AI Model Benchmark Tracker

What does AI Model Benchmark Tracker do?

AI Model Benchmark Tracker pulls the top open-source large language models from the HuggingFace public API — sorted by monthly downloads — and enriches each record with architecture metadata, parameter counts, efficiency scores, deployment suitability, and capability profiles. It also produces an ecosystem-level summary that identifies the most-downloaded model, fastest-growing release, SOTA leaderboard, and the best models for crypto and trading agent use cases. No API key or HuggingFace account is required.

Why use AI Model Benchmark Tracker?

• Comprehensive LLM landscape coverage — tracks up to 100 top text-generation models in a single run, spanning every major family: Llama, Mistral, Gemma, Qwen, DeepSeek, Phi, Falcon, and more, so you never have to manually browse HuggingFace to find what's trending.
• Deep enrichment beyond raw download counts — every model gets a performance_tier, cost_efficiency_score, deployment_readiness, capability_profile, reasoning_vs_speed_tradeoff, and a context_window_tier, turning raw metadata into actionable selection criteria.
• Crypto and AI agent use-case fit scoring — a dedicated crypto_use_case_fit field (ideal / good / moderate / poor) rates each model specifically for trading bots, on-chain analytics agents, and finance automation pipelines.
• Open vs. closed source intelligence — open_vs_closed_signal classifies every model as fully_open, open_weights, api_only, or closed, and open_vs_closed_advantage identifies where open-weight models are genuinely competitive with proprietary alternatives.
• Affordable weekly monitoring — at $0.001 per model tracked plus a $0.001 start fee, tracking 50 models costs just $0.051, making it practical to run weekly and keep your model selection strategy current as the LLM landscape evolves rapidly.

How to use AI Model Benchmark Tracker

1. Go to the Apify Store and open AI Model Benchmark Tracker.
2. Click Try for free to open the Actor in Apify Console.
3. Set limit (default 50, max 100) to control how many top models to fetch.
4. Optionally set filterArchitecture (e.g. llama, mistral, gemma) to narrow results to a specific model family.
5. Toggle excludeQuantized to skip GGUF/GPTQ/AWQ variants, or instructTunedOnly to return only chat/instruct models.
6. Set minDownloads to filter out low-adoption models below a downloads threshold.
7. Click Start — the run typically completes in under 30 seconds.
8. Download results as JSON or connect via the Apify API for dashboard pipelines.

Input

Field	Type	Default	Description
limit	integer	50	Number of top models to fetch by monthly downloads (max 100)
filterArchitecture	string	""	Filter by base model family (e.g. llama, mistral, gemma, qwen, phi)
excludeQuantized	boolean	false	When true, skips GGUF, GPTQ, and AWQ quantized model variants
instructTunedOnly	boolean	false	When true, returns only instruction-tuned / chat models
minDownloads	integer	0	Minimum 30-day download count threshold

Output

The dataset contains one record per model followed by a final summary record:

[
  {
    "model_id": "meta-llama/Meta-Llama-3-8B-Instruct",
    "architecture": "LlamaForCausalLM",
    "parameters_b": 8.0,
    "params_tier": "medium",
    "downloads_30d": 4823901,
    "likes": 18234,
    "license": "llama3",
    "is_instruction_tuned": true,
    "is_quantized": false,
    "is_open_weight": true,
    "recency_label": "established",
    "base_model": "llama",
    "capability_tags": ["text-generation", "code", "reasoning"],
    "efficiency_score": 602987.63,
    "context_window": 131072,
    "performance_tier": "frontier",
    "cost_efficiency_score": 84.21,
    "cost_efficiency_grade": "A",
    "capability_profile": "reasoning_dominant",
    "deployment_suitability": "production_api",
    "deployment_readiness": "production_ready",
    "improvement_velocity": "incremental",
    "crypto_use_case_fit": "ideal",
    "context_efficiency_tier": "long",
    "context_window_tier": "long",
    "capability_tier": "frontier",
    "reasoning_vs_speed_tradeoff": "high_reasoning",
    "multimodal_capability": "text_only",
    "open_vs_closed_signal": "open_weights",
    "open_vs_closed_advantage": "open_source_strong",
    "benchmark_trend": "stable",
    "hf_url": "https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct",
    "scraped_at": "2025-06-01T08:00:00+00:00"
  },
  {
    "model_id": "mistralai/Mistral-7B-Instruct-v0.3",
    "architecture": "MistralForCausalLM",
    "parameters_b": 7.0,
    "params_tier": "medium",
    "downloads_30d": 2104567,
    "likes": 9812,
    "license": "apache-2.0",
    "is_instruction_tuned": true,
    "is_quantized": false,
    "is_open_weight": true,
    "recency_label": "recent",
    "base_model": "mistral",
    "capability_tags": ["text-generation", "multilingual"],
    "efficiency_score": 300652.43,
    "context_window": 32768,
    "performance_tier": "leading",
    "cost_efficiency_score": 78.55,
    "cost_efficiency_grade": "B",
    "capability_profile": "balanced",
    "deployment_suitability": "production_api",
    "deployment_readiness": "production_ready",
    "improvement_velocity": "significant",
    "crypto_use_case_fit": "good",
    "context_efficiency_tier": "standard",
    "context_window_tier": "standard",
    "capability_tier": "top_tier",
    "reasoning_vs_speed_tradeoff": "balanced",
    "multimodal_capability": "text_only",
    "open_vs_closed_signal": "fully_open",
    "open_vs_closed_advantage": "open_source_strong",
    "benchmark_trend": "improving",
    "hf_url": "https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3",
    "scraped_at": "2025-06-01T08:00:00+00:00"
  }
]

Data fields

Field	Description
model_id	Full HuggingFace model identifier (e.g. meta-llama/Meta-Llama-3-8B-Instruct)
architecture	Transformer class name from model config (e.g. LlamaForCausalLM)
parameters_b	Parameter count in billions, extracted from model card, tags, or model name
params_tier	Size bucket: tiny (<1B), small (<7B), medium (<30B), large (<100B), giant (100B+)
downloads_30d	HuggingFace downloads over the past 30 days
likes	HuggingFace community likes count
license	Model license identifier (e.g. llama3, apache-2.0, mit)
is_instruction_tuned	true if model name contains instruct/chat/IT/DPO/RLHF markers
is_quantized	true if GGUF, GPTQ, AWQ, BnB, 4-bit, or 8-bit quantization detected
is_open_weight	true if weights are available under an open or permissive license
recency_label	new (<90d), recent (<1y), established (<2y), legacy (2y+)
base_model	Dominant model family: llama, mistral, gemma, qwen, falcon, phi, deepseek, other
capability_tags	Standardised capability list: text-generation, code, multilingual, math, reasoning, vision
efficiency_score	Downloads per billion parameters — proxy for community adoption relative to model size
context_window	Maximum context length in tokens (when available from model card or tags)
performance_tier	Download-rank tier: frontier, leading, competitive, average, below_average
cost_efficiency_score	0–100 score: high downloads at small parameter count = high cost efficiency
cost_efficiency_grade	Letter grade A/B/C/D/F derived from cost_efficiency_score
capability_profile	Dominant use-case profile: coding_dominant, reasoning_dominant, multimodal_dominant, instruction_following, raw_knowledge, balanced
deployment_suitability	Best deployment context: production_api, edge_deployment, fine_tuning_base, research_only, enterprise_only
deployment_readiness	Production maturity: production_ready, beta, research_only, deprecated
improvement_velocity	Release momentum: quantum_leap, significant, incremental, stagnant
crypto_use_case_fit	Suitability for crypto/trading agents: ideal, good, moderate, poor, not_suitable
capability_tier	Composite capability level: frontier, top_tier, competitive, mainstream, legacy
reasoning_vs_speed_tradeoff	Speed/depth axis: max_reasoning, high_reasoning, balanced, speed_focused, fastest
context_window_tier	Context size class: ultra_long (>500K), long (>100K), standard (>32K), limited, very_limited
multimodal_capability	full_multimodal, vision_only, text_only, specialized
open_vs_closed_signal	Openness classification: fully_open, open_weights, api_only, closed, unknown
open_vs_closed_advantage	Competitive positioning: open_source_strong, open_source_competitive, gap_closing, closed_dominant, proprietary_required
benchmark_trend	Inferred trajectory: improving, stable, declining, new_entry
hf_url	Direct HuggingFace model page URL
scraped_at	ISO 8601 UTC timestamp of data collection

Cost estimation

Pricing: $0.001 per model tracked + $0.001 one-time start fee (Pay-Per-Event).

Scenario	Models tracked	Start fee	Model fees	Total cost
Small (quick scan)	10	$0.001	$0.010	$0.011
Medium (default)	50	$0.001	$0.050	$0.051
Large (full top 100)	100	$0.001	$0.100	$0.101

A weekly scheduled run tracking 50 models costs approximately $0.20/month — well within the free tier for most Apify plans.

FAQ

Does this Actor require a HuggingFace API token? No. It uses the HuggingFace public /api/models endpoint, which is freely accessible without authentication. No HuggingFace account is needed.

How is parameters_b determined when it isn't explicitly listed? The Actor checks three sources in order: (1) the model card metadata fields parameter_count / num_parameters, (2) HuggingFace tags like 7B or 13B, and (3) a regex pattern applied to the model ID itself (e.g. llama-3-70b-instruct → 70B). If none of these yield a result, parameters_b will be null.

Can I track models other than text-generation? Currently the Actor filters for the text-generation pipeline tag to stay focused on the LLM landscape relevant to agents, chatbots, and code generation. Support for additional pipeline types such as image-to-text or text-to-image may be added in future versions.

Disclaimer: Download counts and derived scores reflect community adoption signals on HuggingFace and do not constitute endorsements of any specific model. Performance tiers are proxies based on download rank, not formal benchmark evaluations (e.g. MMLU, HumanEval). Always evaluate models on your own tasks and data before deploying to production systems.

Keywords: open source LLM tracker, HuggingFace model benchmark, AI model comparison, LLM download rankings, best open source language model, LLM efficiency score, AI model leaderboard, Llama Mistral Gemma comparison, LLM deployment readiness, crypto AI agent model selection

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Related actors

• HuggingFace Model Tracker — Live trending and upload activity for models benchmarked here
• Semantic Scholar Papers — Research papers behind the models in this benchmark tracker
• AI Token Sector Tracker — AI crypto token performance alongside AI model release activity

Feedback

If this actor powers your AI research, a review helps others find it: Leave a review on Apify Store

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Keywords: LLM benchmark, open source AI models, HuggingFace models, language model tracker, AI model downloads, model parameters, AI efficiency score, LLM comparison, model capability, AI research tracker, open weight models, instruction tuned, quantized models, AI deployment, model performance ranking