Reflection Unveils Beam: 501B Parameter MoE with 23B Active Parameters and Apache 2.0 Release
Reflection AI announced Beam, a massive 501-billion-parameter open-weight Mixture-of-Experts architecture that routes tokens through only 23 billion active parameters. Apache 2.0 weights and inference kernels will drop later in October 2026, offering frontier reasoning on multi-GPU server clusters.
Reflection AI announced Beam on October 6, 2026, revealing a 501-billion-parameter Mixture-of-Experts (MoE) foundation model. Unlike proprietary frontier models locked behind closed API endpoints, Reflection committed to releasing the full model weights under the permissive Apache 2.0 license later this month.
Beam addresses a central dilemma in open-source AI: achieving the reasoning capability of half-trillion-parameter systems without requiring a multi-million-dollar supercomputer just to generate output tokens.
Sparse Routing Topology: 501B Total, 23B Active
Beam arranges its parameters into 64 expert sub-networks per feed-forward layer. A high-resolution gating network calculates affinity scores for every incoming token, dispatching each token exclusively to the top-4 experts:
┌────────────────────────────────────────────────────────────────────────┐
│ Reflection Beam Routing Topology │
├────────────────────────────────────────────────────────────────────────┤
│ Total Parameters: 501 Billion (Weights resident in node memory) │
│ Active Forward Pass: 23 Billion (Parameters computed per token) │
│ Expert Architecture: 64 experts per layer, Top-4 sparse gating │
│ Native Context Window: 256,000 tokens (RoPE scaled) │
│ Quantization Targets: FP8 (Native), INT4 (vLLM / TensorRT-LLM) │
└────────────────────────────────────────────────────────────────────────┘
Because only 23 billion parameters calculate matrix multiplications during the forward pass, inference latency mirrors that of a compact dense model, reaching 48 tokens per second on an 8x H100 server node.
Evaluation Performance: Beam vs. Open and Closed Frontiers
Reflection AI shared third-party benchmark evaluations comparing Beam against open-weight models (Llama 3.3 70B, DeepSeek V3) and proprietary frontier models:
| Benchmark Dataset | Evaluation Domain | Llama 3.3 70B | DeepSeek V3 (671B MoE) | Reflection Beam (501B) | Claude 3.5 Sonnet |
|---|---|---|---|---|---|
| MATH-500 | Advanced Mathematics | 68.4% | 79.8% | 81.2% | 78.3% |
| SWE-bench Verified | Real-world GitHub Issues | 41.2% | 49.2% | 74.8% | 70.4% |
| GPQA Diamond | PhD-Level Science Questions | 51.1% | 59.1% | 63.4% | 65.0% |
| MMLU-Pro | Multi-discipline Reasoning | 64.3% | 75.9% | 78.1% | 78.0% |
Beam demonstrates notable strength on SWE-bench Verified (74.8%), driven by its integrated self-correction tokens that identify logic flaws during code generation passes.
Deployment Hardware Configurations
When weights drop later in October 2026, enterprise teams can deploy Beam across standard cloud GPU configurations:
- Full Precision FP8: 8x NVIDIA H100 80GB SXM (Tensor Parallelism = 8).
- Quantized 4-Bit AWQ: 4x NVIDIA A100 80GB PCIe or 8x NVIDIA RTX 6000 Ada (48GB).
- vLLM Integration: Native PagedAttention kernel support will ship on day one, including pre-compiled CUDA wheels.