is the value pick.
Kimi K2.7 Code (Moonshot AI) and Qwen3.6 35B A3B (Alibaba) are two of the models people most often weigh against each other in 2026. Kimi K2.7 Code is moonshot AI's open-weight 1T-parameter MoE model (32B active) tuned for long-horizon agentic coding, always reasoning yet ~30% more token-efficient than K2.6. 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. Their biggest split is price, and the breakdown below shows exactly how that plays out for your workload.
Key differences
Context window: both advertise 256K (~393 pages). Tie on paper — test on your own long inputs, since usable recall varies by model.
Recency: Kimi K2.7 Code is the newer model by about 57 days (released June 12, 2026), usually meaning fresher training data and capabilities.
Specifications
Spec
Kimi K2.7 Code
Qwen3.6 35B A3B
Provider
Moonshot AI (China)
Alibaba (China)
Released
June 12, 2026
April 16, 2026
Context window
256K (~393 pages)
256K (~393 pages)
Price (in/out)
$0.95/$4 per 1M tokens
Open weight (self-host / free)
Open weight?
Yes — self-hostable
Yes — self-hostable
Modalities
text, image, video, code
text, image, code
SWE-Bench Verified
Not published
73.4%
MRCR v2 @ 1M
Not published
Not published
Who wins what
Long-horizon agentic software engineering: Kimi K2.7 Code — Moonshot AI's open-weight 1T-parameter MoE model (32B active) tuned for long-horizon agentic coding, always reasoning yet ~30% more token-efficient than K2.6 — and it is the newer of the two.
Token-efficient reasoning (~30% fewer than K2.6): Kimi K2.7 Code — Qwen3.6 35B A3B is comparatively weak here — all 35B parameters must stay resident in VRAM even though only 3B compute per token
Open-weight 1T MoE, self-hostable: Kimi K2.7 Code — Kimi K2.7 Code lists open-weight 1T MoE, self-hostable among its strengths; Qwen3.6 35B A3B does not.
Extreme sparsity — only 3B of 35B parameters active per token, giving near-3B inference cost: Qwen3.6 35B A3B — Qwen3.6 35B A3B lists extreme sparsity — only 3B of 35B parameters active per token, giving near-3B inference cost among its strengths; Kimi K2.7 Code does not.
Runs at roughly 120 tokens per second on a single 24GB consumer GPU: Qwen3.6 35B A3B — Qwen3.6 35B A3B lists runs at roughly 120 tokens per second on a single 24GB consumer GPU among its strengths; Kimi K2.7 Code does not.
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; Kimi K2.7 Code does not.
Lowest cost at scale: Qwen3.6 35B A3B — Its weights are open, so at volume you pay for your own hardware instead of Kimi K2.7 Code's $0.95/$4 per 1M tokens.
Which should you pick?
A cost-sensitive startup shipping high volume: Qwen3.6 35B A3B — At Open weight (self-host / free) it undercuts Kimi K2.7 Code, and on millions of tokens that margin decides the monthly bill.
Anyone whose priority is long-horizon agentic software engineering: Kimi K2.7 Code — 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.
Kimi K2.7 Code: where it fits
Moonshot AI's open-weight 1T-parameter MoE model (32B active) tuned for long-horizon agentic coding, always reasoning yet ~30% more token-efficient than K2.6. Released June 12, 2026 by Moonshot AI, it is built for long-horizon agentic software engineering, token-efficient reasoning (~30% fewer than K2.6), open-weight 1T MoE, self-hostable, and multi-turn tool use with preserved reasoning.
Its trade-offs are real: only self-reported benchmarks; no SWE-Bench Verified, and thinking mode and sampling params can't be disabled. At $0.95 in / $4 out per million tokens, it sits in the budget price band.
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
Kimi K2.7 Code and Qwen3.6 35B A3B overlap enough that the right pick depends on your specific job. Qwen3.6 35B A3B costs less per token; and each leads in its own area — Kimi K2.7 Code for long-horizon agentic software engineering, Qwen3.6 35B A3B for extreme sparsity — only 3b of 35b parameters active per token, giving near-3b inference cost. Rather than crowning one, run the same hard task through both once and let the results decide.
Frequently asked questions
Is Kimi K2.7 Code or Qwen3.6 35B A3B better for coding?
Public SWE-Bench figures are not available for Kimi K2.7 Code, so the honest test is your own repository — run an identical real bug through both. By design, Kimi K2.7 Code leans toward long-horizon agentic software engineering while Qwen3.6 35B A3B leans toward extreme sparsity — only 3b of 35b parameters active per token, giving near-3b inference cost, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, Kimi K2.7 Code or Qwen3.6 35B A3B?
Qwen3.6 35B A3B is cheaper — $0.95/$4 per 1M tokens vs Open weight (self-host / free).
Which has the bigger context window?
Both advertise 256K (~393 pages). Remember advertised ≠ usable: recall typically degrades before the ceiling.
Can I use both Kimi K2.7 Code and Qwen3.6 35B A3B together?
Yes — a multi-model platform like LumiChats gives you Kimi K2.7 Code, Qwen3.6 35B A3B 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, Kimi K2.7 Code or Qwen3.6 35B A3B?
Kimi K2.7 Code — released June 12, 2026, about 57 days after Qwen3.6 35B A3B.
Kimi K2.7 Code vs Qwen3.6 35B A3B
Moonshot AI · China | Alibaba · China · Updated June 2026
Quick verdict
Pick Kimi K2.7 Code for long-horizon agentic software engineering or token-efficient reasoning (~30% fewer than k2.6). Pick Qwen3.6 35B A3B for extreme sparsity — only 3b of 35b parameters active per token, giving near-3b inference cost or runs at roughly 120 tokens per second on a single 24gb consumer gpu. On a tight budget at scale, Qwen3.6 35B A3B is the value pick.
Kimi K2.7 Code (Moonshot AI) and Qwen3.6 35B A3B (Alibaba) are two of the models people most often weigh against each other in 2026. Kimi K2.7 Code is moonshot AI's open-weight 1T-parameter MoE model (32B active) tuned for long-horizon agentic coding, always reasoning yet ~30% more token-efficient than K2.6. 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. Their biggest split is price, and the breakdown below shows exactly how that plays out for your workload.
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.
▸Recency: Kimi K2.7 Code is the newer model by about 57 days (released June 12, 2026), usually meaning fresher training data and capabilities.
Side-by-side specs
Spec
Kimi K2.7 Code
Qwen3.6 35B A3B
Provider
Moonshot AI (China)
Alibaba (China)
Released
June 12, 2026
April 16, 2026
Context window
256K (~393 pages)
256K (~393 pages)
Price (in/out)
$0.95/$4 per 1M tokens
Open weight (self-host / free)
Open weight?
Yes — self-hostable
Yes — self-hostable
Modalities
text, image, video, code
text, image, code
SWE-Bench Verified
Not published
73.4%
MRCR v2 @ 1M
Not published
Not published
Who wins what
Long-horizon agentic software engineering
Kimi K2.7 Code
Moonshot AI's open-weight 1T-parameter MoE model (32B active) tuned for long-horizon agentic coding, always reasoning yet ~30% more token-efficient than K2.6 — and it is the newer of the two.
Token-efficient reasoning (~30% fewer than K2.6)
Kimi K2.7 Code
Qwen3.6 35B A3B is comparatively weak here — all 35B parameters must stay resident in VRAM even though only 3B compute per token
Open-weight 1T MoE, self-hostable
Kimi K2.7 Code
Kimi K2.7 Code lists open-weight 1T MoE, self-hostable among its strengths; Qwen3.6 35B A3B does not.
Extreme sparsity — only 3B of 35B parameters active per token, giving near-3B inference cost
Qwen3.6 35B A3B
Qwen3.6 35B A3B lists extreme sparsity — only 3B of 35B parameters active per token, giving near-3B inference cost among its strengths; Kimi K2.7 Code does not.
Runs at roughly 120 tokens per second on a single 24GB consumer GPU
Qwen3.6 35B A3B
Qwen3.6 35B A3B lists runs at roughly 120 tokens per second on a single 24GB consumer GPU among its strengths; Kimi K2.7 Code does not.
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; Kimi K2.7 Code does not.
Lowest cost at scale
Qwen3.6 35B A3B
Its weights are open, so at volume you pay for your own hardware instead of Kimi K2.7 Code's $0.95/$4 per 1M tokens.
Which should you pick?
A cost-sensitive startup shipping high volume
→ Qwen3.6 35B A3B
At Open weight (self-host / free) it undercuts Kimi K2.7 Code, and on millions of tokens that margin decides the monthly bill.
Anyone whose priority is long-horizon agentic software engineering
→ Kimi K2.7 Code
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.
Kimi K2.7 Code: where it fits
Moonshot AI's open-weight 1T-parameter MoE model (32B active) tuned for long-horizon agentic coding, always reasoning yet ~30% more token-efficient than K2.6. Released June 12, 2026 by Moonshot AI, it is built for long-horizon agentic software engineering, token-efficient reasoning (~30% fewer than K2.6), open-weight 1T MoE, self-hostable, and multi-turn tool use with preserved reasoning.
Its trade-offs are real: only self-reported benchmarks; no SWE-Bench Verified, and thinking mode and sampling params can't be disabled. At $0.95 in / $4 out per million tokens, it sits in the budget price band.
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
Kimi K2.7 Code and Qwen3.6 35B A3B overlap enough that the right pick depends on your specific job. Qwen3.6 35B A3B costs less per token; and each leads in its own area — Kimi K2.7 Code for long-horizon agentic software engineering, Qwen3.6 35B A3B for extreme sparsity — only 3b of 35b parameters active per token, giving near-3b inference cost. Rather than crowning one, run the same hard task through both once and let the results decide.
Want both Kimi K2.7 Code 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 Kimi K2.7 Code or Qwen3.6 35B A3B better for coding?
Public SWE-Bench figures are not available for Kimi K2.7 Code, so the honest test is your own repository — run an identical real bug through both. By design, Kimi K2.7 Code leans toward long-horizon agentic software engineering while Qwen3.6 35B A3B leans toward extreme sparsity — only 3b of 35b parameters active per token, giving near-3b inference cost, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, Kimi K2.7 Code or Qwen3.6 35B A3B?
Qwen3.6 35B A3B is cheaper — $0.95/$4 per 1M tokens vs Open weight (self-host / free).
Which has the bigger context window?
Both advertise 256K (~393 pages). Remember advertised ≠ usable: recall typically degrades before the ceiling.
Can I use both Kimi K2.7 Code and Qwen3.6 35B A3B together?
Yes — a multi-model platform like LumiChats gives you Kimi K2.7 Code, Qwen3.6 35B A3B 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, Kimi K2.7 Code or Qwen3.6 35B A3B?
Kimi K2.7 Code — released June 12, 2026, about 57 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.