Pick MiniMax M3 for open-weight 428b moe (~23b active per token) with a 1m-token context or native multimodal input — text, image and video. 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.
MiniMax M3 (MiniMax) and Qwen3.8-Flash-Next (Alibaba) are two of the models people most often weigh against each other in 2026. MiniMax M3 is miniMax's open-weight 428B multimodal model with a 1M context — strong reported coding (59.0 SWE-Bench Pro) at low entry pricing. 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
Price: Qwen3.8-Flash-Next is about 1.4× cheaper on input ($0.16/$0.47 per 1M tokens vs $0.23/$0.96 per 1M tokens) — modest, but it adds up at steady volume.
Context window: MiniMax M3 holds 4× more — 1M (~1,573 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 3 months (released August 26, 2026), usually meaning fresher training data and capabilities.
Specifications
Spec
MiniMax M3
Qwen3.8-Flash-Next
Provider
MiniMax (China)
Alibaba (China)
Released
May 31, 2026
August 26, 2026
Context window
1M (~1,573 pages)
262K tokens natively (extensible to 1M with YaRN) (~393 pages)
Price (in/out)
$0.23/$0.96 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
Open-weight 428B MoE (~23B active per token) with a 1M-token context: MiniMax M3 — Its 1M window holds about 4× more than Qwen3.8-Flash-Next's 262K tokens natively (extensible to 1M with YaRN) in a single prompt.
Native multimodal input — text, image and video: MiniMax M3 — MiniMax's open-weight 428B multimodal model with a 1M context — strong reported coding (59.0 SWE-Bench Pro) at low entry pricing — and it carries the larger 1M context.
Reports 59.0 on SWE-Bench Pro (a strong open-weight score), surpassing GPT-5.5: MiniMax M3 — Qwen3.8-Flash-Next is comparatively weak here — an open-weight architecture preview rather than Alibaba's polished flagship product
SWE-bench Pro (62.5, ahead of Claude Opus 4.6 Max's 53.4): Qwen3.8-Flash-Next — MiniMax M3 is comparatively weak here — miniMax's own reported 80.5 SWE-Bench Verified figure is vendor-stated, not independently verified
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 MiniMax M3 ($0.23/$0.96 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: MiniMax M3 — Its 1M window is about 4× larger than Qwen3.8-Flash-Next's 262K tokens natively (extensible to 1M with YaRN), fitting roughly 1,573 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 MiniMax M3, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases: MiniMax M3 — Larger 1M window fits more in one prompt.
Anyone whose priority is open-weight 428b moe (~23b active per token) with a 1m-token context: MiniMax M3 — 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.
MiniMax M3: where it fits
MiniMax's open-weight 428B multimodal model with a 1M context — strong reported coding (59.0 SWE-Bench Pro) at low entry pricing. Released May 31, 2026 by MiniMax, it is built for open-weight 428B MoE (~23B active per token) with a 1M-token context, native multimodal input — text, image and video, reports 59.0 on SWE-Bench Pro (a strong open-weight score), surpassing GPT-5.5, and low entry pricing at $0.30/$1.20 per million up to 512K tokens.
Its trade-offs are real: price doubles to $0.60/$2.40 above 512K tokens — not flat across 1M, miniMax's own reported 80.5 SWE-Bench Verified figure is vendor-stated, not independently verified, sWE-Bench Pro is a different, harder benchmark than SWE-Bench Verified, and newer than M2.7 but with less independent testing so far. At $0.23 in / $0.96 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
MiniMax M3 and Qwen3.8-Flash-Next overlap enough that the right pick depends on your specific job. Qwen3.8-Flash-Next costs less per token; MiniMax M3 holds the larger context; and each leads in its own area — MiniMax M3 for open-weight 428b moe (~23b active per token) with a 1m-token context, 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.
Frequently asked questions
Is MiniMax M3 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, MiniMax M3 leans toward open-weight 428b moe (~23b active per token) with a 1m-token context 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, MiniMax M3 or Qwen3.8-Flash-Next?
Qwen3.8-Flash-Next is cheaper — $0.23/$0.96 per 1M tokens vs $0.16/$0.47 per 1M tokens, roughly 1.4× apart on input.
Which has the bigger context window?
MiniMax M3 — 1M vs 262K tokens natively (extensible to 1M with YaRN), about 4× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both MiniMax M3 and Qwen3.8-Flash-Next together?
Yes — a multi-model platform like LumiChats gives you MiniMax M3, 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, MiniMax M3 or Qwen3.8-Flash-Next?
Qwen3.8-Flash-Next — released August 26, 2026, about 3 months after MiniMax M3.
MiniMax M3 vs Qwen3.8-Flash-Next
MiniMax · China | Alibaba · China · Updated June 2026
Quick verdict
Pick MiniMax M3 for open-weight 428b moe (~23b active per token) with a 1m-token context or native multimodal input — text, image and video. 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.
MiniMax M3 (MiniMax) and Qwen3.8-Flash-Next (Alibaba) are two of the models people most often weigh against each other in 2026. MiniMax M3 is miniMax's open-weight 428B multimodal model with a 1M context — strong reported coding (59.0 SWE-Bench Pro) at low entry pricing. 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 1.4× cheaper on input ($0.16/$0.47 per 1M tokens vs $0.23/$0.96 per 1M tokens) — modest, but it adds up at steady volume.
▸Context window: MiniMax M3 holds 4× more — 1M (~1,573 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 3 months (released August 26, 2026), usually meaning fresher training data and capabilities.
Side-by-side specs
Spec
MiniMax M3
Qwen3.8-Flash-Next
Provider
MiniMax (China)
Alibaba (China)
Released
May 31, 2026
August 26, 2026
Context window
1M (~1,573 pages)
262K tokens natively (extensible to 1M with YaRN) (~393 pages)
Price (in/out)
$0.23/$0.96 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
Open-weight 428B MoE (~23B active per token) with a 1M-token context
MiniMax M3
Its 1M window holds about 4× more than Qwen3.8-Flash-Next's 262K tokens natively (extensible to 1M with YaRN) in a single prompt.
Native multimodal input — text, image and video
MiniMax M3
MiniMax's open-weight 428B multimodal model with a 1M context — strong reported coding (59.0 SWE-Bench Pro) at low entry pricing — and it carries the larger 1M context.
Reports 59.0 on SWE-Bench Pro (a strong open-weight score), surpassing GPT-5.5
MiniMax M3
Qwen3.8-Flash-Next is comparatively weak here — an open-weight architecture preview rather than Alibaba's polished flagship product
SWE-bench Pro (62.5, ahead of Claude Opus 4.6 Max's 53.4)
Qwen3.8-Flash-Next
MiniMax M3 is comparatively weak here — miniMax's own reported 80.5 SWE-Bench Verified figure is vendor-stated, not independently verified
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 MiniMax M3 ($0.23/$0.96 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
MiniMax M3
Its 1M window is about 4× larger than Qwen3.8-Flash-Next's 262K tokens natively (extensible to 1M with YaRN), fitting roughly 1,573 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 MiniMax M3, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases
→ MiniMax M3
Larger 1M window fits more in one prompt.
Anyone whose priority is open-weight 428b moe (~23b active per token) with a 1m-token context
→ MiniMax M3
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.
MiniMax M3: where it fits
MiniMax's open-weight 428B multimodal model with a 1M context — strong reported coding (59.0 SWE-Bench Pro) at low entry pricing. Released May 31, 2026 by MiniMax, it is built for open-weight 428B MoE (~23B active per token) with a 1M-token context, native multimodal input — text, image and video, reports 59.0 on SWE-Bench Pro (a strong open-weight score), surpassing GPT-5.5, and low entry pricing at $0.30/$1.20 per million up to 512K tokens.
Its trade-offs are real: price doubles to $0.60/$2.40 above 512K tokens — not flat across 1M, miniMax's own reported 80.5 SWE-Bench Verified figure is vendor-stated, not independently verified, sWE-Bench Pro is a different, harder benchmark than SWE-Bench Verified, and newer than M2.7 but with less independent testing so far. At $0.23 in / $0.96 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
MiniMax M3 and Qwen3.8-Flash-Next overlap enough that the right pick depends on your specific job. Qwen3.8-Flash-Next costs less per token; MiniMax M3 holds the larger context; and each leads in its own area — MiniMax M3 for open-weight 428b moe (~23b active per token) with a 1m-token context, 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 MiniMax M3 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.
Is MiniMax M3 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, MiniMax M3 leans toward open-weight 428b moe (~23b active per token) with a 1m-token context 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, MiniMax M3 or Qwen3.8-Flash-Next?
Qwen3.8-Flash-Next is cheaper — $0.23/$0.96 per 1M tokens vs $0.16/$0.47 per 1M tokens, roughly 1.4× apart on input.
Which has the bigger context window?
MiniMax M3 — 1M vs 262K tokens natively (extensible to 1M with YaRN), about 4× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both MiniMax M3 and Qwen3.8-Flash-Next together?
Yes — a multi-model platform like LumiChats gives you MiniMax M3, 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, MiniMax M3 or Qwen3.8-Flash-Next?
Qwen3.8-Flash-Next — released August 26, 2026, about 3 months after MiniMax M3.
Specifications and benchmarks reflect publicly reported figures as of June 2026 and may change as providers release updates. Always verify on your own workload.