| Quality |
| 0 |
| 1 |
| Ecosystem | 1 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Starting Price | — | $10/mo |
| Capabilities | 9 decomposed | 13 decomposed |
| Times Matched | 0 | 0 |
Exposes travel hacking data (award flight availability, points valuations, redemption opportunities) through the Model Context Protocol (MCP) server interface, enabling Claude and other AI agents to query and reason over real-time travel award information without direct API calls. Implements MCP resource and tool schemas to standardize access to heterogeneous travel data sources (airline loyalty programs, award flight databases, points marketplaces).
Unique: Implements MCP protocol specifically for travel hacking domain, standardizing how AI agents access fragmented award flight and points data across multiple loyalty programs and third-party aggregators through a single server interface
vs alternatives: Enables Claude and other MCP-compatible AI agents to natively query travel data without custom API wrappers, whereas most travel tools require direct integration or manual data entry
Provides drop-in Python skills and utilities that Claude Code can directly invoke to analyze award flight options, calculate points-per-mile valuations, and recommend optimal redemption strategies. Skills encapsulate domain logic for comparing cabin classes, routing options, and award availability across multiple airlines, allowing Claude to reason over travel hacking decisions with pre-built domain expertise.
Unique: Packages travel hacking domain logic as reusable Claude Code skills that leverage Claude's reasoning capabilities to synthesize award flight options across multiple airlines and loyalty programs, rather than exposing raw data APIs
vs alternatives: Tighter integration with Claude Code's native reasoning than generic travel APIs, enabling Claude to explain trade-offs and multi-leg strategies without additional orchestration logic
Provides travel hacking skills compatible with OpenCode's skill system, allowing OpenCode agents to access award flight data, points valuations, and redemption logic through OpenCode's native skill invocation mechanism. Implements OpenCode skill schema and lifecycle hooks to enable seamless skill discovery, parameter validation, and result formatting within OpenCode workflows.
Unique: Implements travel hacking logic as portable OpenCode skills that work across different OpenCode agent implementations, enabling code reuse and standardized interfaces for travel domain capabilities
vs alternatives: Provides OpenCode-native skill format vs requiring custom wrapper code, reducing integration friction for OpenCode-based teams
Aggregates real-time or near-real-time award flight availability data from multiple airline loyalty programs (United, American, Delta, etc.) into a unified query interface, normalizing different airline award charts, fuel surcharge policies, and availability calendars into comparable data structures. Uses airline API integrations or web scraping to fetch current inventory and presents results ranked by points efficiency and routing optimality.
Unique: Normalizes heterogeneous airline award chart formats and availability APIs into a unified query interface with consistent ranking logic, handling airline-specific quirks (fuel surcharges, fuel surcharge exemptions, award chart variations) transparently
vs alternatives: Aggregates multiple airlines in single query vs requiring separate searches on each airline website; handles fuel surcharge variations that generic flight search engines ignore
Calculates dynamic points valuations for different loyalty program currencies based on redemption opportunities, historical pricing, and market data. Implements algorithms to recommend optimal redemption strategies by comparing points-per-mile efficiency across different routes, cabin classes, and airlines, accounting for award chart variations and fuel surcharge policies. Provides valuation metrics that help users decide between cash and points payments.
Unique: Implements multi-dimensional valuation accounting for airline-specific award chart variations, fuel surcharges, and dynamic pricing rather than simple cents-per-point calculations, enabling context-aware redemption recommendations
vs alternatives: More sophisticated than static valuation tools by incorporating fuel surcharge variations and route-specific award chart differences; enables AI agents to reason about redemption trade-offs
Integrates with airline and hotel loyalty program accounts to fetch real-time points/miles balances, elite status, and account details. Implements secure credential storage and OAuth/API authentication to loyalty programs, enabling automated balance monitoring and integration with award flight search workflows. Tracks balance changes over time to detect earning opportunities and expiration risks.
Unique: Implements secure multi-program loyalty account aggregation with real-time balance fetching, enabling AI agents to make redemption recommendations based on actual account balances rather than user-provided estimates
vs alternatives: Provides real-time account data vs requiring manual balance entry; integrates directly with loyalty programs vs relying on third-party aggregation services
Analyzes complex multi-leg award trips to optimize routing, minimize points cost, and maximize value. Implements graph-based routing algorithms to find efficient connections across multiple airlines and loyalty programs, accounting for award chart variations, fuel surcharges, and stopover policies. Recommends itineraries that balance points efficiency with schedule preferences and routing flexibility.
Unique: Implements graph-based multi-leg routing that accounts for airline-specific stopover and open-jaw policies, award chart variations, and fuel surcharges across different carriers, enabling complex trip optimization that single-airline tools cannot handle
vs alternatives: Optimizes across multiple airlines and loyalty programs vs single-airline tools; accounts for stopover policies and award chart variations that generic flight search engines ignore
Monitors airline award charts, fuel surcharge policies, and loyalty program rules for changes, automatically detecting updates and alerting users to changes that affect redemption value. Implements periodic scraping or API polling of airline websites to detect award chart modifications, fuel surcharge adjustments, and policy changes, comparing against historical snapshots to identify deltas.
Unique: Implements automated award chart change detection with historical comparison and impact analysis, enabling proactive notification of policy changes that affect redemption value rather than reactive discovery
vs alternatives: Automated change detection vs manual monitoring of airline websites; provides impact analysis vs raw change notifications
+1 more capabilities
Generates single-line and multi-line code suggestions in real-time as developers type, using semantic indexing of the entire codebase to retrieve relevant type definitions, function signatures, and contextual patterns. The system maintains a 1M token context window (Pro/Team tiers) that enables suggestions informed by distant code definitions and cross-file dependencies, constructed via local codebase semantic search rather than simple token-based recency. Suggestions adapt to detected coding style on Pro/Team tiers through implicit pattern learning from recent edits.
Unique: 1M token context window with codebase-wide semantic indexing enables suggestions informed by distant code definitions and cross-file patterns, versus competitors (Copilot, Tabnine) that typically use fixed context windows (4K-32K tokens) or file-local context. Claimed 250ms latency suggests optimized retrieval pipeline, though indexing mechanism and performance at scale remain undisclosed.
vs alternatives: Larger context window than GitHub Copilot (8K-32K tokens) and faster latency than unnamed competitors (250ms vs 783ms claimed), enabling suggestions on large codebases with minimal typing delay; trade-off is cloud dependency and undisclosed free tier limitations.
Provides a separate chat interface supporting multiple LLM backends (GPT-4o, Claude 3.5 Sonnet, GPT-4, others) for conversational code assistance. Users attach files, reference recent edits, and trigger compiler diagnostic uploads; the system generates diffs and applies code changes directly to the editor. Model selection is per-conversation, and $5/month in credits (included in Pro/Team) covers external model API costs; overage pricing is undisclosed. Hotkey-driven workflow enables rapid context switching between inline completion and chat.
Unique: Multi-model chat interface with per-conversation model selection and integrated diff application, combined with compiler diagnostic auto-upload. Unlike Copilot Chat (single model per tier) or standalone ChatGPT, Supermaven Chat unifies multiple LLM backends in a single hotkey-driven workflow with direct editor integration for change application.
Supermaven scores higher at 71/100 vs travel-hacking-toolkit at 35/100. travel-hacking-toolkit leads on ecosystem, while Supermaven is stronger on adoption and quality.
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vs alternatives: Supports multiple LLM backends (GPT-4o, Claude 3.5 Sonnet) in one interface with included credits, whereas GitHub Copilot Chat is single-model per tier and requires separate ChatGPT subscription for model switching; trade-off is credit limits and unknown overage pricing.
Supermaven Chat can automatically upload compiler diagnostic messages (errors, warnings) alongside code context to provide error-aware suggestions and fixes. The mechanism is described as 'automatically uploading your code together with compiler diagnostic messages,' but specific language/compiler support and the upload trigger mechanism are undisclosed. This feature is Chat-only and not available in inline completion.
Unique: Automatic compiler diagnostic upload in Chat for error-aware suggestions, versus competitors (Copilot, Tabnine) that require manual error context or have limited diagnostic integration. Supermaven's approach reduces friction but with undisclosed language/compiler support.
vs alternatives: Automatic diagnostic upload reduces manual context-gathering compared to manual copy-paste; trade-off is undisclosed language support and unclear upload trigger mechanism.
Supermaven offers a 30-day free trial of the Pro tier ($10/month), providing full access to 1M token context window, largest model, style adaptation, and $5/month chat credits. No credit card is required to start the trial (implied), and trial conversion to paid is automatic after 30 days unless cancelled. Trial terms and auto-renewal policy are not explicitly detailed.
Unique: 30-day free trial of Pro tier with full feature access (1M context, largest model, chat credits), versus competitors (Copilot 2-month free trial, Tabnine free tier only) with different trial lengths and feature access. Supermaven's approach is generous but with undisclosed auto-renewal terms.
vs alternatives: Full Pro feature access during trial compared to limited free tier; trade-off is undisclosed auto-renewal policy and potential unexpected charges if not cancelled.
Supermaven requires internet connectivity and server-side inference; no offline mode or local inference capability is mentioned or available. All code completion requests are sent to Supermaven's backend servers for processing, and responses are returned over the network. This creates a hard dependency on network connectivity and Supermaven's service availability; if the service is down or network is unavailable, code completion is not available.
Unique: Supermaven has no offline mode or local inference capability; all processing is server-side. GitHub Copilot also requires server-side inference, but Tabnine offers local inference options for some use cases. Supermaven's lack of offline capability is a significant limitation for developers with connectivity constraints.
vs alternatives: Supermaven's server-side-only approach is comparable to GitHub Copilot; Tabnine offers local inference options, making Tabnine more suitable for offline work. Supermaven's lack of offline capability is a weakness vs. Tabnine.
Analyzes recent code edits and inferred coding patterns to adapt inline suggestions to match team conventions, naming patterns, and structural preferences. The mechanism is implicit (not explicit fine-tuning) and operates only on Pro/Team tiers, suggesting pattern learning from editor activity rather than explicit configuration. Free tier uses a single base model without personalization.
Unique: Implicit style adaptation via editor activity analysis without explicit configuration, versus competitors (Copilot, Tabnine) that require manual style guides or explicit fine-tuning. Supermaven's approach is transparent to the user but also non-configurable and undisclosed in mechanism.
vs alternatives: Requires no manual style configuration compared to tools requiring explicit style guides; trade-off is lack of transparency and inability to control or export learned styles.
Delivers code suggestions to the editor inline as the developer types, with a claimed baseline latency of 250ms from keystroke to suggestion display. The system uses a cloud inference backend and local editor plugin to minimize round-trip time. Latency claim is positioned against an unnamed competitor (783ms), but methodology is undisclosed and no independent verification is provided.
Unique: Claimed 250ms latency via optimized cloud inference pipeline and editor plugin architecture, versus competitors with higher latency (783ms unnamed baseline). Actual differentiation is undisclosed; mechanism may involve request batching, model quantization, or edge caching, but specifics are not public.
vs alternatives: Faster than unnamed competitor (250ms vs 783ms claimed); trade-off is cloud dependency and unverified latency claim with no SLA or performance guarantee.
Provides native editor extensions for VS Code, JetBrains IDEs (IntelliJ IDEA, PyCharm, WebStorm, etc.), and Neovim, enabling inline suggestion rendering, hotkey-driven chat access, and compiler diagnostic integration directly within the editor. Each plugin variant is maintained separately and integrates with the editor's native autocomplete UI, keybinding system, and file context APIs.
Unique: Native plugins for three major editor ecosystems (VS Code, JetBrains, Neovim) with integrated chat and diff application, versus competitors (Copilot, Tabnine) that support broader editor ecosystems but with less deep integration in some cases. Supermaven's approach prioritizes depth over breadth.
vs alternatives: Deep integration with VS Code and JetBrains (native autocomplete UI, hotkey system) compared to web-based tools or lighter integrations; trade-off is limited editor support (no Sublime, Vim, Emacs) and undisclosed Neovim support details.
+5 more capabilities