MiMo-V2.5-Pro vs Qwen3.8-Flash-Next
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 Qwen3.8-Flash-Next for swe-bench pro (62.5, ahead of claude opus 4.6 max's 53.4) or cost efficiency: ~1/9th the training cost of qwen3.7-plus, ~12x cheaper api than flagship qwen3.8-max. On a tight budget at scale, Qwen3.8-Flash-Next is the value pick.
MiMo-V2.5-Pro (Xiaomi) and Qwen3.8-Flash-Next (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. Qwen3.8-Flash-Next is alibaba's open-weight preview of its next-generation Qwen4 hybrid-attention architecture, released August 26, 2026 as a 125B-parameter (6B active) MoE model priced roughly 12x below its own flagship Qwen3.8-Max. They diverge most on price and context window — each quantified below from the models' real specs.
Key differences at a glance
- ▸Price: Qwen3.8-Flash-Next is about 2.7× cheaper on input ($0.16/$0.47 per 1M tokens vs $0.435/$0.87 per 1M tokens) — meaningful once you are processing millions of tokens a month.
- ▸Context window: MiMo-V2.5-Pro holds 3.8× more — 1M (~1,500 pages) vs 262K tokens natively (extensible to 1M with YaRN) (~393 pages). But effective recall usually fades long before the advertised ceiling, so the bigger number only helps if the model reasons over it.
- ▸Recency: Qwen3.8-Flash-Next is the newer model by about 4 months (released August 26, 2026), usually meaning fresher training data and capabilities.
Side-by-side specs
| Spec | MiMo-V2.5-Pro | Qwen3.8-Flash-Next |
|---|---|---|
| Provider | Xiaomi (China) | Alibaba (China) |
| Released | April 22, 2026 | August 26, 2026 |
| Context window | 1M (~1,500 pages) | 262K tokens natively (extensible to 1M with YaRN) (~393 pages) |
| Price (in/out) | $0.435/$0.87 per 1M tokens | $0.16/$0.47 per 1M tokens |
| Open weight? | Yes — self-hostable | Yes — self-hostable |
| Modalities | text, image, video, code | text, image, video |
| SWE-Bench Verified | Not published | Not published |
| MRCR v2 @ 1M | Not published | Not published |
Who wins what
Complex software engineering (top-ranked on SWE-bench Pro)
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.
Long-horizon autonomous tasks (1,000+ tool calls)
MiMo-V2.5-Pro
Its 1M window holds about 3.8× more than Qwen3.8-Flash-Next's 262K tokens natively (extensible to 1M with YaRN) in a single prompt.
Strong on GDPVal and ClawEval
MiMo-V2.5-Pro
MiMo-V2.5-Pro lists strong on GDPVal and ClawEval among its strengths; Qwen3.8-Flash-Next does not.
SWE-bench Pro (62.5, ahead of Claude Opus 4.6 Max's 53.4)
Qwen3.8-Flash-Next
MiMo-V2.5-Pro is comparatively weak here — benchmark rankings are largely vendor-stated
Cost efficiency: ~1/9th the training cost of Qwen3.7-Plus, ~12x cheaper API than flagship Qwen3.8-Max
Qwen3.8-Flash-Next
At $0.16/$0.47 per 1M tokens it undercuts MiMo-V2.5-Pro ($0.435/$0.87 per 1M tokens), and that gap compounds at volume.
Vision-based agentic tasks (AndroidWorld: 84.5)
Qwen3.8-Flash-Next
Alibaba's open-weight preview of its next-generation Qwen4 hybrid-attention architecture, released August 26, 2026 as a 125B-parameter (6B active) MoE model priced roughly 12x below its own flagship Qwen3.8-Max — and it runs cheaper at $0.16/$0.47 per 1M tokens.
Lowest cost at scale
Qwen3.8-Flash-Next
At $0.16/$0.47 per 1M tokens, it is the cheaper of the two — the gap dominates the bill on high-volume workloads.
Largest single-prompt input
MiMo-V2.5-Pro
Its 1M window is about 3.8× larger than Qwen3.8-Flash-Next's 262K tokens natively (extensible to 1M with YaRN), fitting roughly 1,500 pages in one prompt.
Which should you pick?
A cost-sensitive startup shipping high volume
→ Qwen3.8-Flash-Next
At $0.16/$0.47 per 1M tokens 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 swe-bench pro (62.5, ahead of claude opus 4.6 max's 53.4)
→ Qwen3.8-Flash-Next
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.
Qwen3.8-Flash-Next: where it fits
Alibaba's open-weight preview of its next-generation Qwen4 hybrid-attention architecture, released August 26, 2026 as a 125B-parameter (6B active) MoE model priced roughly 12x below its own flagship Qwen3.8-Max. Released August 26, 2026 by Alibaba, it is built for sWE-bench Pro (62.5, ahead of Claude Opus 4.6 Max's 53.4), cost efficiency: ~1/9th the training cost of Qwen3.7-Plus, ~12x cheaper API than flagship Qwen3.8-Max, vision-based agentic tasks (AndroidWorld: 84.5), and previews Qwen4's hybrid gated-DeltaNet plus sparse-attention architecture.
Its trade-offs: trails Claude Opus 4.6 Max on Humanity's Last Exam (35.9 vs 40.0), lower OSWorld 2.0 binary success rate (19.4%), and an open-weight architecture preview rather than Alibaba's polished flagship product. At $0.16 in / $0.47 out per million tokens, it sits in the budget price band.
The bottom line for this matchup
MiMo-V2.5-Pro and Qwen3.8-Flash-Next overlap enough that the right pick depends on your specific job. Qwen3.8-Flash-Next 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), Qwen3.8-Flash-Next for swe-bench pro (62.5, ahead of claude opus 4.6 max's 53.4). Rather than crowning one, run the same hard task through both once and let the results decide.
Want both MiMo-V2.5-Pro and Qwen3.8-Flash-Next 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.
See pricingFrequently asked questions
Is MiMo-V2.5-Pro or Qwen3.8-Flash-Next better for coding?
Public SWE-Bench figures are not available for either model, 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 Qwen3.8-Flash-Next leans toward swe-bench pro (62.5, ahead of claude opus 4.6 max's 53.4), and that positioning usually predicts which feels better on your codebase.
Which is cheaper, MiMo-V2.5-Pro or Qwen3.8-Flash-Next?
Qwen3.8-Flash-Next is cheaper — $0.435/$0.87 per 1M tokens vs $0.16/$0.47 per 1M tokens, roughly 2.7× apart on input.
Which has the bigger context window?
MiMo-V2.5-Pro — 1M vs 262K tokens natively (extensible to 1M with YaRN), 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 Qwen3.8-Flash-Next together?
Yes — a multi-model platform like LumiChats gives you MiMo-V2.5-Pro, Qwen3.8-Flash-Next 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 Qwen3.8-Flash-Next?
Qwen3.8-Flash-Next — released August 26, 2026, about 4 months after MiMo-V2.5-Pro.
Related comparisons
Specifications and benchmarks reflect publicly reported figures as of June 2026 and may change as providers release updates. Always verify on your own workload.