| 1 |
| Ecosystem | 1 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Starting Price | — | $10/mo |
| Capabilities | 5 decomposed | 13 decomposed |
| Times Matched | 0 | 0 |
Intercepts and surgically trims verbose MCP tool responses before they reach Claude by applying configurable depth-based filtering rules. Uses a hook-based architecture that wraps the MCP protocol layer, analyzing response payloads and selectively removing nested fields, array elements, or entire subtrees based on user-defined thresholds. This prevents token waste from bloated tool outputs without modifying the underlying tool implementations.
Unique: Implements a transparent MCP protocol hook that trims responses at the transport layer before Claude ingests them, using depth-based heuristics rather than semantic analysis. This is distinct from post-processing because it operates at the MCP boundary and prevents tokens from being counted in the first place.
vs alternatives: More surgical than naive response truncation because it preserves response structure while selectively removing subtrees, and more transparent than modifying tool code because it works as a drop-in middleware layer.
Automatically caps file read operations from MCP file-system tools to a maximum byte threshold, preventing oversized file reads from consuming excessive tokens. Intercepts file read requests before execution and either truncates the read size or returns a partial file with metadata indicating truncation. Works transparently within the MCP hook layer without requiring changes to file-reading tool implementations.
Unique: Operates at the MCP request layer to preemptively clamp file reads before they execute, rather than post-processing results. This prevents unnecessary I/O and token consumption at the source, using a configurable byte threshold that applies uniformly across all file read operations.
vs alternatives: More efficient than post-truncation because it prevents the full file from being read from disk and transmitted; more flexible than hard-coded limits because thresholds are configurable per deployment.
Provides a middleware layer that transparently intercepts MCP protocol messages at the request and response boundaries, enabling inspection, modification, and filtering without requiring changes to MCP client or server code. Uses a hook-based architecture that wraps the MCP transport layer, allowing multiple transformations (trimming, clamping, filtering) to be chained together in a composable pipeline.
Unique: Implements a transparent hook-based middleware pattern that operates at the MCP protocol boundary, allowing composable transformations without modifying client or server code. This is architecturally distinct from proxy-based approaches because it operates in-process and can access both request and response context simultaneously.
vs alternatives: More transparent than proxy-based filtering because it doesn't require network routing changes; more composable than single-purpose tools because the hook layer supports chaining multiple transformations.
Tracks and reports token savings achieved through response trimming and file clamping operations, providing visibility into cost reduction impact. Collects metrics on original vs. trimmed response sizes, file read reductions, and estimated token savings based on Claude's token counting. Outputs metrics in structured format (JSON, CSV) for analysis and optimization feedback.
Unique: Provides first-class metrics collection integrated into the MCP hook layer, capturing before/after sizes at the protocol boundary. This enables precise measurement of token savings without requiring external instrumentation or log parsing.
vs alternatives: More accurate than post-hoc log analysis because it measures at the interception point; more integrated than external monitoring tools because metrics are native to the middleware.
Provides seamless integration with Claude Code environments through automatic hook injection into the MCP client initialization, requiring minimal configuration to activate tokenomy's trimming and clamping features. Detects Claude Code runtime and automatically registers the tokenomy middleware without requiring explicit code changes in user workflows.
Unique: Implements automatic hook injection into Claude Code's MCP client initialization, detecting the runtime environment and registering middleware without explicit user code. This is distinct from manual middleware registration because it requires zero code changes in the user's workflow.
vs alternatives: More user-friendly than manual hook registration because it activates automatically; more reliable than environment-based detection because it integrates directly with Claude Code's initialization pipeline.
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 tokenomy at 27/100. tokenomy leads on ecosystem, while Supermaven is stronger on adoption and quality.
Need something different?
Search the match graph →© 2026 Unfragile. Stronger through disorder.
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