Pick MiMo-V2.5-Pro for complex software engineering (top-ranked on swe-bench pro) or long-horizon autonomous tasks (1,000+ tool calls). 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.
MiMo-V2.5-Pro (Xiaomi) and Qwen3.6 27B (Alibaba) are two of the models people most often weigh against each other in 2026. MiMo-V2.5-Pro is xiaomi's flagship agentic model — autonomous, long-horizon software engineering at a fraction of frontier 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 and context window — each quantified below from the models' real specs.
Key differences
Context window: MiMo-V2.5-Pro 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.
Specifications
Spec
MiMo-V2.5-Pro
Qwen3.6 27B
Provider
Xiaomi (China)
Alibaba (China)
Released
April 22, 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, image, video, code
text, image, code
SWE-Bench Verified
Not published
77.2%
MRCR v2 @ 1M
Not published
Not published
Who wins what
Complex software engineering (top-ranked on SWE-bench Pro): MiMo-V2.5-Pro — Qwen3.6 27B is comparatively weak here — its SWE-Bench score comes from Alibaba's internal scaffold rather than the standard public harness
Long-horizon autonomous tasks (1,000+ tool calls): MiMo-V2.5-Pro — Its 1M window holds about 3.8× more than Qwen3.6 27B's 256K in a single prompt.
Strong on GDPVal and ClawEval: MiMo-V2.5-Pro — Xiaomi's flagship agentic model — autonomous, long-horizon software engineering at a fraction of frontier cost — and it carries the larger 1M context.
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 — MiMo-V2.5-Pro is comparatively weak here — benchmark rankings are largely vendor-stated
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; MiMo-V2.5-Pro 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; MiMo-V2.5-Pro does not.
Lowest cost at scale: Qwen3.6 27B — Its weights are open, so at volume you pay for your own hardware instead of MiMo-V2.5-Pro's $0.435/$0.87 per 1M tokens.
Largest single-prompt input: MiMo-V2.5-Pro — 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 MiMo-V2.5-Pro, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases: MiMo-V2.5-Pro — Larger 1M window fits more in one prompt.
Anyone whose priority is complex software engineering (top-ranked on swe-bench pro): MiMo-V2.5-Pro — 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.
MiMo-V2.5-Pro: where it fits
Xiaomi's flagship agentic model — autonomous, long-horizon software engineering at a fraction of frontier cost. Released April 22, 2026 by Xiaomi, it is built for complex software engineering (top-ranked on SWE-bench Pro), long-horizon autonomous tasks (1,000+ tool calls), strong on GDPVal and ClawEval, and agent-framework integration.
Its trade-offs are real: benchmark rankings are largely vendor-stated, and limited Western adoption and tooling. 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
MiMo-V2.5-Pro and Qwen3.6 27B overlap enough that the right pick depends on your specific job. Qwen3.6 27B costs less per token; MiMo-V2.5-Pro holds the larger context; and each leads in its own area — MiMo-V2.5-Pro for complex software engineering (top-ranked on swe-bench pro), 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 MiMo-V2.5-Pro or Qwen3.6 27B better for coding?
Public SWE-Bench figures are not available for MiMo-V2.5-Pro, so the honest test is your own repository — run an identical real bug through both. By design, MiMo-V2.5-Pro leans toward complex software engineering (top-ranked on swe-bench pro) 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, MiMo-V2.5-Pro 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?
MiMo-V2.5-Pro — 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 MiMo-V2.5-Pro and Qwen3.6 27B together?
Yes — a multi-model platform like LumiChats gives you MiMo-V2.5-Pro, 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, MiMo-V2.5-Pro or Qwen3.6 27B?
They were released around the same time (April 22, 2026 and April 22, 2026).
MiMo-V2.5-Pro vs Qwen3.6 27B
Xiaomi · China | Alibaba · China · Updated June 2026
Quick verdict
Pick MiMo-V2.5-Pro for complex software engineering (top-ranked on swe-bench pro) or long-horizon autonomous tasks (1,000+ tool calls). 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.
MiMo-V2.5-Pro (Xiaomi) and Qwen3.6 27B (Alibaba) are two of the models people most often weigh against each other in 2026. MiMo-V2.5-Pro is xiaomi's flagship agentic model — autonomous, long-horizon software engineering at a fraction of frontier 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 and context window — each quantified below from the models' real specs.
Key differences at a glance
▸Context window: MiMo-V2.5-Pro 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.
Side-by-side specs
Spec
MiMo-V2.5-Pro
Qwen3.6 27B
Provider
Xiaomi (China)
Alibaba (China)
Released
April 22, 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, image, video, code
text, image, code
SWE-Bench Verified
Not published
77.2%
MRCR v2 @ 1M
Not published
Not published
Who wins what
Complex software engineering (top-ranked on SWE-bench Pro)
MiMo-V2.5-Pro
Qwen3.6 27B is comparatively weak here — its SWE-Bench score comes from Alibaba's internal scaffold rather than the standard public harness
Long-horizon autonomous tasks (1,000+ tool calls)
MiMo-V2.5-Pro
Its 1M window holds about 3.8× more than Qwen3.6 27B's 256K in a single prompt.
Strong on GDPVal and ClawEval
MiMo-V2.5-Pro
Xiaomi's flagship agentic model — autonomous, long-horizon software engineering at a fraction of frontier cost — and it carries the larger 1M context.
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
MiMo-V2.5-Pro is comparatively weak here — benchmark rankings are largely vendor-stated
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; MiMo-V2.5-Pro 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; MiMo-V2.5-Pro does not.
Lowest cost at scale
Qwen3.6 27B
Its weights are open, so at volume you pay for your own hardware instead of MiMo-V2.5-Pro's $0.435/$0.87 per 1M tokens.
Largest single-prompt input
MiMo-V2.5-Pro
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 MiMo-V2.5-Pro, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases
→ MiMo-V2.5-Pro
Larger 1M window fits more in one prompt.
Anyone whose priority is complex software engineering (top-ranked on swe-bench pro)
→ MiMo-V2.5-Pro
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.
MiMo-V2.5-Pro: where it fits
Xiaomi's flagship agentic model — autonomous, long-horizon software engineering at a fraction of frontier cost. Released April 22, 2026 by Xiaomi, it is built for complex software engineering (top-ranked on SWE-bench Pro), long-horizon autonomous tasks (1,000+ tool calls), strong on GDPVal and ClawEval, and agent-framework integration.
Its trade-offs are real: benchmark rankings are largely vendor-stated, and limited Western adoption and tooling. 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
MiMo-V2.5-Pro and Qwen3.6 27B overlap enough that the right pick depends on your specific job. Qwen3.6 27B costs less per token; MiMo-V2.5-Pro holds the larger context; and each leads in its own area — MiMo-V2.5-Pro for complex software engineering (top-ranked on swe-bench pro), 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 MiMo-V2.5-Pro 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.
Is MiMo-V2.5-Pro or Qwen3.6 27B better for coding?
Public SWE-Bench figures are not available for MiMo-V2.5-Pro, so the honest test is your own repository — run an identical real bug through both. By design, MiMo-V2.5-Pro leans toward complex software engineering (top-ranked on swe-bench pro) 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, MiMo-V2.5-Pro 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?
MiMo-V2.5-Pro — 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 MiMo-V2.5-Pro and Qwen3.6 27B together?
Yes — a multi-model platform like LumiChats gives you MiMo-V2.5-Pro, 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, MiMo-V2.5-Pro or Qwen3.6 27B?
They were released around the same time (April 22, 2026 and April 22, 2026).
Specifications and benchmarks reflect publicly reported figures as of June 2026 and may change as providers release updates. Always verify on your own workload.