MiniMax M3
A Tier · 8.4/10
**MiniMax-H3's weights are public on Hugging Face (repo 2026-07-28, updated 2026-08-13) under a community licence whose open-weight grant is limited to the US, EU, UK and South Korea**, and **MiniMax Music 3 (2026-08-07)** ships open weights for five-minute full-song generation with a prominent-attribution commercial clause. MiniMax's coding/agent flagship -- M3 (June 1 2026): 1M-token context, MSA sparse attention (>15x decoding speedup at long context), SWE-Bench Pro 59.0%, Terminal-Bench 66.0%. OPEN WEIGHTS LIVE on HuggingFace since June 12 (~428B total / ~23B active, native multimodal, minimax-community license)
Score Breakdown
Benchmark Scores
Benchmarks for MiniMax-M2.7 (229B total, ~10B active MoE) -- self-evolving agent positioning per vendor
| Benchmark | Description | Score | |
|---|---|---|---|
| SWE-Bench Pro | 56.22% | ||
| Terminal Bench 2 | 57% | ||
| SWE Multilingual | 76.5% | ||
| Multi SWE Bench | 52.7% | ||
| VIBE-Pro | 55.6% |
Last updated: 2026-04-27
Personality & Tone
The Chinese multimodal generalist
Tone: Expressive and media-rich. MiniMax's chat models lean into long, formatted responses and handle voice and image prompts more naturally than most pure-text peers.
Quirks: Strong multimodal story; text-only quality is good but not class-leading versus DeepSeek or Qwen. Like other Chinese models, careful on domestic political topics.
The Good and the Bad
What we like
- +229B/10B-active MoE delivers Tier-1 agentic performance with the smallest active footprint in its class -- M2.7 hits 56.22% on SWE-Bench Pro and 57.0% on Terminal Bench 2
- +Sparse MoE design: ~10B active params during inference means fast and cheap to run despite 229B total
- +M2 / M2.5 (predecessors) carry true MIT licensing with zero commercial restrictions; M2.7 license needs verification (HF lists 'Other')
- +Native agentic and tool-use training -- positioned as a 'self-evolving agent model'
- +Per-layer QK-Norm + full-attention blocks make long-context stable
- +Strong cost-to-performance ratio for agentic workloads vs closed frontier models
What could be better
- −Smaller Western community than Qwen/DeepSeek -- tutorials sparse
- −Ollama support arrived late -- community relied on vLLM for months
- −English writing tone is noticeably less polished than Claude or Mistral
- −PRC content filters apply
- −MiniMax as a lab is less well-known than Alibaba or DeepSeek -- some enterprise buyers hesitate
Pricing
Self-hosted (Free)
- ✓MIT license on M2 / M2.5
- ✓M2.7 license listed as 'Other' on HuggingFace -- verify before commercial use
- ✓Weights on Hugging Face (MiniMaxAI/MiniMax-M2.7)
API (M2 / M2.5 reference, MiniMax / OpenRouter)
- ✓M2: $0.30 in / $1.20 out
- ✓192K+ context
- ✓Native agentic + tool-use
API (M2.7)
- ✓M2.7 per-token pricing not posted on platform.minimax.io/subscribe/token-plan as of 2026-04-27 -- verify before quoting in production planning
System Requirements
Hardware needed to self-host. Min = smallest viable setup (usually heavy quantization). Max = full-precision / production-grade.
| Model variant | Min | Max |
|---|---|---|
| MiniMax M2 / M2.5 (230B total, ~10B active MoE)Sparse MoE activates only ~10B params during inference -- fast tok/s on moderate hardware | 96 GB unified RAM Q3 (Mac M3 Ultra) | 4× A100 80 GB FP8 |
| MiniMax M1 (hybrid-attention reasoning predecessor) | 96 GB unified RAM Q3 | 4× A100 80 GB FP8 |
Known Issues
- MINIMAX-H3 WEIGHTS ARE PUBLIC -- BUT THE OPEN-WEIGHT GRANT IS LIMITED TO THE US, EU, UK AND SOUTH KOREA, WHICH THE 7/31 ENTRY BELOW COULD NOT HAVE KNOWN (weights on Hugging Face; repo created 2026-07-28, last modified 2026-08-13; verified 2026-09-17): the MiniMaxAI/MiniMax-H3 repository carries **104 safetensors shards** and about 4.6M downloads, so the 'weights not released as of 8/03' caveat on this page is retired. **Licence: 'MiniMax H3 Community License Agreement' (HF front matter `license: other`), with an application form 'only for USA/EU/UK/South Korea'.** MiniMax's own Q&A explains why: video models face 'a more complex and rapidly evolving regulatory environment', the EU AI Act is in enforcement, and 'in the US... MiniMax is also involved in ongoing copyright-related legal proceedings specifically concerning generative video AI'; the company chose to 'release the model now with a transparent license scope' and calls the limitation 'not yet, not not ever'. **The hosted API is global** because MiniMax can enforce safeguards on its own infrastructure. Model card specifics: general-purpose omni-modal system, unified text/image/video/audio context, video with native stereo audio up to 2K and 15 seconds, task tags covering T2V, I2V, V2V, audio-to-audio-video and reference-to-audio-video, diffusers integration, plus official prompt-writing skills on GitHub. **Practical read:** H3 is now the only roster video model with published weights at this capability level, but 'open-weight' here carries a geography clause -- a licence class of its own, distinct from GLM-5.3's custom licence or Qwen's Apache 2.0, and one to state explicitly rather than summarise as 'open'.Source: Hugging Face (huggingface.co/MiniMaxAI/MiniMax-H3 -- API metadata createdAt 2026-07-28, lastModified 2026-08-13, 104 .safetensors; raw README.md front matter license_name minimax-h3-community-license-agreement; docs/QA-about-License.md) -- all fetched 2026-09-17 · 2026-09-17
- MINIMAX MUSIC 3 -- OPEN WEIGHTS FOR FIVE-MINUTE FULL-SONG GENERATION UNDER A COMMUNITY LICENCE WITH AN ATTRIBUTION CLAUSE (2026-08-07 on Hugging Face; unrecorded until 2026-09-17): the MiniMaxAI/MiniMax-Music3 repo (created 2026-08-07, updated 2026-08-14, 56 safetensors shards) publishes **MiniMax Music 3**, 'a high-performance music generation model for creating complete songs up to five minutes long' from lyrics plus a music description. **Architecture per the model card:** an **8B Global LLM initialised from Qwen3-8B** for long-range structure, a **0.6B Local LLM** for frame-level acoustic detail, and continuous hidden-state synthesis via Flow Matching and a Flow-VAE; output is **32 kHz, 16-bit stereo WAV** at 25 frames per second. Lyrics accept explicit section tags ([Intro], [Verse], [Pre-Chorus], [Chorus], [Bridge], [Instrumental], [Solo], [Outro]); the description controls style, emotional progression, vocals, instrumentation and production. **Licence: 'MiniMax-Music3 COMMUNITY LICENSE' -- an MIT-style grant with an Acceptable Use Policy exhibit and Commercial Terms that require you to 'prominently display MiniMax-Music3 on the user interface' of any commercial product using it, plus a further clause requiring a separate prior written agreement whose scope was cut off in our fetch -- read the full LICENSE before commercial use.** No vendor benchmark table is in the card; no listening-test comparison against Suno v6 or Lyria 3.5 is published. **Why it matters:** with Suno v6 closed and label-partnered, Music 3 is the most capable open-weight song model on the roster's radar, and it is shipped by the same lab whose H3 video weights are geography-gated -- Music 3's licence carries no such territory clause in the portion read.Source: Hugging Face (huggingface.co/MiniMaxAI/MiniMax-Music3 -- API metadata createdAt 2026-08-07, lastModified 2026-08-14, 56 .safetensors; raw README.md; raw LICENSE, first ~1,200 characters) -- all fetched 2026-09-17 · 2026-08-07
- NEW MODEL -- MINIMAX H3 / HAILUO 3.0 (2026-07-31, vendor blog): MiniMax launched **H3**, a general-purpose **omni-modal** model that understands and generates across text, images, video and audio. Vendor-stated capability, verbatim: '**H3 understands unified context across text, images, video, and audio, generating video with native stereo sound, up to 15 seconds at 2K resolution**' -- plus instruction following, text rendering, V2V motion transfer, and multimodal editing. The headline is that audio is **jointly generated with the video** rather than added afterwards. On cost, MiniMax claims H3 runs at less than one-third the cost of mainstream models at 2K, though the post publishes no rate card. **IMPORTANT -- DO NOT LABEL THIS OPEN-WEIGHT YET.** MiniMax only *promises* weights: '**we plan to open up the model weights in the coming days**, subject to applicable laws and regulations.' As of 2026-08-03 the weights are not released, so H3 is currently a closed model from an otherwise open-weight-leaning vendor -- re-check before describing it as open. [SUPERSEDED 2026-09-17: the weights are live on Hugging Face under a community licence limited to the US, EU, UK and South Korea -- see the newer entry above]Source: MiniMax (minimax.io/blog/minimax-h3, fetched 2026-08-03) · 2026-07-31
- INDEPENDENT BENCHMARK PUBLISHED (verified 2026-07-04): Artificial Analysis now has a citable M3 page (artificialanalysis.ai/models/minimax-m3, 'Released June 2026') showing an **Intelligence Index of 44**. This is lower than early social-media chatter had suggested (~55 circulated on X/LinkedIn but does not match the authoritative page) -- cite 44 as the independently-measured number. It keeps M3 solidly mid-pack among open-weights (below GLM-5.2's 51) despite the strong vendor SWE-Bench Pro 59% claim; treat vendor coding numbers and the AA general-intelligence number as measuring different things.Source: Artificial Analysis (artificialanalysis.ai/models/minimax-m3) · 2026-07-04
- WEIGHTS SHIPPED (2026-06-12): **MiniMax-M3 open weights landed on HuggingFace** (MiniMaxAI/MiniMax-M3 + an MXFP8 quant) -- hitting the 'within 10 days of June 1' promise on day 10-11. Model-card specs: **~428B total / ~23B active params**, native multimodal (text/image/video input), MiniMax Sparse Attention, 1M-token context, 9x prefill / 15x decode speedup vs M2 at 1M ctx; tech report arXiv:2606.13392. LICENSE CAVEAT: **minimax-community license**, not MIT like the M2 line -- review terms before commercial self-hosting. Self-reported SWE-Bench Pro 59% now open to independent verification (Artificial Analysis pre-drop analysis pending update)Source: HuggingFace (huggingface.co/MiniMaxAI/MiniMax-M3), Artificial Analysis (artificialanalysis.ai/articles/minimax-m3), The Decoder · 2026-06-12
- MODEL LAUNCH (2026-06-01): **MiniMax M3** -- coding/agent flagship with **1M-token context** (product page guarantees minimum 512K) and MSA sparse attention delivering vendor-claimed **>9x prefilling / >15x decoding speedups** at long context (per-token compute at 1M context = 1/20th of prior gen). Vendor-published benchmarks: SWE-Bench Pro 59.0%, Terminal-Bench 2.1 66.0%, BrowseComp 83.5 (above Opus 4.7's 79.3), PostTrainBench 37.1 (#3 behind Opus 4.7 and GPT-5.5), MCP Atlas 74.2%. WEIGHTS CAVEAT: announced as open-weight with release 'over the next 10 days,' but as of 6/10 there is NO M3 repo on HuggingFace (latest = M2.7) -- label it 'open-weight, release pending,' not available. Token Plan subscriptions: Plus $20/mo (~1.7B tokens), Max $50/mo (~5.1B), Ultra $120/mo (~9.8B)Source: MiniMax blog (minimax.io/blog/minimax-m3), minimax.io/models/text/m3, HuggingFace MiniMaxAI (checked 2026-06-10) · 2026-06-01
- MiniMax-M2.7 (released 2026-03-18) supersedes M2.5 -- 229B total params, sparse MoE positioned by MiniMax as a 'self-evolving' agent model. Verified third-party benchmarks: SWE-Bench Pro 56.22%, Terminal Bench 2 57.0%, SWE Multilingual 76.5%, Multi SWE Bench 52.7%, VIBE-Pro 55.6%, GDPval-AA Elo 1495. The old 'first open-weight to hit 80.2% SWE-Bench Verified matching Opus 4.6' framing was an M2.5-on-different-bench claim and does NOT carry forward to M2.7 directly -- SWE-Bench Pro and SWE-Bench Verified are not comparable. License labeled 'Other' on HuggingFace (not MIT like M2 / M2.5) -- verify before commercial useSource: MiniMax blog (minimax.io/news/minimax-m27-en), HuggingFace MiniMaxAI/MiniMax-M2.7, MarkTechPost · 2026-03-18
- M2 initial release required custom vLLM build -- community quants took 2-3 weeks to stabilize. M2.7 inherits the same architecture so the vLLM compatibility story carries forwardSource: GitHub MiniMax-AI/MiniMax-M2, Hugging Face discussions · 2026-02
- Per-layer QK-Norm is non-standard -- some inference backends had subtle bugs at long contextSource: Reddit r/LocalLLaMA · 2026-03
Best for
Agentic coding and tool-use workflows on a budget. Best price-to-SWE-Bench ratio of any open-weights model in 2026.
Not for
Teams that prioritize polished English writing (Mistral Large 3 or Claude are better), or anyone who needs the deepest ecosystem support (Llama is still that).
Our Verdict
MiniMax M2/M2.5 is the most cost-efficient frontier-tier open model in 2026. The 80.2% SWE-Bench Verified score is a genuine breakthrough -- matching Claude Opus 4.6 on real coding tasks at a tenth of the price. The sparse 10B-active MoE runs fast on moderate hardware. The main drawback is ecosystem: MiniMax has less Western infrastructure support than Alibaba or DeepSeek. If you're building an agentic product and want maximum value per token, M2.5 is an A-tier pick.
Sources
- Hugging Face: MiniMaxAI/MiniMax-H3 model card -- MiniMax H3 Community License, territory-limited open-weight grant (repo 2026-07-28, verified 2026-09-17) (accessed 2026-09-17)
- Hugging Face: MiniMaxAI/MiniMax-H3 docs/QA-about-License.md -- why open weights are limited to the EU, UK, South Korea and US (accessed 2026-09-17)
- Hugging Face: MiniMaxAI/MiniMax-Music3 model card -- 8B global + 0.6B local LLM, 5-minute songs, 32 kHz stereo (repo 2026-08-07) (accessed 2026-09-17)
- Hugging Face: MiniMaxAI/MiniMax-Music3 LICENSE -- community licence with prominent-attribution commercial term (accessed 2026-09-17)
- Artificial Analysis: MiniMax M3 (Intelligence Index 44, released June 2026) (accessed 2026-07-04)
- MiniMax: M2.7 release blog (2026-03-18) (accessed 2026-04-27)
- HuggingFace MiniMaxAI/MiniMax-M2.7 (accessed 2026-04-27)
- MarkTechPost: MiniMax M2.7 release coverage (accessed 2026-04-27)
- Artificial Analysis MiniMax M2 benchmarks (accessed 2026-04-13)
- Bytebot MiniMax M2.5 analysis (accessed 2026-04-13)
- Hugging Face MiniMaxAI collection (accessed 2026-04-13)
- GitHub MiniMax-AI/MiniMax-M2 (accessed 2026-04-13)
- OpenRouter pricing (accessed 2026-04-13)
Explore more MiniMax M3 rankings
Deeper leaderboards, benchmarks, task-specific tier lists, and status/pricing pages for MiniMax M3.
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Alternatives to MiniMax M3
Llama 4 (Meta)
Meta's open-weights family -- Scout (10M context), Maverick (multimodal 400B MoE). NOTE: Meta's frontier work moved to the proprietary Muse Spark line in April 2026; Llama remains downloadable and supported but is effectively in maintenance mode
Mistral AI
**New 2026-10-15: Timestral, Mistral's first time-series forecasting family (5M, 28M, 123M and 209M parameters, weights on Hugging Face since 10/14 under a non-Apache licence), with the 209M model claimed first on GIFT-Eval, TIME and fev-bench and CPU inference in 10-140 ms.** **Mistral Large 4 ('le Chonk', ML4) entered public preview on 2026-10-06: a 1T-parameter natively multimodal MoE with roughly 50B active parameters, a 1M context window, callable today as mistral-large-4 at $1.36 / $0.14 cached / $4.18 per 1M tokens (batch at half price), open weights promised by the end of October, and vendor-claimed open-weight leads in cyber (Cybench 93%), coding (DeepSWE v1.1 61.7%) and agentic work (AutomationBench 59.9%).** **Mistral opened a Munich hub for Physics AI and Industrial AI on 2026-09-28**, confirming the May 2026 acquisition of Emmi AI (30+ physicists and engineers in computational fluid dynamics, structural mechanics and multi-physics simulation), naming BMW (crash simulation) and Siemens Energy as partners, and committing to **build one gigawatt of European compute capacity by 2030**. **Mistral now powers Firefox Smart Window (2026-09-16)** -- Mozilla's beta AI browsing assistant runs on Mistral models for France and North America, UK and Germany later this year, with zero data retention. **EUR 3B Series D at a EUR 21B+ post-money (9/08, Samsung-led)**; Cloudera partnership (9/10); Medium 3 retired 8/31 in favour of Medium 3.5.
DeepSeek
**DeepSeek-V4.1-Flash-1017 (2026-10-17) replaced V4.1-Flash on the web, app and API at unchanged prices** (the deepseek-flash model name now serves it; MIT weights on Hugging Face), with higher scores and shorter outputs, and DeepSeek opened its in-house **Search and Fetch APIs at $0.75 per 1,000 calls per endpoint** -- server-side Search/Fetch calls through DeepSeek Harness, Claude Code and Codex stay free until 2026-10-31 16:00 UTC. Earlier: **DeepSeek-V4.1-Flash shipped 2026-09-10**: a 552B MoE on a new Causal Encoder-Decoder architecture (8B active for input, 16B for output), natively multimodal, MIT-licensed on HF, and priced BELOW the V4-Flash it replaces -- $0.15/$0.60 off-peak, $0.30/$1.20 peak per 1M, with cache-hit input cut to $0.003/$0.006. V4-Flash and V4-Flash-Vision-Exp are retired (aliases route to V4.1-Flash). DeepSeek said V4-Pro would be phased out on 9/14, then **reversed that four days later** -- V4-Pro stays on the API at unchanged $0.66/$1.98 off-peak, $1.32/$3.96 peak until V4.1-Pro lands. V4.1-Flash's vendor benchmarks beat V4-Pro (DeepSWE 74.2, Terminal-Bench 2.1 90.6, GPQA 90.9)
Gemma 4 (Google)
Google DeepMind's open-weights model family -- multimodal, 256K context, runs on edge devices **EmbeddingGemma 2 (announced 2026-10-06; weights on Hugging Face since 9/14) adds a 740M-parameter Apache 2.0 embedding model built on Gemma 4 that maps text, code, images, video and audio into one 768-dimensional space, with an 8K context, Matryoshka truncation down to 128 dimensions and about 191 MB of RAM for text-only use on a Pixel 11 Pro.**
DiffusionGemma (Google)
Google DeepMind's experimental open-weights TEXT-DIFFUSION model (June 10, 2026) -- 26B MoE (3.8B active), Apache 2.0, generates 256-token blocks in parallel with bidirectional attention for up to 4x faster output (1,000+ tok/s on H100). Trades some quality vs Gemma 4 for raw speed
Qwen (Alibaba)
**Qwen-Image-2.1 went open-weight (weights 2026-09-14, blog 2026-09-20)** -- a unified text-to-image and editing model with a 7B visual-generation component, native transparent (RGBA) output, up to 10 reference images and mask-guided local edits -- but under a **research-only licence, not Apache**: commercial use needs a separate licence from Alibaba. Alibaba's open-weights + API family, and August 2026 was its biggest open-release month yet: **Qwen3.8-2.4T-A95B -- the Max-class flagship -- went open-weight on 2026-08-08** (custom Qwen3.8-Max licence), **Qwen3.8-27B** dense landed Apache 2.0 (8/05), and **Qwen3.8-Flash-Next** (8/24 weights, 8/26 blog) previews the Qwen4 architecture: 125B main + 51B n-gram embeddings with only 6B active, served as Qwen3.8-Flash at $0.15/$0.47 per 1M. Qwen 3.7 Max remains the GA API flagship ($2.50/$7.50)
GLM / Z.ai (Zhipu AI)
**GLM 5.2 has a named production deployment at Perplexity: on 2026-09-21 Perplexity disclosed that Perplexity Computer sessions are served by GLM 5.2, post-trained by Perplexity on real user sessions** (21.2% relative cut in live tool-call failures). **GLM-5.3 and GLM-5.3-Flash released 2026-08-25** (weights on Hugging Face): 5.3 is a post-training-only upgrade of the 5.2 base that Z.ai calls 'the most capable open-weights model for coding' (Terminal Bench 2.1 88.2, DeepSWE 66.9, CyberGym 84.5) but moves from MIT to a custom licence; 5.3-Flash (320B/18B active, multimodal) stays MIT at $0.15/$0.50 per 1M.
Kimi K3 (Moonshot)
Moonshot's 2.8T-parameter Kimi K3 (launched 2026-07-16/17) is the largest open-weight model ever released -- 1M context, multimodal, $3/$15 per 1M via API, ranked best-available on Arena.AI at launch. WEIGHTS SHIPPED ~2026-07-26/27 on Hugging Face (2.8T total / 104B activated, safetensors) under a custom Kimi K3 License, not the Modified MIT of the K2 line
Nemotron (Nvidia)
Nvidia's open-weights family -- hybrid Mamba-Transformer MoE architecture, optimized for efficient reasoning on Nvidia hardware. Nemotron 3 Ultra (550B total / 55B active) shipped 2026-06-04 as the family flagship, joining Super (120B/12B, March) and Nano
Falcon (TII)
UAE's Technology Innovation Institute open-weights family -- Falcon 3 optimized for efficient sub-10B deployment on consumer hardware
gpt-oss (OpenAI)
OpenAI's FIRST open-weight models -- gpt-oss-120b (single 80GB GPU, near parity with o4-mini on reasoning) and gpt-oss-20b (runs on 16GB edge devices). Apache 2.0. Launched 2025-08-05. gpt-oss-safeguard ships in 2026 as the safety-tuned variant
IBM Granite 4.0
IBM's enterprise-focused open-weight family -- Granite 4.0 hybrid Mamba-2 + transformer architecture (70-80% memory reduction vs pure transformer), 3B to 32B sizes, Apache 2.0. First open model family to secure ISO 42001 certification. Nano 350M runs on CPU with 8-16GB RAM. 3B Vision variant landed 2026-04-01
Arcee Trinity-Large-Thinking
Arcee AI's US-made open-weight frontier reasoning model -- launched 2026-04-01. 398B total params, ~13B active. Sparse MoE (256 experts, 4 active = 1.56% routing). Apache 2.0, trained from scratch. #2 on PinchBench trailing only Claude 3.5 Opus. ~96% cheaper than Opus-4.6 on agentic tasks
Olmo 3 (AI2)
Allen Institute for AI's fully-open frontier reasoning models -- Olmo 3 family (2025-11-20) includes 7B and 32B sizes, four variants (Base, Think, Instruct, RLZero). Apache 2.0 with fully open data + checkpoints + training logs. Olmo 3-Think 32B matches Qwen3-32B-Thinking at 6x fewer training tokens
AI21 Jamba2
AI21 Labs' hybrid SSM-Transformer (Mamba-style) open-weight family -- Jamba2 launched 2026-01-08. Two sizes: 3B dense (runs on phones / laptops) and Jamba2 Mini MoE (12B active / 52B total). Apache 2.0, 256K context, mid-trained on 500B tokens
StepFun Step 3.7 Flash
StepFun's (China) agent-focused open-weight family -- Step 3.7 Flash (May 28 2026): 198B sparse MoE vision-language model, ~11B active, 256K context, Apache 2.0, ~400 tok/s, SWE-Bench Pro 56.3. Supersedes Step 3.5 Flash (Feb 2026) as the flagship
Cohere Command A
Cohere's enterprise-multilingual flagship -- 111B params, 256K context, runs on 2x H100. 23 languages. CC-BY-NC 4.0 on weights (research / non-commercial), commercial requires Cohere enterprise contract. Follow-ups: Command A Reasoning + Command A Vision
LongCat-2.0 (Meituan)
Meituan's open-source 1.6T-parameter MoE (~48B active) with native 1M-token context, MIT license -- trained entirely on domestic Chinese AI ASICs and revealed as the stealth 'Owl Alpha' model that had been topping OpenRouter
Inkling (Thinking Machines Lab)
Mira Murati's $12B lab ships its first model (2026-07-15): a 975B/41B-active open-weights MoE that reasons natively over text, images, and audio with a 1M-token context -- positioned not as the strongest model, but as the best starting point for fine-tuning via Tinker
Bonsai 27B (PrismML)
The first 27B-class model that runs on a phone (2026-07-14) -- ternary and 1-bit quantizations of Qwen3.6 27B squeeze a multimodal, tool-calling, 262K-context model into 3.9-5.9GB under Apache 2.0