Project page Review 2026: Fresh AI debate platform for rapid LLM testing

📋 Overview

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Imagine you’re a data scientist tasked with choosing the best large language model for a new customer‑support chatbot, and you have to evaluate five candidates across dozens of prompts. In a typical workflow you’d manually copy‑paste prompts, log responses, and then spend hours in a spreadsheet trying to spot trends-a process that is error‑prone, repetitive, and delays product rollout. Project page eliminates that bottleneck by turning the evaluation into an automated, visual debate where each model argues its answer in real time, letting you compare quality, speed, and cost at a glance.

Project page is an open‑source web app built by the research team behind Composable Models, first released in early 2024. The core idea is to treat LLM evaluation as a structured debate: a moderator prompt defines the task, each participant LLM receives the same input, and a final arbiter model scores the arguments. The codebase lives on GitHub and is powered by Streamlit, LangChain, and a lightweight SQLite store, allowing anyone to self‑host or use the hosted version at composable‑models.github.io/llm_debate. The developers emphasize transparency and reproducibility, providing ready‑made templates for common tasks like summarisation, coding assistance, and factual QA.

The platform resonates most with AI product managers, prompt engineers, and research labs that need rapid, repeatable model benchmarking. A typical user might be a Prompt Engineer at a SaaS startup who must decide whether GPT‑4, Claude‑3, or an internal fine‑tuned model should power the next feature. They upload a CSV of 200 real‑world queries, select the models via API keys, and let Project page run the debate in parallel, producing a dashboard that highlights win‑loss ratios, latency, and token cost per model. The workflow fits neatly into CI pipelines: after each model iteration, the team can trigger a new debate run and automatically generate a markdown report for stakeholders.

Project page’s most direct competitors are OpenAI’s ChatGPT Playground (free tier, $0.00; paid Pro $20/mo) and the commercial tool Langfuse (Starter $49/mo, Professional $149/mo). The Playground excels at ad‑hoc prompt testing but lacks structured side‑by‑side comparison and no built‑in scoring. Langfuse offers sophisticated logging, tracing, and evaluation dashboards, but its pricing quickly escalates with higher token volumes and it requires a separate logging backend. Project page wins on flexibility-any LLM with an OpenAI‑compatible endpoint can be dropped in, and the debate logic is fully customisable. For teams that value open‑source control and want to avoid per‑token fees beyond the underlying model costs, Project page remains the preferred choice.

⚡ Key Features

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Debate Engine – The heart of Project page is its Debate Engine, which orchestrates a round‑robin exchange between multiple LLMs under a moderator prompt. This solves the problem of inconsistent evaluation order that skews results. Users define a JSON schema for the moderator, participants, and arbiter, then click ‘Run’. The engine streams each model’s response, stores it, and finally asks the arbiter to score each argument on a 0‑10 scale. In a recent internal test, a team of three engineers used the engine to evaluate 500 customer‑support queries, cutting evaluation time from 12 hours of manual work to under 30 minutes, with a 15 % increase in inter‑rater agreement. The only friction is that the arbiter must be a capable model (e.g., GPT‑4), which adds extra token cost.

Template Library – Project page ships with a curated library of 12 debate templates covering summarisation, code generation, factual verification, and creative writing. Each template includes pre‑written system prompts, example inputs, and scoring rubrics, removing the need for users to craft complex prompts from scratch. For instance, the “Legal Clause Extraction” template helped a compliance analyst process 1,000 contracts in half the time, achieving a 92 % precision versus 78 % with a single‑model approach. The limitation is that the library is static; adding new templates requires editing YAML files, which can be daunting for non‑technical users.

Live Dashboard – After a debate run, the platform renders an interactive dashboard that visualises win‑loss matrices, latency histograms, and token‑cost breakdowns per model. This feature addresses the lack of immediate insight that plagues spreadsheet‑based evaluation. In a pilot with a fintech startup, the dashboard highlighted that Claude‑3 was 0.4 seconds faster per response on average, saving the team an estimated $1,200 per month in compute costs. The dashboard currently does not support export to PowerBI or custom charting, limiting deeper business‑intelligence integration.

API‑First Integration – Project page exposes a RESTful API that lets developers trigger debates programmatically and retrieve results in JSON. This solves the bottleneck of manual UI interaction for CI/CD pipelines. A machine‑learning ops team integrated the API into their GitHub Actions workflow, automatically running a debate after each model fine‑tune and posting a summary comment on the PR. The process shaved 3 days off their release cycle. However, the API lacks granular rate‑limiting controls, so high‑frequency users must self‑host to avoid throttling.

Self‑Hosting & Data Privacy – Because the code is open source, organisations can deploy Project page on private Kubernetes clusters, ensuring that all prompts and model outputs never leave their network. A health‑tech firm leveraged this to comply with HIPAA, running debates on patient‑record summarisation without exposing PHI to external services. The self‑hosted setup reduced per‑token exposure risk but required a dedicated DevOps effort (≈ 2 weeks of work) to configure TLS, secrets management, and persistent storage. The hosted version does not currently offer a GDPR‑compliant data‑deletion API, which can be a compliance hurdle.

🎯 Use Cases

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Prompt Engineer at a mid‑size SaaS (e.g., HubSpot) – Before discovering Project page, the engineer manually tested new prompt variations across three LLMs by copying prompts into separate browser tabs, recording responses in a Google Sheet, and then scoring them subjectively. The process took roughly 2 hours per batch of 50 prompts and produced inconsistent scores. After adopting Project page, the engineer uploads a CSV of 200 real‑world tickets, selects GPT‑4, Claude‑3, and a fine‑tuned Llama 2 model, and runs a debate. The dashboard instantly shows that GPT‑4 wins 62 % of the time with a 0.3 second latency advantage, leading the team to choose it for the next release, saving an estimated $3,500 in token costs per month.

Compliance Analyst at a financial institution – The analyst previously spent weeks extracting regulatory clauses from contracts using a single LLM, then manually verifying each extraction, resulting in a 78 % accuracy rate and high labor costs. With Project page’s “Legal Clause Extraction” template, the analyst runs a debate between an internal fine‑tuned model and an external Claude‑3 instance. The arbiter scores each extraction, and the dashboard reveals a 92 % precision for the internal model, cutting verification time from 40 hours to 6 hours per batch of 1,000 contracts. The measurable outcome is a 85 % reduction in manual review effort and a $4,800 quarterly cost saving.

Product Manager at an e‑commerce platform – The manager needed to decide which LLM should generate product descriptions for 10,000 SKUs. Previously, the team ran a blind A/B test on the website, which required weeks of traffic collection and introduced inconsistent user experiences. Using Project page, the manager set up a debate where GPT‑4, Gemini‑1.5, and a proprietary model each produced a description, while a GPT‑4 arbiter scored readability and SEO relevance. The results showed Gemini‑1.5 outperformed on SEO metrics by 12 % while maintaining comparable readability, allowing the manager to roll out the new model with confidence and increase organic traffic by 4.3 % within the first month.

⚠️ Limitations

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Scalability of Debate Length – When debates involve long‑form generation (e.g., 2,000‑token essays), the platform can hit the OpenAI token limit for the arbiter model, causing incomplete scoring. This technical limitation forces users to truncate inputs or split debates, which reduces the natural flow of argumentation. Competitor Claude 3 Playground (included in Anthropic’s $20/mo plan) handles longer context windows (up to 100k tokens) more gracefully, making it a better choice for extensive document analysis.

Limited Real‑Time Collaboration – Project page’s UI is single‑user focused; there is no built‑in collaborative editing or comment threading. Teams that need multiple stakeholders to review debate outcomes simultaneously must export the results and use external tools like Google Docs. In contrast, Langfuse’s shared workspaces (Starter $49/mo) provide real‑time comment threads, versioning, and role‑based permissions, which is essential for distributed research teams. If collaborative review is a priority, Langfuse is the superior option.

Pricing Transparency for Self‑Hosted Deployments – While the hosted version is free, the self‑hosted option incurs hidden infrastructure costs (cloud compute, storage, TLS certificates). The documentation does not provide a clear cost calculator, leaving enterprises to guess expenses. Competitor OpenAI’s ChatGPT Enterprise ($20 per active user per month) bundles hosting, SLA, and support, offering a predictable bill. Organizations with limited DevOps resources may prefer the enterprise SaaS model over Project page’s ambiguous self‑hosting economics.

💰 Pricing & Value

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The hosted version of Project page offers three tiers: Free (unlimited debates, up to 10,000 tokens per month, community support only), Pro ($29/mo or $299/yr) which raises the token cap to 100,000, adds priority email support, and enables custom arbiter models, and Enterprise (custom pricing, unlimited tokens, dedicated account manager, SSO, on‑premise deployment assistance, and 24/7 SLAs). All tiers are billed monthly by default, with a 15 % discount for annual commitments.

While the core platform is free, there are hidden costs that can add up. Each token consumed by the underlying LLMs is billed at the provider’s rate (e.g., $0.03 per 1,000 tokens for GPT‑4). The Pro tier includes a $5 overage buffer; beyond that, you are charged $0.01 per additional 1,000 tokens. If you enable the optional “Data Residency” add‑on for EU‑hosted storage, there is an extra $10/mo. Additionally, the API rate‑limit is 60 requests per minute on Free and 300 on Pro; exceeding it triggers a $0.02 per extra request surcharge.

When compared to Langfuse’s Starter plan ($49/mo) and OpenAI’s ChatGPT Enterprise ($20 per user/mo), Project page’s Pro tier delivers the best raw value for teams that already pay for model usage. For a typical user running 80,000 tokens per month, the Pro tier costs $29 plus the underlying model fees, whereas Langfuse would be $49 plus its own token tracking fees, and Enterprise would be $20 × 5 users = $100 with no token‑level transparency. Therefore, the Pro tier offers the most cost‑effective blend of flexibility, open‑source control, and support for most small‑to‑mid‑size AI teams.

✅ Verdict

Buy Project page if you are a Prompt Engineer, AI Product Manager, or Research Scientist who needs a reproducible, side‑by‑side LLM evaluation framework, have a modest budget (under $50/mo), and are comfortable managing API keys for multiple providers. The free tier is sufficient for occasional benchmarking, while the $29 Pro plan unlocks higher token limits and priority support-perfect for teams that run weekly debates on hundreds of prompts and need an exportable dashboard for stakeholder reporting.

Skip Project page if you require deep collaborative review, enterprise‑grade SLAs, or built‑in long‑context handling. In those scenarios, Langfuse (Starter $49/mo) offers real‑time commenting and unlimited context, while OpenAI’s ChatGPT Enterprise ($20 per user/mo) provides a fully managed, SLA‑backed experience with predictable pricing. The single improvement that would make Project page a clear market leader is native support for multi‑user workspaces with granular permission controls, eliminating the need for external export workflows.