Maincode

Introducing the Matilda Platypus open-weight model family. Intelligence that fits. Open-weight LLMs have made powerful AI accessible to everyone, but are limited to fixed sizes. When 4B is too small for your workload, but 8B is more than you need, you have had to compromise. Matilda Platypus is built on the existing Qwen3 open-weight model family, but without the predefined model sizes. Through the method of model stitching, we have delivered eight new model sizes, ranging from 1.5B to 13.5B parameters, filling the gaps between the original Qwen3 releases. The Matilda Platypus models don’t compromise on performance. Evaluations have found that they deliver capabilities that are appropriate to their size, with a number of configurations outperforming their larger parent models in specific benchmarks. These new LLMs can be further fine-tuned and adapted for downstream tasks, just like conventional open-weight models. Our goal is to remove constraints of predefined models, and deliver LLM’s that fit your compute budget, performance and real-world workload needs. Find your model fit with Matilda Platypus. maincode.com

To showcase the functionality of Matilda and Matilda Jev, we set them against one another in a game of poker. As the Agent SDK powers Matilda and the Client SDK's new systemOne.evaluate() API powers Matilda Jev, this showcase presents how the two different models approach the same task. Matilda thinks, considers and then acts, while Matilda Jev forms the poker game’s current state and available actions as a question. The model then returns the percentages for each action and executes the highest-ranked option. Within this game, the bet/raise sizing is a follow-up evaluate() call with pre-set bet sizing based on available chips and the game type. In the right-hand panel, you can view how each model makes its decision for every hand. This showcase also presents how easily Matilda plugs into a TypeScript web app to power new experiences. Learn more about Matilda today. maincode.com

What makes Matilda Train different to other providers? Each training run is isolated on Australian infrastructure operated by Maincode, with no outbound internet access, so your training data cannot be sent elsewhere during a run. Many other providers train models in the US or EU. Train any open-weight model your code can load, including Qwen, Llama and Gemma. When training is complete, the model goes into your workspace. You own the weights outright, so you never pay to keep a trained model or get locked into a provider’s serving fees. In the MLPerf Llama 2 70B fine-tuning benchmark, Matilda Train ran 2.7× faster and 4× cheaper per run than AWS Sydney, so you will also receive superior performance at a more affordable price. Fine-tune and own your models while keeping your data in Australia with Matilda Train. Sign up today. train.matilda.com

Your data shouldn’t have to leave Australia to train a model. Matilda Train runs on Australian infrastructure and every run is isolated, with its own identity, its own filesystem and no outbound internet. This means your training data cannot be sent elsewhere during a run. When you have completed your model’s training, it is yours. Checkpoints, adapters and merged weights are written to your workspace as standard files, ready to download and serve anywhere. Trusted by Australian companies, including Monash University, RMIT University and CSIRO. Bring your training and own the model with Matilda Train. Sign up today. train.matilda.com

Why should you train your model with Matilda Train? Matilda train allows you to fine-tune your model and own everything that is produced, unlike other providers who will keep your model and charge you to serve it. You will train on software that is 2.7x faster than the best published 8xH100 results and 4x cheaper to run than AWS Sydney. Get GPUs on demand from $9.35 per GPU-hour rather than waiting months, with all training run in Australia on AMD Instinct MI355X and NVIDIA B300 GPUs. Sign up today to begin running your model training, right here in Australia. train.matilda.com

Introducing Matilda Train. Matilda Train enables you to train models that you own, while keeping your data right here in Australia. While other clouds keep your fine-tuned model and charge you to serve it, with Matilda Train you pay for the training run, and everything produced is yours. Your training will run on AMD Instinct MI355X and NVIDIA B300 GPUs hosted in Australia, available from $8.50 per GPU-hour on demand. Train your model in three easy steps 1. Connect your agent 2. Ask for the model you want 3. Download the weights and serve them anywhere you like Matilda Train is 2.7x faster than 8xH100s and 4x cheaper per run than AWS Sydney. Sign up today and a Maincode data scientist will help port your code and tune your first runs. train.matilda.com

Today we are open-sourcing Matilda-K3, a 2.8T-parameter frontier model post-trained in Australia with our own conditional post-training. Conditional post-training changes how a model behaves on the requests we target and leaves every other request to the frozen base model, so the changes are precise and the original capability stays intact. Matilda-K3 is built on Kimi K3, the open-weight model from Kimi (Moonshot AI). The strongest open-weight models today come from a small number of labs, and a model carries the assumptions of the lab that trained it. Our post-training keeps the base model's capability and changes how it behaves in two targeted areas: its identity and its biases. The base weights are frozen and shipped unmodified. Everything else is left unchanged. It holds its identity under pressure, presenting as Matilda in all 474 held-out adversarial prompts, including jailbreaks, role-play and long-document attacks. On contested political questions, balanced or facts-only answers rose from 9% to 92% and one-sided answers fell from 54% to 3%, with factual accuracy unchanged. It keeps the full capability of its base: 95.0% on AIME 2025 and 99.4% on HumanEval, both within run-to-run variation of the original model, with a 1M-token context window, native reasoning, and image and video input. This approach can be applied to shape how a frontier model behaves for a specific organisation, including identity, tone, and policy, without sacrificing its capability.

Today we are open-sourcing Matilda Jev, Australia's first decision model, conditioned for Australian deployment and post-trained with our own specialised recipe. Decision models do not generate text. They answer a typed question and respond with a calibrated probability for each option in a single forward pass, reducing hallucination and making intelligent decisions faster and more efficient. This release proves that highly capable AI models can be built, post-trained, evaluated, and served entirely within Australia. Post-trained end-to-end on our local infrastructure, the model achieved a median decision latency of 56.8ms on a single @AMD MI355X across 150,317 evaluation requests.