Falcon-H1R-7B: Abu Dhabi's 7B Model Surpasses AI Giants in Advanced Reasoning, Math, and Coding

Introducing the Falcon-H1R-7B: A New Paradigm in AI Reasoning

The Technology Innovation Institute (TII) in Abu Dhabi recently introduced Falcon-H1R-7B, a 7-billion-parameter model engineered specifically for advanced reasoning tasks. This compact AI system has notably matched or exceeded the capabilities of many larger reasoning models, ranging from 14 billion to 47 billion parameters, across various benchmarks in mathematics, coding, and general intelligence. Built upon the foundation of Falcon H1 7B Base, the model is now accessible on Hugging Face as part of the Falcon-H1R collection.

Innovative Architecture for Enhanced Performance

Falcon-H1R-7B integrates several innovative design elements to achieve its efficiency and accuracy. It features a hybrid architecture combining Transformer layers with Mamba2 state space components. This causal decoder-only model leverages Transformer blocks for attention-based reasoning and incorporates Mamba2 components for linear-time sequence processing and improved memory scaling, particularly with longer context lengths. This architectural synergy addresses key aspects of reasoning efficiency: speed, token economy, and accuracy.

A standout feature is its remarkably long context window, supporting up to 256,000 tokens in standard vLLM deployments. This extensive capacity facilitates complex chain-of-thought processes, multi-step tool interactions, and handling large multi-document prompts within a single pass. The hybrid design plays a crucial role in managing memory consumption at such sequence lengths, contributing to higher throughput compared to a pure Transformer 7B model on identical hardware configurations.

Specialized Training for Complex Challenges

The development of Falcon-H1R-7B involved a meticulous two-stage training methodology:

• Supervised Fine-Tuning (SFT): Initially, the model underwent a cold-start supervised fine-tuning phase on Falcon-H1-7B Base. This stage involved a diverse dataset of step-by-step long-form reasoning traces in areas like mathematics, coding, and science, alongside non-reasoning tasks such as chat and tool calling. The training data utilized a difficulty-aware filtering process, prioritizing challenging problems while de-emphasizing simpler ones. Targets during this phase could extend up to 48,000 tokens, exposing the model to comprehensive derivations and complete solution pathways.
• Reinforcement Learning (RL) with GRPO: Following SFT, the model was refined using Group Relative Policy Optimization (GRPO), a reinforcement learning technique. Rewards were granted based on the verifiable correctness of the generated reasoning chains. For mathematical problems, symbolic checks on final answers were employed, while code generation was validated by executing programs against unit tests. This RL phase was instrumental in encouraging the model to retain valuable intermediate steps while adhering to token budgets.

This specialized training regimen ensures that Falcon-H1R-7B is precisely calibrated for chain-of-thought reasoning, distinguishing it from general conversational AI models.

Benchmark Performance: Outperforming Expectations

Falcon-H1R-7B demonstrated impressive results across various benchmarks:

• Mathematics: The model achieved an aggregate score of 73.96%, surpassing competitor models like Apriel-1.5-15B (69.32%) and larger systems such as Qwen3-32B and Nemotron-H-47B. Notable individual scores included 88.1% on AIME 24 and 83.1% on AIME 25.
• Coding and Agentic Tasks: With an aggregate score of 33.95%, Falcon-H1R-7B scored 68.6% on LiveCodeBench v6, outperforming Qwen3-32B. It also performed strongly on SciCode and Terminal Bench Hard.
• General Reasoning: Recording an aggregate score of 49.48%, the model achieved 72.1% on MMLU Pro, exceeding all other 8B models in its comparison group, and 61.3% on GPQA D.

These results underscore the potential for a 7-billion-parameter model to achieve performance levels typically associated with much larger models, provided its architecture and training are optimized for specific reasoning challenges.

Optimized Inference and Scalability

Beyond its impressive benchmark scores, Falcon-H1R-7B also exhibits superior inference throughput and test-time scaling. For example, processing a 512-token input and generating a 32,000-token output, the model achieved approximately 1,000 to 1,500 tokens per second per GPU at batch sizes of 32 and 64, respectively. This throughput nearly doubles that of Qwen3-8B under similar conditions. The hybrid Transformer-Mamba architecture is crucial for this efficiency, as it mitigates the quadratic computational cost associated with attention mechanisms in long sequences.

The model also incorporates Deep Think with Confidence (DeepConf) for enhanced test-time scaling. This method involves executing multiple chains of thought in parallel and then utilizing the model's own next-token confidence scores to filter out suboptimal traces, retaining only high-quality outputs. This approach allows Falcon-H1R-7B to achieve high accuracy, such as 96.7% on AIME 24 and AIME 25 with a controlled token budget, establishing a favorable balance between accuracy and token cost compared to various 8B, 14B, and 32B reasoning models.