AI engineering, October 20, 2026

Top News · 423 HN points

M3.1 Flash is MiniMax's hosted model for coding, computer use, and multi-step workflows, with output priced at $0.60 per million tokens. You can access it through the MiniMax API, M Plan, and MiniMax Code.

Here's what you get:

• Input costs $0.15 per million tokens, while cached input costs $0.015 per million tokens.
• Everyday output speeds reach up to 200+ tokens per second, with median speeds of 170+ tokens per second.
• Complex software task performance reaches 73.7% on DeepSWE v1.1.
• Computer-use results include 61.7 on OSWorld V2, alongside 29.6 on Agents' Last Exam.

One catch: Model weights are not available yet.

Try it: Use the MiniMax API at https://platform.minimax.io, or download MiniMax Code for desktop at https://agent.minimax.io/download.

Sources: minimax.io

Top News · 234 HN points

Mercury 3 is Inception's flagship diffusion-based LLM. Inception says it matches or beats GPT-6 Luna and Claude Haiku 5.5 while running 4-8x faster.

Here's what changed:

• Generation reaches 1,068 tokens per second on NVIDIA GPUs.
• Launch pricing is 50% below the standard rates of $0.20 per million input tokens and $0.70 per million output tokens.
• Cached input tokens cost 90% less than the input price.
• The API is OpenAI API compatible, and the model supports a 1-million-token context window, up from Mercury 2.5's 260,000 tokens.

One catch: The 50% Mind2Web completion result comes from Inception's own evaluation of 50 live navigation tasks.

Try it: Use the Inception API Platform with 100 million free API tokens, or access Mercury 3 through OpenRouter or Baseten. Also available via Vercel

Sources: inceptionlabs.ai · inceptionlabs.ai

Top News · Reported · pytorch.org

If you want to move an existing PyTorch model onto Trainium, TorchNeuron gives you a native backend. Deepgram reports about 60% more input audio seconds per dollar on Trainium than on its existing GPU baselines.

Here's what it supports:

• Public beta is scheduled for October 20, 2026, for researchers and developers, with packages from the AWS Neuron pip repository.
• Trainium2 and Trainium3 support eager execution, torch.compile, and custom kernels through NKI.
• Distributed workloads can use DDP, FSDP2, tensor parallelism, and pipeline parallelism.

One catch: The first release excludes dynamic and symbolic shapes and sparse tensors, and some operators lack native lowerings.

Try it: Install pip install torch-neuronx from the AWS Neuron pip repository. Move your model with model = MyModel().to('neuron'), then compile with torch.compile(model, backend="neuron").

Sources: pytorch.org

1. Unsloth runs 552B DeepSeek-V4.1-Flash-1017 locally at 1-bit in about 125 GB using SSD offloading
2. AWS adds push-based KV transfers to vLLM for up to 3x better time-to-first-token
3. Prime Intellect's prime-rl trains Qwen3-235B-A22B 1.15x faster than torchtitan on identical hardware
4. UltraBERT-Embed-150M embeds up to 32K tokens into one 768-dimensional vector
5. OpenCode pairs a full-access coding agent with a read-only planning agent · 70 Stars today
6. HandsOn benchmarks robotic manipulation with 66 tasks and 18,000 human demonstrations