目錄
Reflection Beam is an open-weight large language model developed by Reflection AI, featuring approximately 501.3 billion total parameters, 23.4 billion active parameters, and a context window of up to 1 million tokens. It is primarily designed for software development, complex reasoning, and multi-step AI agent tasks. After the model weights were officially released on October 16, 2026, developers could not only access Beam through an API but also deploy it themselves, customize their inference environments, or integrate it into existing Coding Agent tools.
Beam has attracted attention not only for its open weights and large-scale mixture-of-experts (MoE) architecture, but also for its attempt to reduce the computational resources AI agents need to complete complex tasks. However, third-party tests indicate that Beam still has significant limitations in software development, and using fewer reasoning tokens does not necessarily translate into lower real-world costs. For teams planning to integrate AI with GitHub, Notion, or internal enterprise systems, model performance is only one consideration. Deployment costs, data permissions, and task acceptance criteria also affect the final outcome.
What Is Reflection Beam? Reflection AI Officially Releases Its Open-Weight Model
Reflection Beam is the first open-weight large language model developed by Reflection AI, a US-based AI company. It uses a sparse mixture-of-experts (MoE) architecture and is designed primarily for software engineering, tool calling, and long-running agent workflows.
Reflection AI first announced Beam on October 5, 2026, initially introducing its model architecture, training methods, and preliminary performance. Following additional safety testing and evaluation, the company officially released the Beam 1.0 weights on October 16, along with model documentation, a safety technical report, and developer tools. Platforms including AWS, Google Cloud, and Hugging Face also provided deployment or integration support around the release.
The biggest practical difference between Beam and conventional AI chat tools is that Beam is specifically designed for workflows involving repeated tool use. For example, fixing a software bug may require more than a single AI response. The AI needs to read project files, run tests, interpret error messages, and determine what to do next based on the results. Beam has been trained for these types of tasks, although actual execution still requires a Coding Agent framework and external tools.
How Is Reflection Beam's Open-Weight Model Different from Conventional Open-Source AI?
Beam is released under the Apache 2.0 license, allowing developers to download, modify, redistribute, and commercially use its model weights, provided they comply with the license terms. Compared with closed models that can only be accessed through a provider's API, open weights allow companies to choose where their models run, which inference frameworks they use, and how their hardware is configured. They can also fine-tune or customize the model to meet internal requirements.
However, open weights do not mean the entire model development process has been made public. Reflection AI provides model weights, architectural information, and some details about its training methods, but it has not released the complete training dataset, data filtering procedures, or every aspect of its internal research and development. External researchers therefore cannot fully reproduce the original training process using only the publicly available files.
For enterprises, the main difference lies in the degree of control available. Open weights allow organizations to manage their own model execution environments and reduce their dependence on a single API provider. However, whether the training process can be fully audited and whether a deployed model meets information security requirements still need to be evaluated separately. These questions cannot be answered based solely on the license name.
Reflection Beam Model Specifications: 501B Parameters, MoE Architecture, and a 1 Million Token Context Window
Beam is an exceptionally large model, but it does not use all its parameters every time it generates text. Instead, its MoE architecture selects certain experts to participate in computation. This design allows the model to maintain a large parameter capacity while reducing some of the computation required to process each token. It is also the main technical foundation behind Reflection AI's emphasis on inference efficiency.
According to Reflection AI's official Model Card, the main specifications of Beam 1.0 are as follows.
| Specification | Reflection Beam 1.0 |
| Developer | Reflection AI |
| Model architecture | Sparse Mixture-of-Experts (MoE) |
| Total parameters | 501.3B, approximately 501.3 billion |
| Active parameters | 23.4B, approximately 23.4 billion |
| Number of experts | 256 |
| Model layers | 52 |
| Context length | 1 million tokens |
| Input and output | Text |
| Weight formats | BF16, FP8, NVFP4 |
| Model license | Apache 2.0 |
| Knowledge cutoff | June 30, 2026 |
| Primary development language | English |
These figures provide an initial understanding of the model's architecture, but they do not directly indicate its actual response quality or deployment costs. For example, two models with similar total parameter counts may have very different hardware requirements and execution speeds because of differences in expert routing, attention mechanisms, quantization formats, and inference frameworks.
Why Don't 501B Total Parameters and 23B Active Parameters Mean Beam Can Run on an Ordinary Computer?
Beam's 501.3B total parameters are distributed across different model layers and expert networks. When processing each token, only approximately 23.4B parameters participate in computation. This avoids using the computational capacity of the entire model for every inference operation. However, the remaining expert weights still need to be stored in memory or made accessible through an appropriate loading mechanism. As a result, the hardware requirements do not simply shrink to those of a conventional 23B model.
With BF16 weights, each parameter requires approximately 2 bytes. This means the raw weight data for 501.3B parameters approaches 1 TB, excluding the KV Cache, execution framework, and other memory overhead. Even when FP8 or NVFP4 is used to compress the weights, GPU memory capacity, inter-device communication, memory bandwidth, and compatibility with quantized execution environments must still be considered.
The main differences among the BF16, FP8, and NVFP4 formats provided by Reflection AI are weight precision and resource requirements. BF16 weights occupy more space, while FP8 reduces storage and memory demands. NVFP4 goes further by representing some weights at lower precision, making it suitable for exploring deployment options on hardware that supports the format.
However, higher precision does not guarantee the best performance on every benchmark. In Beam's official SWE-bench Verified results, BF16, FP8, and NVFP4 scored 81.6%, 80.2%, and 80.7%, respectively. Although the three formats produced different results, these differences are not sufficient to establish a definitive quality ranking across all real-world workloads. Enterprises considering long-term deployment should compare the accuracy, response speed, and overall resource consumption of different formats using their own internal tasks.
Reflection Beam Supports 1 Million Tokens, but Can It Really Understand an Entire Software Project?
A 1 million token context window allows Beam to receive large amounts of source code, technical documentation, and tool execution logs in a single input. This is particularly attractive for Coding Agents that need to analyze multiple files. For example, investigating an API malfunction may require an agent to examine service code, test files, error logs, and related specifications simultaneously. A longer context window reduces the likelihood that information must be discarded because of capacity limitations.
However, the amount of text a model can receive and its ability to accurately locate relevant information are two different things. When the context contains multiple versions of technical documents, duplicate error logs, or outdated specifications, the model may still reference incorrect information. In addition, Coding Agents typically add new results to their conversation histories after each tool execution, continuously increasing the amount of data that subsequent inference operations must process.
For large codebases, sending every file directly to the model is usually not the most efficient approach. Using code search, file indexing, or retrieval systems to identify relevant modules first, then supplying only the necessary code and test logs, can reduce interference from irrelevant information. This approach also makes it easier to trace why an AI system made a particular modification.
Furthermore, Beam currently supports text input and text output only and was developed primarily using English-language data. When working with Traditional Chinese requirements documents, mixed-language code comments, or image-based design specifications, it should not be assumed that Beam offers the same capabilities as it does for English text tasks.
What Can the Reflection Beam Coding Agent Do? How Mirror CLI Executes Software Development Tasks
The main difference between a Coding Agent and a conventional code generation tool is whether it can continue working based on execution results. A typical AI tool might generate a piece of code after receiving a request. A Coding Agent, by contrast, can use external tools to search files, modify code, run tests, and decide whether further adjustments are needed based on the test results.
Mirror CLI, released alongside Beam by Reflection AI, is a Coding Agent execution tool designed for the model. Mirror operates through the terminal and manages model conversations, tool calls, and work sessions. It also supports MCP integration and parallel workflows using different Git Worktrees. Developers can inspect the agent's actions, approve tool calls, and intervene when necessary.
Mirror's architecture separates the developer tool interface from the agent execution core. Its Python frontend manages user interactions, prompts, and extension tools, while a Rust core coordinates the model and external operations. This design helps separate model reasoning from actual tool execution and makes it easier for development teams to establish testing environments for different types of work.
However, whether a Coding Agent can complete a task still depends on the interaction between the model, execution framework, and tool permissions. Even if Beam correctly identifies a software bug, the agent may still be unable to deliver a verifiable result if it cannot run the necessary tests or lacks permission to read relevant files.
How Good Is Reflection Beam's Performance? Differences Between Official Benchmarks and Third-Party Evaluations
When releasing the model, Reflection AI published several performance tests covering software engineering, terminal operations, mathematical reasoning, and tool calling. Official figures indicate that Beam is competitive on certain Coding Agent tasks, but results from third-party evaluation platforms are not entirely consistent. It is therefore necessary to examine the evaluation methodology before deciding what the scores actually represent.
What Are Beam's Official SWE-bench and Terminal-Bench Scores?
Reflection AI's Model Card reports benchmark results for models at different precision levels. For the BF16 model, SWE-bench Verified reached 81.6%, SWE-bench Pro v2-Hard scored 76.5%, and Terminal-Bench v2.1 reached 80.1%. These results indicate that Beam can complete a substantial proportion of software repair and terminal tasks within the testing environments used by Reflection AI.
However, Coding Agent benchmarks are generally influenced by execution frameworks, tool configurations, retry counts, and scoring rules. Achieving 80% on a particular benchmark does not mean that connecting Beam to an enterprise codebase will give it an 80% chance of successfully fixing a bug on the first attempt.
Artificial Analysis Testing: Beam Performs Well in Automation but Still Has Coding Agent Limitations
On October 16, 2026, independent evaluation platform Artificial Analysis published its test results for Beam. At the time, Beam scored 30 on the Artificial Analysis Intelligence Index, ranking second among American open-weight models. However, its performance varied significantly across different task categories.
| Benchmark | Beam Result | Source |
| SWE-bench Verified | 81.6% | Reflection AI official results |
| SWE-bench Pro v2-Hard | 76.5% | Reflection AI official results |
| Terminal-Bench v2.1 | 80.1% | Reflection AI official results |
| Artificial Analysis Intelligence Index | 30 points | Artificial Analysis |
| AutomationBench-AA | 48.5% | Artificial Analysis |
| Terminal-Bench 4.0 | 3.0% | Artificial Analysis |
| Coding Agent Index (Mirror) | 28 points | Artificial Analysis |
An important distinction is that the official Terminal-Bench v2.1 and the third-party Terminal-Bench 4.0 are not the same test. Their tasks, execution environments, and scoring conditions may differ. Therefore, the 80.1% and 3.0% scores cannot be directly compared as measurements of the same capability under different conditions.
The third-party results show that Beam scored 48.5% on AutomationBench-AA, suggesting it has a certain level of capability in some automated workflows. However, it achieved just 3.0% on Terminal-Bench 4.0, indicating that its performance remained weak on terminal tasks under that benchmark's conditions. Its Coding Agent Index score with Mirror was also only 28 points, leaving a gap between Beam and some of the leading agent configurations at the time.
Another test worth examining is AA-Omniscience. Beam answered approximately 33% of the questions correctly in this knowledge evaluation, but its hallucination rate reached 47%. This indicates that even a model with relatively strong reasoning capabilities may generate incorrect information in situations where a definite answer is unavailable. These results cannot be directly applied to software repair success rates, but they highlight the continued need for external verification when enterprises use Beam to process technical documentation, research materials, or automated decisions.
Plotly Data Analytics Testing: Why Does Beam Cost More Than a Model With the Same Score?
High inference efficiency does not necessarily mean a lower cost to complete an entire task. On October 17, 2026, Plotly published the results of testing Beam 1.0 on DataAnalyticsBench. The evaluation used the same agent tools and data environment to ask different models 43 data analysis questions.
Beam answered 36 questions correctly, achieving a pass rate of approximately 84%, matching DeepSeek V4.1 Flash. However, the costs of the two models differed substantially.
| Comparison | Reflection Beam 1.0 | DeepSeek V4.1 Flash |
| Correct answers | 36/43 | 36/43 |
| Pass rate | Approximately 84% | Approximately 84% |
| Total test cost | US$8.29 | US$0.46 |
| Average model calls per question | Approximately 15 | Approximately 7 |
Plotly's analysis noted that Beam required more model calls during the test and that its hosting provider at the time made almost no effective use of input caching. Every time the agent continued working, it had to resend a large amount of conversation history, causing input token costs to accumulate. Only approximately 0.4% of Beam's input was cached, significantly increasing the total cost of the test.
These findings reflect the performance of a specific hosting service and agent configuration in October 2026. They do not establish that Beam is more expensive in every deployment environment. If an inference service can effectively reuse the KV Cache and reduce repeated context processing, actual costs may change.
Nevertheless, this example is useful for organizations running long-lived AI agents. Enterprises should compare more than the price per million tokens. They also need to record how many times an agent calls the model to complete a task, how much content it processes repeatedly, and whether errors require additional execution attempts.
How to Deploy Reflection Beam: Comparing AWS SageMaker, Google Cloud, and Self-Hosting
Because Beam uses an open-weight license, it can be accessed through APIs provided by different service providers or deployed in GPU environments managed directly by enterprises. Both AWS and Google Cloud announced Beam support on October 16, 2026. However, their tools and management approaches differ, and they are not necessarily suited to teams of the same size.
What Is the Difference Between AWS SageMaker JumpStart and HyperPod?
One option provided by AWS is to create a managed real-time inference endpoint using Amazon SageMaker JumpStart. JumpStart reduces some of the initial setup work required for model deployment, making it suitable for teams that want to test Beam's capabilities, evaluate API integration, and monitor inference costs before making a larger commitment.
Organizations requiring more detailed control over Kubernetes, GPU resources, and inference frameworks can consider Amazon SageMaker HyperPod Inference, combined with tools such as SGLang and vLLM to customize the execution environment. This type of configuration offers greater control, but enterprises must also commit engineering resources to capacity planning, monitoring, troubleshooting, and version updates.
AWS also announced Beam support for Trainium3, giving enterprises another hardware deployment option beyond GPUs. However, hardware support does not automatically translate into cost savings. Organizations should compare the available execution environments, request volumes, latency requirements, and total billing costs.
At the time of release, the Beam service on AWS SageMaker JumpStart was initially available in US East (Northern Virginia) and US East (Ohio). Taiwanese enterprises planning to use these regions to process internal source code or customer data need to review their data governance policies, cross-region data transfer requirements, and relevant service configurations. Deploying the model within their own AWS account does not automatically guarantee that all data remains in a designated region.
Deploying Beam on Google Cloud: What Does Up to 5x Better Performance Actually Mean?
Google Cloud also provided deployment support on the day Beam's weights were released. Working with Reflection AI, it optimized inference components including llm-d, vLLM, NVIDIA Dynamo, and SGLang.
Google Cloud reported that, under specified hardware and testing conditions, its optimized configuration could deliver up to 5 times the throughput and up to an 8-fold improvement in latency. These figures primarily reflect improvements in the system efficiency of inference services. They do not mean Beam's answer accuracy has increased, nor do they guarantee the same results across all hardware configurations.
Such optimizations matter because Coding Agents typically make repeated model calls over multiple rounds. As tool logs, code differences, and conversation histories grow, inference services must repeatedly process large amounts of context. If each request is assigned to a different execution node, previously computed KV Cache data may not be effectively reused, increasing latency and GPU workloads.
Google Cloud's approach uses cache-aware routing, memory tiering, and inference workload distribution to reduce unnecessary repeated computation. For enterprises running AI agents frequently, these infrastructure configurations may have a greater effect on long-term costs than simply increasing model output speed.
Which of the Three Ways to Use Reflection Beam Is Most Suitable?
| Deployment Method | Suitable Users | Main Advantages | Costs to Consider |
| Hosted API | Individual developers and small teams | No need to manage GPUs directly | API charges, service limitations, and data processing policies |
| Cloud-managed endpoint | Enterprise testing and internal application development | Reduces some deployment work | Endpoint running costs, idle resources, and regional restrictions |
| Self-managed model | Enterprises with AI platform engineering capabilities | Control over model versions, hardware, and data environments | GPUs, maintenance, security monitoring, and scaling costs |
If the goal is simply to test Beam's code repair capabilities, a hosted API or managed endpoint is generally an easier way to obtain initial performance data. For organizations that need to process sensitive source code over the long term, restrict where data can be transferred, or customize the model, self-hosting may offer greater flexibility and control. However, this assumes that the organization has sufficient engineering expertise and a sustained workload.
Is Reflection Beam Safe? Coding Agent Tool Permissions and Prompt Injection Risks
In its Beam Safety Technical Report, Reflection AI published the results of its safety training and risk assessments, including tests involving indirect prompt injection. This type of attack hides malicious instructions in materials that an AI agent is about to read, such as web pages, emails, project documents, or tool responses, attempting to redirect the model away from its original task.
Reflection AI evaluated Beam using the Mirror execution framework in two sets of internal tests. Without specific defensive prompts, the attack success rates were 3.1% and 3.6%, respectively. After defensive strategies were introduced, the measured success rates decreased to 0.3% and 0%.
These results indicate that the protective measures were effective in the specified tests. However, the tests used fixed attack methods and cannot represent every risk encountered in real-world environments. In particular, when attackers can repeatedly modify their content, or when agents have access to private data and the ability to transmit information externally, simply instructing an AI model to ignore malicious commands is not sufficient protection.
For Coding Agents, the real priority is managing what their tools are permitted to do. For example, allowing an agent to read a code repository does not mean it should also be allowed to obtain production database credentials. Similarly, permission to create Git branches does not imply that the agent should have direct authority to deploy changes to production. Assigning different operations to isolated environments and requiring independent authorization for high-risk actions can reduce the practical consequences of incorrect model decisions.
Open weights do not automatically solve these problems either. Even if Beam runs entirely on servers managed by an enterprise, the organization still needs to review the entire data processing workflow if the agent calls external APIs, writes to cloud databases, or transmits execution logs.
How to Integrate Reflection Beam With Notion: Building Verifiable AI Agent Workflows
For teams using Notion to manage requirements documents, development progress, and project records, Reflection Beam can serve as a model option for an external Coding Agent. This integration does not mean that Beam is natively built into Notion. Instead, it uses the Notion API, MCP, or a custom middleware service to allow an authorized AI agent to read task information and write execution results back to a designated database.
A more suitable design is to use Notion to store requirements, acceptance criteria, and decision records, while handling code modifications in a separate Coding Agent execution environment. For example, if a website experiences a member login error, a Notion task could first record the error scenario, relevant code modules, expected behavior, and data that must not be modified. After receiving the task, the agent can modify code in an isolated Git branch and run tests. Once the work is complete, it submits the test results and code differences for review by the responsible team member.
This workflow requires data fields and permissions to be designed in advance. Simply telling an AI agent to "fix the website" is not enough to expect it to determine every operational boundary independently. The more specific the task description, the easier it becomes to verify that the agent modified the correct areas and trace the cause if the results do not meet expectations.
Differences Between Notion API and MCP Permissions
Both the Notion API and the official Notion MCP provide data integration capabilities, but their authorization mechanisms are not identical.
When creating an internal integration through the Notion API, organizations can configure the operations the integration is allowed to perform and limit access to authorized pages or databases. This makes it suitable for workflows requiring clearly defined automation boundaries. The official Notion MCP, by contrast, operates according to the existing Notion permissions of the connected user. As a result, the scope available to AI tools may also include other information that the user's account can access.
This means enterprises should verify the permissions of a connected account before enabling MCP. If an account can read large amounts of sensitive documentation, relying solely on prompts that instruct the agent not to access those materials is insufficient. A more cautious approach is to use a dedicated connection with restricted permissions or introduce additional limits on tool calls and accessible data through middleware.
Furthermore, Notion task statuses should not be determined entirely by the agent. AI can report which files were modified, the test results, and execution logs. However, decisions about whether to merge code, update production systems, or close a task should still be based on predefined acceptance criteria.
Is Reflection Beam Worth Adopting? Compare Task Success Rates, Costs, and Maintenance Requirements First
Reflection Beam now provides officially released model weights and multiple deployment options, making it a reasonable candidate for organizations evaluating open-weight Coding Agents. However, current third-party test results do not provide sufficient evidence to support enterprises immediately replacing their existing software development tools across the board. Evaluations of Beam vary considerably between tests, particularly when tasks involve terminal operations, repeated bug fixing, or long-running agent workflows. Performance should therefore be measured against actual workloads.
During initial adoption, organizations can begin with small tasks that are easy to reverse and verify, such as adding unit tests, fixing bugs in non-production environments, or organizing technical documentation. During testing, codebase versions, tool permissions, and acceptance criteria should remain as consistent as possible before comparing Beam with existing AI tools. This helps avoid mistaking differences caused by execution frameworks for differences in the models' capabilities.
When evaluating costs, enterprises should account for more than API or GPU expenses. Agent retry counts, average task completion time, human review hours, and the cost of correcting errors also need to be included. Plotly's testing has already demonstrated that even when a model has good inference efficiency, insufficient context caching and excessive tool calls can make completing an entire task more expensive than using another model.
For large enterprises, Beam's value may come from greater control over deployment locations, model versions, and infrastructure. For individual developers or small studios, however, hosted services may be more practical if their workloads are insufficient to justify the fixed maintenance costs of operating such a large model.
As of October 22, 2026, Reflection Beam offers open weights, a 1 million token context window, and support for deployment across multiple platforms. Nevertheless, differences in benchmark results show that it is not yet the best choice for every Coding Agent task. Organizations planning to integrate Beam with Notion, GitHub, or other automation systems should first establish data access boundaries and acceptance procedures, then compare success rates, processing times, and total costs on identical tasks. Decisions about expanding its use should be made only after obtaining real-world test results.
FAQs
Reflection Beam is an open-weight large language model developed by Reflection AI, primarily designed for software development, reasoning, and multi-step AI agent tasks. The weights for Beam 1.0 were officially released on October 16, 2026.
Approximately 501.3B refers to the model's total parameters, while around 23.4B parameters are activated when processing each token. The MoE architecture reduces some computational requirements, but deployment still involves managing extensive expert weights and GPU memory.
Not necessarily. Context capacity does not guarantee retrieval or reasoning accuracy. Relevant source files, test results, and requirements documents should still be selected in advance to prevent outdated or duplicate information from interfering with the model's analysis.
Beam's weights are licensed under Apache 2.0, allowing commercial use, modification, and redistribution in accordance with the license terms. However, API access, GPUs, storage, inference, and maintenance services may incur separate charges.
Beam is the model that provides reasoning capabilities, while Mirror CLI is a Coding Agent framework that coordinates terminal operations, files, and external tools. Whether it can successfully repair software still depends on tool configurations, permissions, and testing environments.
The official evaluation used Terminal-Bench v2.1, while Artificial Analysis used Terminal-Bench 4.0. Because the benchmark versions and execution conditions differ, the 80.1% and 3.0% scores cannot be directly compared or used to predict success rates on enterprise tasks.
Completing an agent task may require multiple model calls and repeated processing of context. Plotly's testing showed that insufficient caching and higher call counts can increase total costs compared with other models that answer the same number of questions correctly.
SageMaker JumpStart can be used to create a managed real-time inference endpoint. Teams requiring greater control over infrastructure can explore SageMaker HyperPod Inference combined with SGLang or vLLM.
No. Google Cloud reported improved throughput under specific hardware conditions and an optimized inference architecture. The improvements primarily relate to routing, KV Cache reuse, and service efficiency, rather than increased accuracy in model responses.
No. The integration discussed in this article involves connecting an external Coding Agent through the Notion API, MCP, or a custom service. Notion stores task information and acceptance records, while the agent performs work in an isolated environment.
Internal Notion API integrations can restrict authorized content and permitted operations. The official MCP operates according to the connected user's existing Notion permissions, so organizations need to verify the account's actual access scope.
The main risks include prompt injection, unauthorized tool operations, and unintended code modifications. Code operations should be restricted to reversible environments, while production deployments, deletions, and writes involving sensitive data should require additional authorization.
Not necessarily. Beam's large model weights require substantial hardware resources and maintenance capabilities. Small teams should first test real tasks using a hosted API or managed endpoint, then compare success rates, human review time, and total costs before deciding whether to self-host.