MiniMax M3.1 Flash vs Qwen 3.7 Max

MiniMax · China  |  Alibaba · China · Updated June 2026

Quick verdict

Pick MiniMax M3.1 Flash for $0.15/$0.60 per million tokens (cached $0.015) with flat pricing regardless of prompt length or 428b-parameter moe (about 23b active) with weights on hugging face from october 23, 2026. 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 MiniMax M3.1 Flash if you need self-hosting or data privacy; Qwen 3.7 Max if you want a managed API.

MiniMax M3.1 Flash (MiniMax) and Qwen 3.7 Max (Alibaba) are two of the models people most often weigh against each other in 2026. MiniMax M3.1 Flash is miniMax's October 19, 2026 always-thinking agentic Flash model - 1M context, text/image/video input, $0.15/$0.60 and open weights under a community license. 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 and open vs. closed weights — each quantified below from the models' real specs.

Key differences at a glance

Side-by-side specs

SpecMiniMax M3.1 FlashQwen 3.7 Max
ProviderMiniMax (China) Alibaba (China)
ReleasedOctober 19, 2026 May 20, 2026
Context window1M tokens (~1,500 pages) 1M (~1,500 pages)
Price (in/out)$0.15/$0.6 per 1M tokens $2.5/$7.5 per 1M tokens
Open weight?Yes — self-hostable No — API only
Modalitiestext, image, video, code text, code
SWE-Bench VerifiedNot published Not published
MRCR v2 @ 1MNot published Not published

Who wins what

$0.15/$0.60 per million tokens (cached $0.015) with flat pricing regardless of prompt length

MiniMax M3.1 Flash

MiniMax's October 19, 2026 always-thinking agentic Flash model - 1M context, text/image/video input, $0.15/$0.60 and open weights under a community license — and it runs cheaper at $0.15/$0.6 per 1M tokens.

428B-parameter MoE (about 23B active) with weights on Hugging Face from October 23, 2026

MiniMax M3.1 Flash

MiniMax's October 19, 2026 always-thinking agentic Flash model - 1M context, text/image/video input, $0.15/$0.60 and open weights under a community license — and its weights are open while Qwen 3.7 Max is API-only.

DeepSWE v1.1 of 73.7% and OSWorld V2 of 61.7 (MiniMax-reported)

MiniMax M3.1 Flash

MiniMax's October 19, 2026 always-thinking agentic Flash model - 1M context, text/image/video input, $0.15/$0.60 and open weights under a community license — and it is the newer of the two.

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

Qwen 3.7 Max

MiniMax M3.1 Flash is comparatively weak here — benchmarks are mostly self-reported; no SWE-bench Verified figure published

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

Qwen 3.7 Max

MiniMax M3.1 Flash is comparatively weak here — thinking is always on and default effort is max, which inflates output-token cost

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; MiniMax M3.1 Flash does not.

Lowest cost at scale

MiniMax M3.1 Flash

At $0.15/$0.6 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

→ MiniMax M3.1 Flash

At $0.15/$0.6 per 1M tokens it undercuts Qwen 3.7 Max, and on millions of tokens that margin decides the monthly bill.

A team with data-privacy or self-hosting needs

→ MiniMax M3.1 Flash

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

Anyone whose priority is $0.15/$0.60 per million tokens (cached $0.015) with flat pricing regardless of prompt length

→ MiniMax M3.1 Flash

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.

MiniMax M3.1 Flash: where it fits

MiniMax's October 19, 2026 always-thinking agentic Flash model - 1M context, text/image/video input, $0.15/$0.60 and open weights under a community license. Released October 19, 2026 by MiniMax, it is built for $0.15/$0.60 per million tokens (cached $0.015) with flat pricing regardless of prompt length, 428B-parameter MoE (about 23B active) with weights on Hugging Face from October 23, 2026, deepSWE v1.1 of 73.7% and OSWorld V2 of 61.7 (MiniMax-reported), and artificial Analysis Intelligence Index of 42, 13 points above MiniMax M3.

Its trade-offs are real: thinking is always on and default effort is max, which inflates output-token cost, behind DeepSeek V4.1 Flash 1017 on Artificial Analysis's independent index, custom MiniMax Community license: attribution required, and products above $20M annual revenue need written authorization, and benchmarks are mostly self-reported; no SWE-bench Verified figure published. At $0.15 in / $0.6 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. MiniMax M3.1 Flash 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 MiniMax M3.1 Flash 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 MiniMax M3.1 Flash 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, MiniMax M3.1 Flash leans toward $0.15/$0.60 per million tokens (cached $0.015) with flat pricing regardless of prompt length 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, MiniMax M3.1 Flash or Qwen 3.7 Max?

MiniMax M3.1 Flash 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?

Both advertise 1M tokens (~1,500 pages). Remember advertised ≠ usable: recall typically degrades before the ceiling.

Can I use both MiniMax M3.1 Flash and Qwen 3.7 Max together?

Yes — a multi-model platform like LumiChats gives you MiniMax M3.1 Flash, 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, MiniMax M3.1 Flash or Qwen 3.7 Max?

MiniMax M3.1 Flash — released October 19, 2026, about 5 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.