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MiniMax has just released MiniMax M3.1 Flash on its API, priced at $0.15 per 1M input tokens . MiniMax also confirmed that Space Bunny was an earlier M3.1 Flash build. That stealth model processed over 200T tokens. For developers, the message is near-frontier agentic scores at Flash-tier cost. Model weights are due in late October, 2026. TL;DR Size: 428B total parameters, about 23B active (MoE); 1,000,000-token context Runs on: MiniMax API, M Plan and MiniMax Code today. Self-hosting hardware not disclosed; weights arrive Oct 23 Performance: Near-frontier on agentic office work, mid-pack on hard coding, all MiniMax-reported Best: Automation-Bench v1.0.6 at 62.2, the top score in MiniMax’s table Worst: Terminal Bench 4.0 at 38.1, versus 51.8 for Opus 5 What is MiniMax M3.1 Flash? MiniMax M3.1 Flash is a multimodal mixture-of-experts model from MiniMax. It targets coding, agentic office work and visual reasoning. MiniMax’s product page ...

NVIDIA has released the full NVIDIA Run:ai platform as open source software. The source code ships under the Apache 2.0 license on GitHub. NVIDIA Run:ai open source puts production GPU scheduling, quotas and workload management in developers’ hands. The release, makes Run:ai part of the NVIDIA DSX AI Factory platform. Platform teams can now inspect, fork and self-host the stack without a paid subscription. TL;DR Size: Monorepo with control plane, cluster services, installer, CLI and MCP server. Local dev builds about 50 Go services. Runs on: Kubernetes or OpenShift clusters with NVIDIA GPUs. Local dev runs on macOS or Linux with 8 simulated Tesla-A10 GPUs, no GPU needed. Performance: NVIDIA published no platform-level benchmark for this release. Best: The related Model Streamer loaded models 3x to 6x faster on 8 NVIDIA H100 VMs on Azure. Worst: The community edition has no NVIDIA enterprise support. Local builds need 16 GB RAM and 100 GB disk on Lin...

Mistral AI has released Timestral , its first family of time series foundation models. Timestral is a set of 4 zero-shot forecasting models from Mistral AI, sized from 5M to 209M parameters. The largest checkpoint ranks first among pretrained, zero-shot models on GIFT-Eval, TIME and fev-bench. It does this without a GPU at inference. For developers, this Timestral time series foundation model means probabilistic forecasts on standard server CPUs, with open weights and inference code . TL;DR Size: 4 models: 5M, 28M, 123M, 209M parameters (dense). Context up to 32,768 time steps. Runs on: Standard server CPU, no GPU needed. 209M weights are 837 MB in fp32. GPU optional. Performance: #1 among zero-shot pretrained models on GIFT-Eval, TIME and fev-bench (Mistral-reported). Best: fev-bench SQL skill score of 50.39, ahead of TimesFM-3 at 48.99. Worst: GIFT-Eval MASE of 0.658 leads t0-gamma (0.661) by only 0.003. What is Timestral? Timestral is a time...

Google Cloud AI Research has open-sourced Harness² . It is a recursive agent harnessing framework that improves the code and files around a frozen model. For developers, the pitch is simple. Your agent may get better without a better model. TL;DR Size: A framework, not a model. 0 parameters trained; 8 editable harness components. Version 0.1.0 . Runs on: Linux with Python 3.12 or 3.13 and uv. Model runs call models through Vertex AI. GPU requirement: Not disclosed. Performance: 1 refinement step improved all 4 model and harness setups on all 3 benchmarks. Best: GLM-5 under OpenCode rose from 64.2 to 75.7 on WorkBuddy-Bench (+11.5 points). Worst: GLM-5 under Codex gained only 1.2 points on JobBench (23.6 to 24.8). What is Harness²? Harness² is a plug-and-play framework from Google Research that refines an agent’s harness during deployment. A harness is everything around the model: prompts, tools, memory, skills and orchestration . Harnessing ...