Today we’re releasing DeepBrainz-R1, a family of reasoning-first Small Language Models (SLMs) designed for agentic AI systems in real-world production.

Agentic systems don’t ask once — they reason repeatedly. Tool calls, verification loops, schema-constrained outputs, retries, and long-context planning fundamentally change the economics and reliability requirements of language models. LLM-only stacks struggle under this load.

DeepBrainz-R1 is built from the opposite premise:

Reasoning is a trained behavior, not an emergent side-effect of scale.

What DeepBrainz-R1 is designed for

Repeatable multi-step reasoning, not one-shot chat
Agent-compatible behavior: tool use, structured outputs, low-variance reasoning
Production economics: lower latency, predictable cost, deployability
Inference-time scalability: compute where needed, not everywhere

The R1 lineup

DeepBrainz-R1-4B — Flagship production model Best starting point for reliable agentic systems.
DeepBrainz-R1-2B — Balanced production model Strong reasoning with lower cost and latency.
DeepBrainz-R1-0.6B-v2 — Canonical small model Cost-efficient baseline for small-model agent workloads.
Long-context variants (16K / 40K) — early and experimental
Research checkpoints — raw artifacts for ablation and evaluation
Community quantizations (GGUF, low-bit) — community-maintained, not officially supported

We publish supported releases, experimental variants, and research checkpoints separately to keep expectations clear for builders, enterprises, and researchers.

Why now

2026 is the year agentic AI stops being a demo and starts becoming infrastructure. Infrastructure cannot rely on LLM-only economics or LLM-only reliability. Reasoning-first SLMs are the only viable path to scaling agents sustainably.

— DeepBrainz AI & Labs

DeepBrainz-R1-2B

DeepBrainz-R1-2B is a compact, high-performance reasoning model engineered by DeepBrainz AI & Labs. It is part of the DeepBrainz-R1 Series, designed to deliver frontier-class reasoning capabilities in cost-effective parameter sizes.

This variant features a 32,768 token context window, optimized for processing medium-to-long documents and codebases.

🚀 Model Highlights

Parameter Count: ~2B
Context Window: 32,768 tokens
Specialization: STEM Reasoning, Logic, Code Analysis
Architecture: Optimized Dense Transformer
Deployment: Ready for vLLM, SGLang, and local inference

🎯 Intended Use Cases

Agentic Workflows: Reliability in multi-step planning tasks.
Math & Science: Solving complex word problems and equations.
Code Generation: Writing and debugging algorithms.
Structured Data Extraction: Parsing and reasoning over unstructured text.

Note: This model is post-trained for reasoning and agentic reliability.
For conversational chat, additional instruction tuning is recommended.

💻 Usage

python

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "DeepBrainz/DeepBrainz-R1-2B"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="bfloat16",
    device_map="auto"
)

prompt = "Analyze the time complexity of the following algorithm:"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🏗️ Technical Summary

The model has undergone post-training to enhance reasoning quality, stability, and agentic reliability.

Detailed post-training recipes and dataset compositions are not fully disclosed.

📜 License

This model is released under the Apache 2.0 license, allowing for academic and commercial use.

DeepBrainz AI & Labs
Advancing General Intelligence through Scalable Reasoning