Predictefy vs Codex (2026): Prediction Market Data APIs Compared

Comparison checked October 27, 2026. Codex here refers to the market-data service at codex.io.

Choose the API around your prediction-market workflow

Predictefy fits an application whose main task is comparing event contracts across venues. Its normalized read interface gives your application a consistent market-data starting point, while venue-specific identifiers and capability information remain available for integration decisions.

Matching is included on every plan. Stored indicative differences help with discovery, and the Pro arbitrage feed evaluates candidates against requested depth, venue fees and resolution equivalence. Paper trading provides a way to exercise a strategy's decisions before moving into supported client-signed trading.

Codex can be useful when prediction markets sit inside a wider token or wallet product. The same GraphQL environment covers token data and prediction events, markets, bars and trades. That makes its existing query model relevant to teams already maintaining Codex clients.

Both provide books, so inspect their meaning

Codex's documented predictionOutcomeOrderBooks query returns bids, asks and a venue timestamp for Polymarket and Kalshi outcomes. The documentation says those snapshots can be cached for up to ten seconds. It also specifies how to identify the best bid and ask in the returned arrays.

Those details belong in an integration test. Do not assume the first array element is the best quote, or carry a timestamp through your application without checking its units. Treat a missing snapshot explicitly and keep the original outcome ID available for reconciliation.

With Predictefy, inspect the record's provenance and capabilities alongside the observation time. Native and synthetic books are distinguishable. When the task is estimating a possible fill, choose the appropriate current book and walk the available levels at your intended quantity.

The venue capability reference helps scope that test. Compare the same venue and outcome on each service, then extend the sample across the additional venues required by your application. A successful test on one market does not establish universal coverage.

GraphQL requests and credits need separate budgets

Predictefy's Free evaluation plan includes 25,000 monthly credits. Builder costs $48.88 a month with 250,000 credits, and Pro costs $149 with 2,000,000. Pro includes the arbitrage feed, price-gap feed and history replay. Commercial use requires an Enterprise agreement.

Codex documents both request-count and compute-unit billing, with the applicable model depending on the account. A GraphQL request can contain several root fields, and subscription delivery also contributes to usage. Counting HTTP calls alone can therefore give a misleading estimate.

Build a representative workload: catalog filters, a set of outcome books, historical chart ranges and the subscriptions you would keep open. Price that workload using each account's actual operation costs and limits. Keep the Predictefy credit schedule beside the query list rather than converting one provider's headline allowance into the other's units.

How to evaluate Predictefy alongside a Codex client

Keep the existing GraphQL client while you map a small sample into your application's internal model. Save protocol, native market ID, outcome ID, timestamps and price units. Codex documents protocol-qualified identifiers, so keep an explicit mapping instead of deriving a venue ID by trimming strings.

Next, compare a chart workflow with a book workflow separately. Candle history is useful for research and visualization, while a proposed order needs a current observation at the chosen size. Preserve those purposes in your storage schema so a later component does not silently substitute one for the other.

Run your strategy in paper trading and review its decisions through lifecycle changes such as suspensions and settlement. For a commercial Predictefy product, confirm Enterprise terms before launch. This lets your team evaluate the broader venue integration using its own data model and requirements.