MiniMax

fal's H3 Max Live achieves faster-than-playback generation for 5-second clips, letting viewers alter scenes via chat. 15-second clips still take 16 seconds, and cross-clip consistency is unsolved. At $144–288/hour for 768p, a single stream needs 1,400–2,900 concurrent viewers to break even. The real bottleneck is platform access rules across markets, not moderation tech.

Fal post-trained Minimax's H3 model, then ran it on their own inference engine at 35x the official endpoint speed. The result is H3 Max Live: video generates faster than you can watch it. Ethan Mollick first flagged the milestone; Fal employees turned it into an infinite Twitch stream, and Pieter Levels built a similar interactive feed. Twitch and YouTube quickly banned Fal's streams, so Fal launched fal.live. Quality is still slop, but this is the floor for real-time video generation. The post doesn't disclose pricing or exact latency numbers.

At ModCon 2026, Modular open-sourced Mojo 1.0 under Apache 2.0 and made Modular Cloud publicly available, already serving customers like MiniMax. The platform now supports AWS Trainium, Google TPUs, and Qualcomm Cloud AI 100 and Dragonfly accelerators. Mojo is getting native Windows support via a Microsoft collaboration. MAX drops device usage restrictions and moves to source-available with an open alliance program.

MiniMax today released Music 3.0, an open-weights music generation model. Given a creative concept and optional lyrics, it outputs a complete song with arrangement, performance, and vocals in one pass, up to 5 minutes long. The upgrade targets three pain points: accurately interpreting creative intent, maintaining that intent across a full song, and making vocals and instruments sound performed rather than synthesized. The new Hybrid-LM architecture uses an 8B global model for song-level structure and a 0.6B local model for per-frame acoustic detail, with flow matching and a Flow-VAE converting discrete predictions to continuous audio. The post does not disclose training data scale, inference latency, the specific open-source license, or quantitative benchmark comparisons.

MiniMax H3, the only open-source flagship video model this generation, released its weights with native ComfyUI support on day one. Community plugins cut generation time from 500+ seconds to just over 200. Blind tests show H3 matches Seedance 2.0 visually, though 2.5 still leads; hand physics correctness is a surprise plus. Minimum hardware is 2×RTX 4090 with 384GB RAM, production config 4×H200. OpenAI acquired Ona to move Codex to the cloud—Tibo predicts laptops will be mere control surfaces in two to three months. On the reverse-engineering front, AI plus Frida hooked PBKDF2 to extract WeChat 4.1.8 macOS database keys in one hour, bypassing removed memory signatures. Sol's over-engineering saga continues: it built a hard gate, got stuck behind it, then researched how to bypass it. Math harness day four went extreme—banning code made the model stronger through pure reasoning.

Simon Willison got the MiniMax-H3 MLX port running on an M5 Max MacBook Pro. The model, released two days ago, takes text, images, audio, and video as input and outputs 15-second video clips with audio. He downloaded ~115 GB of model files and generated one clip from a text prompt in just under 45 minutes. The visuals looked good, but the audio came out as garbled speech-like noise—he didn't follow the official prompting guide for audio, so that part isn't a fair test.

MiniMax released H3, its third-gen video model, with open weights and day-0 ComfyUI integration. It handles text-to-video, image-to-video, first-and-last-frame control, and motion transfer from reference clips. Output is up to 2K, 15 seconds, with native stereo audio generated in the same pass—no post-processing. The post claims it runs locally on a 3060; I'd wait for community benchmarks before taking that at face value.

MiniMax H3 unifies text, image, video, and audio understanding into one model. It generates up to 15-second videos at 2K resolution with native stereo sound. The company claims per-second pricing at 2K is under one-third of mainstream models, and 768p is under half the price of mainstream 720p. Model weights will be open-sourced in the coming days, subject to legal review. The post does not specify the open-source license or exact release date.

MiniMax launched H3 on Product Hunt, an open multimodal model for unified video generation. It takes text, image, and audio inputs to produce 2K video with native stereo sound. The model focuses on accurate text rendering, visual packaging, and complex instruction following for commercial content like motion design and branding. The post doesn't disclose parameter count, inference latency, or the specific open license. I'd hold off on the 'world-leading' claim until a technical report is out.

Z.ai's GLM-5.2 scores 51 on the Artificial Analysis Intelligence Index v4.1, ahead of MiniMax-M3 (44) and DeepSeek V4 Pro (44), making it the top open-weights model. It keeps the same 744B-total / 40B-active parameter count as GLM-5.1 but posts big gains in scientific reasoning and agentic tasks—HLE jumps 12 points to 40%, CritPt up 16 points to 21%. On GDPval-AA v2, a real-world agent benchmark, it hits 1524, effectively level with GPT-5.5 (xhigh reasoning). The trade-off: it averages 43k output tokens per task, up from 26k on GLM-5.1. API pricing stays at $1.4/$4.4/$0.26 per 1M input/output/cache-hit tokens, context window expands from 200K to 1M, and it ships under an MIT license.

MiniMax open-sourced M3 model weights last Friday—428B total parameters, 23B active—along with the MSA sparse attention paper that cuts long-context inference cost. M3 is the first open-source model trained with interleaved text and image data from the pre-training stage. Two weeks post-release, it ranked #1 among open-source models on the Artificial Analysis Intelligence Index and GDPval-AA, reached Pareto-optimal on Code Arena WebDev, and topped Chinese models on Vals.AI. Output speed improved from ~30 TPS to ~80 TPS, with another 30–40% planned. A usage dashboard was added to the Token Plan backend.

MiniMax released M3 model weights on HuggingFace. The post says 'M3 would never,' a jab at Anthropic's Fable 5 and Mythos 5 being forcibly disabled under US export controls, blocking all foreign nationals. The post doesn't disclose M3's parameter count, benchmarks, or license.

MiniMax published a paper introducing MSA, a blockwise sparse attention built on GQA. A lightweight index branch scores KV blocks and picks a top-k subset per GQA group, then the main branch runs exact attention only on those blocks. With a co-designed GPU kernel, a 109B-parameter multimodal model achieves 14.2× prefill and 7.6× decoding wall-clock speedups on H800 at 1M context, matching full GQA quality. Code and inference kernel are open-sourced, along with a model called MiniMax-M3. The Reddit poster is curious whether the 109B model can run on consumer GPUs; the post doesn't say if weights will be released.

MiniMax uploaded M3 weights to HuggingFace, with the tech report and full weights expected in about 10 days. It's a 428B-total-param, 23B-active-param hybrid model using MiniMax sparse attention to push the context window to 1M tokens, plus native multimodal support. Coding and agent scores: SWE-Bench Pro 59.0%, Terminal Bench 2.1 66.0%, SWE-fficiency 34.8%, KernelBench Hard 28.8%, MCP Atlas 74.2%. MiniMax Code tool and API platform launched alongside. The post doesn't disclose training data, inference cost, or license terms — I'd hold off on usability judgments until the report drops.

MiniMax released MiniMax-M3 weights on Hugging Face. It's a mixture-of-experts model with ~428B total parameters and ~23B activated per inference. The post doesn't disclose training data, benchmarks, or minimum VRAM for local runs.

MiniMax M3 ranks first among open-source models on Artificial Analysis, and the article says it supports a 1M-token context window, used 100T-scale pretraining, and will open-source its weights and full technical report within 10 days.

StepFun released the open-weight Step 3.7 Flash model for fast agentic coding, with tool calling and multimodal understanding, and the model is already available in Kilo alongside MiniMax M3.

MiniMax released the open-source M3 model with coding, a 1M-token context window, and native multimodal support; M3 scores 59.0% on SWE-Bench Pro, 83.5% on BrowseComp, and costs about one-twelfth per token versus GPT-5.5.

MiniMax released M3 as an open-source unified model with coding, agent, and native multimodal capabilities, supporting a 1M-token context window and using MiniMax Sparse Attention to cut per-token compute at 1M context to 1/20 of its predecessor, with over 9x faster prefill and over 15x faster decoding.

Reddit user thibautrey describes a 3-step setup: edit Codex Desktop config.toml, store an API key, and use a multicodex proxy alias to map gpt-5.3-codex to MiniMax-Latest. The post lists a local base_url of 127.0.0.1:1455 and says the proxy disguises returned model names as gpt-5.3-codex.