LLM Stats
ProductCompare AI models across benchmarks, pricing, speed, and context window.
Capabilities7 decomposed
multi-model benchmark comparison engine
Medium confidenceAggregates standardized benchmark results (MMLU, HumanEval, GSM8K, etc.) across dozens of LLM providers and open-source models, normalizing scores to a common scale and enabling side-by-side performance comparison. Uses a centralized data pipeline that ingests results from official model cards, academic papers, and third-party evaluation frameworks, then surfaces them through a unified comparison interface with filtering and sorting by benchmark category.
Centralizes fragmented benchmark data from heterogeneous sources (official model cards, academic papers, leaderboards) into a single normalized schema, enabling direct comparison across models that may not have been evaluated on identical benchmark suites
More comprehensive than individual model cards and faster than manually cross-referencing papers; differs from Hugging Face Open LLM Leaderboard by including commercial models and pricing data alongside benchmarks
pricing and cost-per-token calculator
Medium confidenceMaintains a real-time or frequently-updated database of input/output token pricing for LLM APIs (OpenAI, Anthropic, Google, etc.) and calculates effective cost per token, cost per 1M tokens, and total inference cost for a given token volume. Implements a pricing normalization layer that handles variable pricing tiers (e.g., GPT-4 Turbo vs GPT-4o), batch discounts, and context window-dependent pricing, allowing users to estimate total cost of ownership for a workload.
Implements a multi-dimensional pricing model that normalizes across different pricing structures (per-token, per-request, context-window-dependent) and automatically recalculates when providers update rates, rather than static pricing tables
More current than manual spreadsheets and includes more models than individual provider pricing pages; differs from LLM cost calculators by integrating pricing with performance benchmarks for cost-per-quality analysis
context window and throughput specification database
Medium confidenceMaintains a structured database of model specifications including context window size, maximum output tokens, requests-per-minute limits, tokens-per-minute throughput, and latency characteristics. Allows filtering and comparison of models by these constraints, enabling builders to identify models that fit specific architectural requirements (e.g., 'models with 200K+ context window and <100ms latency').
Consolidates scattered specification data from multiple provider documentation pages into a single queryable schema with consistent units and filtering, enabling constraint-based model selection rather than manual documentation review
Faster than reading individual model cards and enables filtering by multiple constraints simultaneously; differs from provider dashboards by aggregating across all providers in one place
model capability matrix and feature comparison
Medium confidenceProvides a structured matrix comparing discrete capabilities across models: vision support, function calling, JSON mode, streaming, fine-tuning availability, multimodal input types, and other feature flags. Implements a capability taxonomy that normalizes heterogeneous feature naming across providers (e.g., 'tool use' vs 'function calling') and surfaces which models support which features with version/tier specificity.
Normalizes capability naming across providers (OpenAI, Anthropic, Google, etc.) into a unified taxonomy and tracks version-specific feature availability, rather than treating each provider's feature set as isolated
More comprehensive than individual provider feature pages and enables cross-provider capability discovery; differs from model cards by explicitly highlighting which models lack specific features
model release timeline and deprecation tracker
Medium confidenceMaintains a chronological database of model releases, updates, and deprecations with dates and version information. Tracks which models are in active development, maintenance, or deprecated status, and surfaces upcoming model releases or sunset dates. Enables filtering by release date range and status to identify stable vs. cutting-edge models.
Aggregates release and deprecation information from multiple provider announcements and documentation into a unified timeline view with forward-looking alerts, rather than requiring manual monitoring of each provider's blog
Proactive deprecation warnings vs. reactive discovery when a model is removed; differs from provider release notes by cross-referencing all providers in one timeline
model performance trend analysis and historical comparison
Medium confidenceTracks benchmark scores over time for models as they are updated or new versions are released, enabling visualization of performance trends and comparison of how models have improved or degraded. Implements time-series data storage and visualization to show performance trajectories across benchmark categories, allowing users to assess whether a model is improving or stagnating.
Maintains time-series benchmark data with version tracking, enabling trend visualization and velocity analysis rather than just point-in-time snapshots; requires continuous data collection and normalization across benchmark versions
Reveals performance trajectories that static comparisons miss; differs from individual model release notes by aggregating trends across all models and benchmarks in one view
model filtering and advanced search with multi-constraint optimization
Medium confidenceImplements a multi-dimensional filtering engine that allows simultaneous filtering across pricing, performance, context window, capabilities, and other dimensions, with optional constraint optimization to find the 'best' model according to user-defined weights. Uses a scoring algorithm that combines multiple metrics (cost, performance, latency, context window) into a composite ranking, enabling users to express complex requirements like 'cheapest model with >90% MMLU score and 100K context window'.
Combines multiple filtering dimensions with optional multi-objective optimization, allowing users to express complex requirements as a single query rather than iteratively filtering across separate pages
More flexible than single-dimension sorting and faster than manual comparison; differs from provider comparison tools by supporting cross-provider filtering with weighted optimization
Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.
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OpenAI: GPT-3.5 Turbo 16k
This model offers four times the context length of gpt-3.5-turbo, allowing it to support approximately 20 pages of text in a single request at a higher cost. Training data: up...
Best For
- ✓ML engineers evaluating models for production deployment
- ✓AI product managers comparing capabilities before vendor selection
- ✓researchers tracking model performance trends over time
- ✓startup founders optimizing API spend before scaling
- ✓ML engineers doing cost-benefit analysis for model selection
- ✓finance teams budgeting for LLM infrastructure costs
- ✓backend engineers designing LLM application architecture
- ✓RAG system builders selecting models for document processing
Known Limitations
- ⚠Benchmark scores reflect synthetic task performance, not real-world application quality
- ⚠Benchmarks may be outdated if models are released faster than evaluation cycles
- ⚠Different benchmark versions (e.g., MMLU-Pro vs MMLU) are not always directly comparable
- ⚠Closed-source models may not publish all benchmark results, creating incomplete comparison matrices
- ⚠Pricing data may lag behind official announcements by hours or days
- ⚠Does not account for regional pricing variations or enterprise discounts
Requirements
Input / Output
UnfragileRank
UnfragileRank is computed from adoption signals, documentation quality, ecosystem connectivity, match graph feedback, and freshness. No artifact can pay for a higher rank.
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Compare AI models across benchmarks, pricing, speed, and context window.
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