Models › MiniMax-M3.1 family

MiniMax-M3.1-Flash

MiniMaxAI MiniMax Model License (minimax-community) out 8 days ago

220B parameters, images and text in, text out, 977K tokens of context. 2 providers serve it from $0.15 per million input tokens.

Verified 2026-10-25 Rank #432 in LLMs · score 78.5 method v1 Sources 4 Downloads 30d 5.8k · #534 of 619 tracked

Changed this week: MiniMax M3.1 Flash entered the release radar as served, open-weights (Hugging Face, 2026-10-23).2026-10-23

Can I run it?132GB at INT4Estimatefits 2×80 GBmethod
What does it cost?$0.15/1M incheapest of 1Parasail2026-10-24
Can I use it?MiniMax Model License (minimax-community)Attribution + commercial threshold; see LICENSE in repolicence file
Do people use it?5.8kdownloads, 30 days#534 of 619 we track · below the median282 likes since release282 likesHugging Face

Worth reading

Picked by rule from 6 tracked pieces: the lab's own post, the most shared, a field report, the latest.

The lab's own post

MiniMax M3.1 Flash

MiniMax Research officially rolls out MiniMax M3.1 Flash, a next-generation model designed for agentic tasks, coding, and cybersecurity.

minimax.io 2026-10-19 517 HN pts · 220 comments 534 words
Latest · 2 days ago

M3.1 Flash weights are now open

MiniMax M3.1 Flash is a native multimodal Mixture-of-Experts model released October 18, 2026, designed for coding, agentic workflows, and computer use.

  • The model replaces M3's earlier multi-token prediction approach with a DSpark speculative decoding draft head.
  • Three full-attention layers from the previous M3 model are replaced by sparse attention in this release.
together.ai 2026-10-23 2 HN pts · 1 comments 755 words

6 best of 6 tracked

The 4 picks above, plus these 2. Top is the radar's score: discussion, source authority and the lab's own posts, fading with age.

  1. DiscussionMiniMax M3.1 Flash: Space Performance and Bunny Speedtwitter.com · 2026-10-19 · 7 HN pts
  2. DiscussionMiniMax M3.1-Flash-Previewtwitter.com · 2026-09-28 · 1 HN pts

Where to run it

Live hosted endpoints, cheapest first, USD per million tokens. Ties break on output price, then provider name.

#Cheapest live rowIn /1MOut /1MContextQuantSeen
1 Parasail $0.15 $0.60 — — Oct 24

All 2 providers →

Where it's used

Hugging Face does not say who downloads a model or with what. These are the proxies: API providers listing it, quantized builds, fine-tunes/adapters/merges, and Spaces that load it.

Served by 2 API providers; 25 quantized builds for local use.

  • As a service2Novita, Parasail
  • On own hardware25quantized builds
  • Built on55 fine-tunes · 0 adapters · 0 merges
  • In apps0Hugging Face Spaces

Will it fit?

Weights only, an estimate: leave room for the KV cache. Method

Fits two 80 GB GPUs at INT4; too big for one

About 132 GB at INT4, 265 GB at FP8, 529 GB at BF16: weights only, so leave room for the KV cache. Every expert must be resident, so total parameters count.

16 GB card64 GB Mac2×80 GB8×80 GBINT4: about 132 GB of weights (estimate; KV cache and activations extra)INT4132 GBFP8: about 265 GB of weights (estimate; KV cache and activations extra)FP8265 GBBF16: about 529 GB of weights (estimate; KV cache and activations extra)BF16529 GB
16 GB card64 GB Mac2×80 GB8×80 GBINT4: about 132 GB of weights (estimate; KV cache and activations extra)INT4132 GBFP8: about 265 GB of weights (estimate; KV cache and activations extra)FP8265 GBBF16: about 529 GB of weights (estimate; KV cache and activations extra)BF16529 GB

Estimate params × bytes per param × 1.2 · how we estimate

The story so far

Weights, every dated article, and repo commits on one rail.

What people built on it

30 derivatives on the Hub. Which build to run, by runtime: the most downloaded in each class.

30 derivatives on the Hub, most of them quantized

From the Hugging Face model tree; quantized builds in teal, fine-tunes, merges and adapters in greys.

25 quantized (Hugging Face model tree)5 fine-tunes (Hugging Face model tree)25 quantized5 fine-tunes
25 quantized (Hugging Face model tree)5 fine-tunes (Hugging Face model tree)25 quantized5 fine-tunes

All variants →

Reference

Everything else, folded: the full tables, every source.

Use-case evidence

4 use cases

What it is good at 4 use cases

Labels are evidence, not verdicts: measured a third-party leaderboard row for this model; author-reported a number the model author published in the card or eval files; structural a deterministic signal from the name, config or chat template, or a decision-model estimate. Each chip links to its source. All use cases.

Facts

17 fields

Listed in LLMs · MoE

Facts

Pipeline tagimage-text-to-text
Librarytransformers
ArchitecturesMiniMaxM3SparseForConditionalGeneration
Model typeminimax_m3_vl
Params by dtypeBF16: 3,323,222,528 · F32: 46,339,200 · F8_E4M3: 10,560,208,896 · U8: 206,561,083,392
Size bandXXL
MoEyes
Context length1,000,000 (provider listing)
PapersarXiv:2606.13392
LicenseMiniMax Model License (minimax-community) (other)
Hub snapshot2026-10-25

Hardware fit

3 estimates

Hardware fit ESTIMATE

Weights-only memory for MiniMax-M3.1-Flash: params × bytes/param × 1.2 overhead. KV cache and activations are extra. How this is computed.

BF16 (2 bytes/param)529 GB · Multi-GPU node (<640 GB)
FP8 (1 byte/param)265 GB · Multi-GPU node (<640 GB)
INT4 (0.5 bytes/param)132 GB · Multi-GPU node (<640 GB)

Weights only (params × bytes/param × 1.2 overhead); KV cache and activations extra. MoE: all experts must be resident, so total parameters count.

All variants

30 variants

Lineage

Original release (shipped quantized by the lab) Family: MiniMaxAI/MiniMax-M3.1 30 derivatives on the Hub

quantized: 25finetune: 5

Top variants (30 shown, ranked by downloads)

Relation
Quant
Size
30 rows
Variant Relation Quant Params Band License Author Likes Downloads
Baekpica/MiniMax-M3.1-Flash-VQ-GGUF quantized GGUF — — other Baekpica 0 3.2k
webmp3/Sakura-MiniMax-M3.1-Flash-Q3_K_M-GGUF quantized GGUF — — other webmp3 0 2k
LyJonathan/MiniMax-M3.1-Flash-GGUF quantized GGUF — — other LyJonathan 5 1.5k
unsloth/MiniMax-M3.1-Flash-GGUF quantized GGUF — — other unsloth 19 589
AtomicChat/MiniMax-M3.1-Flash-GGUF quantized GGUF — — other AtomicChat 2 807
incoai/MiniMax-M3.1-Flash-DFlash2 finetune — 2.12B M apache-2.0 incoai 12 107
unsloth/MiniMax-M3.1-Flash quantized — 220B XXL other unsloth 7 271
mlx-community/MiniMax-M3.1-Flash-Text-4bit quantized -4bit 426B XXL other mlx-community 4 289
mlx-community/MiniMax-M3.1-Flash-oQ4e-mtp quantized — 427B XXL other mlx-community 3 275
amd/MiniMax-M3.1-Flash-Quark-MXFP4 quantized MXFP4 220B XXL other amd 1 294
mlx-community/MiniMax-M3.1-Flash-oQ3e-mtp quantized — 427B XXL other mlx-community 2 243
mlx-community/MiniMax-M3.1-Flash-oQ3.5e-mtp quantized — 427B XXL other mlx-community 2 175
Mia-AiLab/MiniMax-M3.1-Flash-EXL3-EAQ-v1-3.06-bpw quantized EXL3 84.3B XL — Mia-AiLab 3 88
webmp3/Sakura-MiniMax-M3.1-Flash-Q2_K-GGUF quantized Q2_K — — other webmp3 2 94
mlx-community/MiniMax-M3.1-Flash-Text-3bit quantized — 426B XXL other mlx-community 1 135
vcruz305/MiniMax-M3.1-Flash-EXL3-SAGE-2.15bpw quantized EXL3 58.4B XL other vcruz305 2 34
Mia-AiLab/MiniMax-M3.1-Flash-EXL3-EAQ-v1-3.5-bpw quantized EXL3 97.1B XL — Mia-AiLab 1 56
RadixArk/Minimax-M3.1-Flash-Dflash2 finetune — 2.05B M — RadixArk 1 52
webmp3/Sakura-MiniMax-M3.1-Flash-Q4_K_M-GGUF quantized GGUF — — other webmp3 1 39
primitive-ai/MiniMax-M3.1-Flash-pruned-272B-NVFP4 quantized NVFP4 143B XXL other primitive-ai 1 34
infosave/MiniMax-M3.1-Flash-cmf quantized — — — apache-2.0 infosave 1 31
BobClawblaw/MiniMax-M3.1-Flash-MLX-mix34 finetune MLX 431B XXL other BobClawblaw 1 29
vcruz305/MiniMax-M3.1-Flash-EXL3-SAGE-4.26bpw quantized EXL3 118B XXL other vcruz305 1 18
inferencerlabs/MiniMax-M3.1-Flash-MLX-Q9 quantized MLX — — — inferencerlabs 0 59
webmp3/Sakura-MiniMax-M3.1-Flash-Q5_K_M-GGUF quantized GGUF — — other webmp3 0 35
BobClawblaw/MiniMax-M3.1-Flash-MLX-mix256k finetune MLX 431B XXL other BobClawblaw 0 14
gatilin/minimax_m3_1_flash_vision finetune — — — other gatilin 0 12
mondk/MiniMax-M3.1-Flash-GGUF quantized GGUF — — other mondk 0 0
tiny-random/minimax-m3.1-flash quantized — 14.0M S — tiny-random 0 0
yujiepan/minimax-m3.1-flash-tiny-random quantized — 14.0M S — yujiepan 0 0

Repository changelog

1 change
changed in the last 7 daysNew on the watch list: 2026-10-24all changes

All providers

2 providers · 2 endpoints

Where to run 2 providers · 2 endpoints

Hosted endpoints observed for MiniMax-M3.1-Flash, sorted by input price (USD per million tokens). Each row names its observer and date; prices change daily, so check the source before quoting. Cheapest observed input price: $0.15/M.

ProviderModel refContextQuant$/M in$/M outtok/sTTFTToolsStatusObserved
Novita
hf
minimax/minimax-m3.1-flash 977K — $0.15 $0.60 66.4 1962 ms yes staging 2026-10-25
Parasail
parasail
parasail-minimax-m31-flash — — $0.15 $0.60 — — — serverless 2026-10-24

Articles and coverage

6 pieces

Ranked by source signals (HN, Lobsters, authority domains, official posts) and recency. Summaries are generated from a stored copy of the article text; every sentence cites a span of that text, and nothing is shown that failed the citation check.

Official1

minimax.io2026-10-19officialauthorityHN front pageHN 517 pts · 220 comments534 words
MiniMax Research officially rolls out MiniMax M3.1 Flash, a next-generation model designed for agentic tasks, coding, and cybersecurity. The model processed more than 200 trillion tokens during early testing and scores 76.2 on BankerToolBench for complex real-world challenges. Pricing is flat at $0.15 input, $0.60 output, and $0.015 cached input per 1M tokens, with output speeds reaching 200+ TPS.written, reviewed, cited

Analysis2

artificialanalysis.ai2026-10-19authorityHN 8 pts · 2 comments876 words
MiniMax-M3.1-Flash is a proprietary multimodal model released on October 19, 2026, that scores 43 on the Artificial Analysis Intelligence Index. It generated 180M tokens during evaluation, outputs at 96 tokens per second with a 1.32s time to first token, and is available through 13 API providers. The model costs $0.15 per 1M input tokens and $0.60 per 1M output tokens while supporting a 1M token context window.written, reviewed, cited
  • The model uses extended thinking or chain-of-thought reasoning to work through complex problems before providing an answer.
  • For a blended rate (7:2:1 cache hit/input/output ratio), this is $0.10 per 1M tokens.
  • MiniMax has not disclosed the model size or parameter count.
together.ai2026-10-23authorityHN 2 pts · 1 comments755 words
MiniMax M3.1 Flash is a native multimodal Mixture-of-Experts model released October 18, 2026, designed for coding, agentic workflows, and computer use. It features roughly 428B total parameters with 23B active per token, supports a context window of up to 1M tokens, and costs $0.15 for input and $0.60 for output per 1M tokens. The model improves inference efficiency through MiniMax Sparse Attention and quantization-aware training while releasing under the MiniMax Community License.written, reviewed, cited
  • The model replaces M3's earlier multi-token prediction approach with a DSpark speculative decoding draft head.
  • Three full-attention layers from the previous M3 model are replaced by sparse attention in this release.
  • The model achieves the highest AutomationBench v1.0.6 score of 62.8 among the open models in MiniMax's comparison.

Community3

twitter.com2026-10-19HN 7 pts · 2 comments
news.ycombinator.com2026-10-19HN 8 pts · 1 comments105 words
twitter.com2026-09-28HN 1 pts · 0 comments

Papers

1 paper

Papers 1

arXiv links found in the model card and its tags. "Peer-reviewed" is shown only with evidence: a published venue in OpenAlex (measured) or the authors' own journal reference on arXiv (reported).

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Sources

4 sources

Sources

Hub snapshot: 2026-10-25 · Providers: 2026-10-24 21:44 UTC · Articles: 2026-10-24 21:46 UTC · Summary: 2026-10-24 21:52 UTC · Last verified 2026-10-25

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