MiMo-V2.6-Flash vs Qwen3 235B A22B (2507)

Xiaomi · China  |  Alibaba · China · Updated June 2026

Quick verdict

Pick MiMo-V2.6-Flash for same natively omnimodal design as pro (text, image, video, audio) at a fraction of the size and price or 309b total parameters, 15b active per token (sparse moe) — a hybrid attention mechanism for efficiency. Pick Qwen3 235B A22B (2507) for deep world knowledge from 235b total parameters (83.0 mmlu-pro, 93.1 mmlu-redux) or exceptional multilingual and alignment results (79.2 arena-hard v2, 85.2 writingbench). On a tight budget at scale, Qwen3 235B A22B (2507) is the value pick.

MiMo-V2.6-Flash (Xiaomi) and Qwen3 235B A22B (2507) (Alibaba) are two of the models people most often weigh against each other in 2026. MiMo-V2.6-Flash is xiaomi's efficient omnimodal MoE model — 309B parameters, 15B active, MIT-licensed, priced at $0.14/$0.28 per million tokens. Qwen3 235B A22B (2507) is an older 235B text-only open mixture-of-experts with broad knowledge and strong writing — but no vision, no thinking mode, and weak coding. They diverge most on price and context window — each quantified below from the models' real specs.

Key differences at a glance

Side-by-side specs

SpecMiMo-V2.6-FlashQwen3 235B A22B (2507)
ProviderXiaomi (China) Alibaba (China)
ReleasedSeptember 21, 2026 July 21, 2025
Context window1M tokens (~1,573 pages) 256K (~393 pages)
Price (in/out)$0.14/$0.28 per 1M tokens Open weight (self-host / free)
Open weight?Yes — self-hostable Yes — self-hostable
Modalitiestext, image, video, audio text, code
SWE-Bench VerifiedNot published Not published
MRCR v2 @ 1MNot published Not published

Who wins what

Same natively omnimodal design as Pro (text, image, video, audio) at a fraction of the size and price

MiMo-V2.6-Flash

Xiaomi's efficient omnimodal MoE model — 309B parameters, 15B active, MIT-licensed, priced at $0.14/$0.28 per million tokens — and it carries the larger 1M tokens context.

309B total parameters, 15B active per token (sparse MoE) — a hybrid attention mechanism for efficiency

MiMo-V2.6-Flash

Its 1M tokens window holds about 4× more than Qwen3 235B A22B (2507)'s 256K in a single prompt.

MIT-licensed, self-hostable, and among the cheapest omnimodal options at $0.14/$0.28 per million tokens

MiMo-V2.6-Flash

Xiaomi's efficient omnimodal MoE model — 309B parameters, 15B active, MIT-licensed, priced at $0.14/$0.28 per million tokens — and it is the newer of the two.

Deep world knowledge from 235B total parameters (83.0 MMLU-Pro, 93.1 MMLU-Redux)

Qwen3 235B A22B (2507)

Qwen3 235B A22B (2507) lists deep world knowledge from 235B total parameters (83.0 MMLU-Pro, 93.1 MMLU-Redux) among its strengths; MiMo-V2.6-Flash does not.

Exceptional multilingual and alignment results (79.2 Arena-Hard v2, 85.2 WritingBench)

Qwen3 235B A22B (2507)

Qwen3 235B A22B (2507) lists exceptional multilingual and alignment results (79.2 Arena-Hard v2, 85.2 WritingBench) among its strengths; MiMo-V2.6-Flash does not.

Outstanding structured logic — 95.0 on ZebraLogic

Qwen3 235B A22B (2507)

Qwen3 235B A22B (2507) lists outstanding structured logic — 95.0 on ZebraLogic among its strengths; MiMo-V2.6-Flash does not.

Lowest cost at scale

Qwen3 235B A22B (2507)

Its weights are open, so at volume you pay for your own hardware instead of MiMo-V2.6-Flash's $0.14/$0.28 per 1M tokens.

Largest single-prompt input

MiMo-V2.6-Flash

Its 1M tokens window is about 4× larger than Qwen3 235B A22B (2507)'s 256K, fitting roughly 1,573 pages in one prompt.

Which should you pick?

A cost-sensitive startup shipping high volume

Qwen3 235B A22B (2507)

At Open weight (self-host / free) it undercuts MiMo-V2.6-Flash, and on millions of tokens that margin decides the monthly bill.

Someone analysing very long documents or codebases

MiMo-V2.6-Flash

Larger 1M tokens window fits more in one prompt.

Anyone whose priority is same natively omnimodal design as pro (text, image, video, audio) at a fraction of the size and price

MiMo-V2.6-Flash

It is specifically built for that.

Anyone whose priority is deep world knowledge from 235b total parameters (83.0 mmlu-pro, 93.1 mmlu-redux)

Qwen3 235B A22B (2507)

That is its strongest area.

MiMo-V2.6-Flash: where it fits

Xiaomi's efficient omnimodal MoE model — 309B parameters, 15B active, MIT-licensed, priced at $0.14/$0.28 per million tokens. Released September 21, 2026 by Xiaomi, it is built for same natively omnimodal design as Pro (text, image, video, audio) at a fraction of the size and price, 309B total parameters, 15B active per token (sparse MoE) — a hybrid attention mechanism for efficiency, and mIT-licensed, self-hostable, and among the cheapest omnimodal options at $0.14/$0.28 per million tokens.

Its trade-offs are real: lower capacity than Pro — expect a real quality gap on the hardest reasoning and generation tasks, and same caveat as Pro: benchmark claims are largely self-reported by Xiaomi at launch, not yet independently verified at scale. At $0.14 in / $0.28 out per million tokens, it sits in the budget price band.

Qwen3 235B A22B (2507): where it fits

An older 235B text-only open mixture-of-experts with broad knowledge and strong writing — but no vision, no thinking mode, and weak coding. Released July 21, 2025 by Alibaba, it is built for deep world knowledge from 235B total parameters (83.0 MMLU-Pro, 93.1 MMLU-Redux), exceptional multilingual and alignment results (79.2 Arena-Hard v2, 85.2 WritingBench), outstanding structured logic — 95.0 on ZebraLogic, and no thinking mode, which makes latency and token spend entirely predictable.

Its trade-offs: nearly a year old and superseded — Artificial Analysis now steers users to Qwen3.5-397B instead, text-only with no vision, and the absence of a thinking mode caps its hardest reasoning, coding is weak by 2026 standards, and it publishes no SWE-Bench score to compare on, and its 235B weights need roughly 438GB in BF16, far beyond consumer hardware. 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-V2.6-Flash and Qwen3 235B A22B (2507) overlap enough that the right pick depends on your specific job. Qwen3 235B A22B (2507) costs less per token; MiMo-V2.6-Flash holds the larger context; and each leads in its own area — MiMo-V2.6-Flash for same natively omnimodal design as pro (text, image, video, audio) at a fraction of the size and price, Qwen3 235B A22B (2507) for deep world knowledge from 235b total parameters (83.0 mmlu-pro, 93.1 mmlu-redux). Rather than crowning one, run the same hard task through both once and let the results decide.

Want both MiMo-V2.6-Flash and Qwen3 235B A22B (2507) 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 pricing

Frequently asked questions

Is MiMo-V2.6-Flash or Qwen3 235B A22B (2507) 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-V2.6-Flash leans toward same natively omnimodal design as pro (text, image, video, audio) at a fraction of the size and price while Qwen3 235B A22B (2507) leans toward deep world knowledge from 235b total parameters (83.0 mmlu-pro, 93.1 mmlu-redux), and that positioning usually predicts which feels better on your codebase.

Which is cheaper, MiMo-V2.6-Flash or Qwen3 235B A22B (2507)?

Qwen3 235B A22B (2507) is cheaper — $0.14/$0.28 per 1M tokens vs Open weight (self-host / free).

Which has the bigger context window?

MiMo-V2.6-Flash — 1M tokens vs 256K, about 4× larger. Useful only if the model actually reasons over the full window, which not all do.

Can I use both MiMo-V2.6-Flash and Qwen3 235B A22B (2507) together?

Yes — a multi-model platform like LumiChats gives you MiMo-V2.6-Flash, Qwen3 235B A22B (2507) 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-V2.6-Flash or Qwen3 235B A22B (2507)?

MiMo-V2.6-Flash — released September 21, 2026, about 14 months after Qwen3 235B A22B (2507).

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.