MiMo-V2.6-Pro vs Qwen 3.7 Max

Xiaomi · China  |  Alibaba · China · Updated June 2026

Quick verdict

Pick MiMo-V2.6-Pro for natively omnimodal — text, image, video and audio in one model family, not bolted-on modality adapters or 1.02 trillion total parameters, 42b active per token (sparse moe), mit-licensed and self-hostable. Pick Qwen 3.7 Max for long-horizon agentic coding (swe-bench pro 60.6, terminal-bench 2.0 69.7) or 1m-token long-document and full-codebase analysis. Choose MiMo-V2.6-Pro if you need self-hosting or data privacy; Qwen 3.7 Max if you want a managed API.

MiMo-V2.6-Pro (Xiaomi) and Qwen 3.7 Max (Alibaba) are two of the models people most often weigh against each other in 2026. MiMo-V2.6-Pro is xiaomi's flagship omnimodal model — 1.02T parameters, native text/image/video/audio, MIT-licensed, released September 22, 2026. Qwen 3.7 Max is alibaba's agent-first frontier model — a 1M-token context and long-horizon coding at about half the cost of US flagships. They diverge most on price, context window and open vs. closed weights — each quantified below from the models' real specs.

Key differences at a glance

Side-by-side specs

SpecMiMo-V2.6-ProQwen 3.7 Max
ProviderXiaomi (China) Alibaba (China)
ReleasedSeptember 22, 2026 May 20, 2026
Context window1M tokens (~1,573 pages) 1M (~1,500 pages)
Price (in/out)$0.435/$0.87 per 1M tokens $2.5/$7.5 per 1M tokens
Open weight?Yes — self-hostable No — API only
Modalitiestext, image, video, audio text, code
SWE-Bench VerifiedNot published Not published
MRCR v2 @ 1MNot published Not published

Who wins what

Natively omnimodal — text, image, video and audio in one model family, not bolted-on modality adapters

MiMo-V2.6-Pro

Qwen 3.7 Max is comparatively weak here — text-only — no vision input (the Plus variant adds images)

1.02 trillion total parameters, 42B active per token (sparse MoE), MIT-licensed and self-hostable

MiMo-V2.6-Pro

Open weights make this possible at all — Qwen 3.7 Max is API-only, so it cannot leave the vendor's servers.

Reported Artificial Analysis Intelligence Index score of 46

MiMo-V2.6-Pro

Xiaomi's flagship omnimodal model — 1.02T parameters, native text/image/video/audio, MIT-licensed, released September 22, 2026 — and it runs cheaper at $0.435/$0.87 per 1M tokens.

Long-horizon agentic coding (SWE-Bench Pro 60.6, Terminal-Bench 2.0 69.7)

Qwen 3.7 Max

MiMo-V2.6-Pro is comparatively weak here — first-generation omnimodal release from Xiaomi's MiMo line — benchmark claims are largely Xiaomi's own reporting, not yet widely independently verified

1M-token long-document and full-codebase analysis

Qwen 3.7 Max

MiMo-V2.6-Pro is comparatively weak here — an Artificial Analysis Index score of 46 trails several established frontier models

MCP tool orchestration and multi-hour autonomous runs

Qwen 3.7 Max

Qwen 3.7 Max lists mCP tool orchestration and multi-hour autonomous runs among its strengths; MiMo-V2.6-Pro does not.

Lowest cost at scale

MiMo-V2.6-Pro

At $0.435/$0.87 per 1M tokens, it is the cheaper of the two — the gap dominates the bill on high-volume workloads.

Which should you pick?

A cost-sensitive startup shipping high volume

MiMo-V2.6-Pro

At $0.435/$0.87 per 1M tokens it undercuts Qwen 3.7 Max, and on millions of tokens that margin decides the monthly bill.

Someone analysing very long documents or codebases

MiMo-V2.6-Pro

Larger 1M tokens window fits more in one prompt.

A team with data-privacy or self-hosting needs

MiMo-V2.6-Pro

Open weights let you run it on your own hardware; Qwen 3.7 Max is API-only.

Anyone whose priority is natively omnimodal — text, image, video and audio in one model family, not bolted-on modality adapters

MiMo-V2.6-Pro

It is specifically built for that.

Anyone whose priority is long-horizon agentic coding (swe-bench pro 60.6, terminal-bench 2.0 69.7)

Qwen 3.7 Max

That is its strongest area.

MiMo-V2.6-Pro: where it fits

Xiaomi's flagship omnimodal model — 1.02T parameters, native text/image/video/audio, MIT-licensed, released September 22, 2026. Released September 22, 2026 by Xiaomi, it is built for natively omnimodal — text, image, video and audio in one model family, not bolted-on modality adapters, 1.02 trillion total parameters, 42B active per token (sparse MoE), MIT-licensed and self-hostable, reported Artificial Analysis Intelligence Index score of 46, and a separate 'UltraSpeed' variant reportedly delivers up to 20x faster output than Pro at similar quality for high-throughput use cases.

Its trade-offs are real: first-generation omnimodal release from Xiaomi's MiMo line — benchmark claims are largely Xiaomi's own reporting, not yet widely independently verified, an Artificial Analysis Index score of 46 trails several established frontier models, and no official API pricing from Xiaomi directly — the listed price reflects third-party inference providers (e.g. OpenRouter), which can change independently of Xiaomi's own terms. At $0.435 in / $0.87 out per million tokens, it sits in the budget price band.

Qwen 3.7 Max: where it fits

Alibaba's agent-first frontier model — a 1M-token context and long-horizon coding at about half the cost of US flagships. Released May 20, 2026 by Alibaba, it is built for long-horizon agentic coding (SWE-Bench Pro 60.6, Terminal-Bench 2.0 69.7), 1M-token long-document and full-codebase analysis, mCP tool orchestration and multi-hour autonomous runs, and frontier intelligence at roughly half the price of US flagships.

Its trade-offs: text-only — no vision input (the Plus variant adds images), closed-weight, API-only — no self-hosting, trails GPT-5.5 and Claude Opus on the hardest one-shot reasoning, and chinese-jurisdiction data-residency considerations. At $2.5 in / $7.5 out per million tokens, it sits in the mid price band.

The bottom line for this matchup

The defining split here is open vs. closed. MiMo-V2.6-Pro gives you weights you control — self-host it, fine-tune it, keep data in-house, pay only for hardware. Qwen 3.7 Max gives you a managed, always-updated API with no infrastructure to run. Teams with GPUs, privacy requirements, or huge volume often favour the open model; teams that want zero ops and the latest capabilities favour the closed one. Capability is close enough that this operational question, not the benchmark, usually decides it.

Want both MiMo-V2.6-Pro and Qwen 3.7 Max 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-Pro or Qwen 3.7 Max 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-Pro leans toward natively omnimodal — text, image, video and audio in one model family, not bolted-on modality adapters while Qwen 3.7 Max leans toward long-horizon agentic coding (swe-bench pro 60.6, terminal-bench 2.0 69.7), and that positioning usually predicts which feels better on your codebase.

Which is cheaper, MiMo-V2.6-Pro or Qwen 3.7 Max?

MiMo-V2.6-Pro is open-weight, so self-hosting means no per-token fee (you pay for hardware instead), while Qwen 3.7 Max is API-metered at $2.5/$7.5 per 1M tokens. For most teams without GPUs, the API model is cheaper to start; at very high volume, self-hosting can win.

Which has the bigger context window?

Effectively neither — 1M tokens vs 1M is a difference of a few percent. Remember advertised ≠ usable: recall typically degrades before the ceiling.

Can I use both MiMo-V2.6-Pro and Qwen 3.7 Max together?

Yes — a multi-model platform like LumiChats gives you MiMo-V2.6-Pro, Qwen 3.7 Max 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-Pro or Qwen 3.7 Max?

MiMo-V2.6-Pro — released September 22, 2026, about 4 months after Qwen 3.7 Max.

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.