Best Open Source Models Distilled from RL-Trained Reasoners in 2026

Key Takeaways

1. Modal

Modal provides the AI infrastructure for serving, evaluating, and fine-tuning distilled reasoning models without managing GPU fleets or Kubernetes. While the remaining entries are model checkpoints, Modal is the platform that turns those checkpoints into production APIs and scalable training jobs.

How Does Modal Work?

Modal takes Python code, builds it into a container image, and runs it on serverless cloud compute. Developers select a GPU, attach model weights from a Volume or cloud bucket, and expose an endpoint or launch a batch job. Modal then handles scheduling, container lifecycle, and autoscaling.

Enterprise-Grade Security

Modal has successfully completed a SOC 2 Type II audit and supports HIPAA-compliant workloads for Enterprise customers through a signed BAA. User data is encrypted in transit and at rest, public Modal APIs use TLS 1.3, and compute jobs use gVisor-based isolation.

Purpose-Built for Reasoning Model Deployment

Long reasoning traces increase generation time and KV-cache pressure. Modal's GPU scheduler, container autoscaling, memory snapshots, and optimized filesystem address the operational side of those workloads. Teams can use one platform for low-latency endpoints, asynchronous evaluations, reinforcement-learning rollouts, and fine-tuning. Best For: Teams that want production GPU infrastructure without building a serving and orchestration layer from scratch.

2. DeepSeek-R1-Distill-Qwen-32B

DeepSeek-R1-Distill-Qwen-32B is the largest dense Qwen checkpoint in the January distill family. DeepSeek fine-tuned Qwen2.5-32B with reasoning samples generated by DeepSeek-R1, transferring much of the teacher's mathematical and coding behavior into a model that can be served on a single high-memory GPU or a modest multi-GPU setup.

Benchmark Performance

DeepSeek reports the following model-card results:

Technical Specifications

Key Strengths

3. DeepSeek-R1-0528-Qwen3-8B

DeepSeek-R1-0528-Qwen3-8B is the newer compact distill produced from the May 2025 DeepSeek-R1-0528 teacher. DeepSeek post-trained Qwen3-8B Base on the updated teacher's chain-of-thought data and reports state-of-the-art AIME 2024 performance among open-source models of its class at release.

Reported Benchmark Performance

Architectural Notes

Unlike the January checkpoints based on Qwen2.5, this model starts from Qwen3-8B Base and inherits Qwen3's grouped-query attention and long-context extension path. Its native context is 32,768 tokens; YaRN scaling extends the configured maximum to 131,072 tokens. Long-context support increases KV-cache requirements, so the maximum practical sequence length still depends on GPU memory and concurrency.

Technical Specifications

Key Strengths

4. DeepSeek-R1-Distill-Qwen-14B

The 14B checkpoint occupies the middle of the January family. It retains substantially more reasoning capability than the 7B model while cutting the 32B checkpoint's approximate BF16 weight footprint in half.

Benchmark Performance

Efficiency Analysis

At an estimated 40 GB for BF16 weights, the 14B model fits naturally on a 48 GB-class GPU at shorter contexts, though production serving must also reserve memory for the KV cache, activations, and runtime. Quantization can move it to smaller hardware. Its MATH-500 score is only 0.4 points below the 32B checkpoint, while its Codeforces rating trails by 210 points.

Technical Specifications

5. OpenR1-Distill-7B

OpenR1-Distill-7B is Hugging Face's community reproduction of the distilled-reasoning recipe. It is built from Qwen2.5-Math-7B with an extended-RoPE intermediate checkpoint and supervised fine-tuning on reasoning traces, making it useful for teams that value an open development process and reproducible training artifacts.

Community-Driven Development

The Open R1 project publishes training code, datasets, recipes, and evaluation tooling rather than only a final checkpoint. That transparency helps researchers inspect the data pipeline, reproduce a distillation run, and adapt the recipe to a domain-specific teacher or corpus.

Benchmark Performance (OpenR1-Distill-7B)

Key Strengths

6. DeepSeek-R1-Distill-Qwen-7B

DeepSeek-R1-Distill-Qwen-7B offers a compact production tier based on Qwen2.5-Math-7B. It substantially lowers memory requirements while preserving useful math and coding performance.

Benchmark Performance

Comparison to Other Models, by Benchmark

Against OpenR1-Distill-7B, the DeepSeek checkpoint reports 2.8 points more on AIME 2024 and 3.8 points more on MATH-500. OpenR1 reports 3.7 points more on GPQA Diamond and 1.8 points more on LiveCodeBench. These figures come from the respective model cards and may reflect different harness versions or evaluation settings.

Technical Specifications

7. DeepSeek-R1-Distill-Qwen-1.5B

The 1.5B model is the smallest official Qwen distill. It gives developers a low-cost way to test chain-of-thought behavior, run offline applications, and build edge prototypes where a 7B model is still too large.

Benchmark Performance

Comparison to Larger Models, by Benchmark

The 1.5B checkpoint trails the 7B model by 26.6 points on AIME 2024 pass@1, 8.9 points on MATH-500, 15.3 points on GPQA Diamond, and 20.7 points on LiveCodeBench. The tradeoff is a fourfold reduction in the estimated BF16 weight footprint, from 16 GB to 4 GB.

Technical Specifications

Local and Edge Deployment Potential

A roughly 4 GB BF16 weight estimate makes the 1.5B checkpoint accessible on consumer hardware, although framework overhead and KV cache still add memory use. Four-bit quantization can shrink the model further for laptops and edge devices. Its lower benchmark ceiling makes it better suited to constrained, well-defined tasks than to frontier-level general reasoning.

Why Modal Stands Out for Deploying Distilled Reasoning Models

Purpose-Built AI Infrastructure

Modal's custom container runtime, scheduler, image builder, and filesystem are designed around bursty AI workloads. Developers can select B200, H200, H100, A100, L40S, and other GPUs without managing cloud reservations or separate orchestration systems.

A Code-First Path from Experimentation to Production

The same Python application can begin as a one-off benchmark, become a batch evaluation, and then expose an autoscaling web endpoint. Container images, GPU requirements, secrets, concurrency, and scaling behavior remain versioned in code, reducing the gap between a local prototype and production.

Complete Deployment Primitives

Multi-Node Training Support

Modal's multi-node training support lets teams fine-tune larger checkpoints and run distributed reinforcement-learning workloads on B200, H200, and H100 clusters. Private cluster networking offers up to 3,200 Gbps of RDMA scale-out bandwidth for communication-heavy jobs.

Enterprise-Grade Security and Compliance

Modal is SOC 2 Type II compliant and supports HIPAA-compliant workloads for Enterprise customers through a BAA. The platform encrypts user data in transit and at rest, uses TLS 1.3 for public APIs, and isolates compute jobs with gVisor.

Proven Adoption at Scale

More than 10,000 teams use Modal for inference, training, batch processing, and sandboxed execution. Its multi-cloud capacity pool reduces dependence on one provider and helps teams obtain GPU capacity during demand spikes. For distilled reasoning models, that combination provides a direct path from a 1.5B local experiment to a 32B production service or a distributed fine-tuning run.