LLM Stats
FreemiumIndependent leaderboard comparing 300+ AI models by intelligence, speed, and price.
By Tanmay Verma, Founder · Last verified 06 Jul 2026
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Added 7d ago
77/100Safe BetIn short
LLM Stats — Independent leaderboard comparing 300+ AI models by intelligence, speed, and price. Best for Developers choosing a model for integration, Researchers comparing benchmark performance, AI buyers evaluating cost vs. capability. Free to use.
Is LLM Stats actually worth it?
LiveSee what real users actually say. We scan live discussions, reviews and complaints across the web and hand you an honest verdict — in under a minute.
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Editorial Verdict
Best for
Developers choosing a model for integrationResearchers comparing benchmark performanceAI buyers evaluating cost vs. capabilityHobbyists tracking the latest model releases
Not ideal for
Users needing in-depth security or compliance analysisThose looking for real-time model output quality ratings beyond benchmarksPeople who want a no-code AI assistant rather than a comparison tool
LLM Stats offers one of the most comprehensive and current model comparisons available, with transparent methodology and a composite score that balances multiple dimensions. Its real-time pricing and speed data, plus dedicated open-weight leaderboard, make it a solid starting point for model selection. However, the platform relies on public benchmarks that may not capture real-world performance for specialized tasks, and the free tier has rate limits. For a quick, data-driven model comparison, LLM Stats is a recommended resource.
Skip LLM Stats if Skip LLM Stats if you need in-depth security or compliance analysis, or if you're looking for real-time model output quality ratings beyond benchmarks.
Compare with: LLM Stats vs Mineral (Alphabet X), LLM Stats vs Goodfire, LLM Stats vs Arena AI
Last verified: July 2026
What independent users actually report about LLM Stats
We ran a structured research pass across product reviews, community discussions, and post-purchase forum threads to surface the patterns vendors won't publish themselves. Below: the recurring strengths, the hidden costs people mention most, and the cohort that consistently regrets adopting this tool.
69 mentions across 4 sources (Hacker News, YouTube, Product Hunt, Lemmy).
40% positive60% critical
Recurring strengths
- +Aggregates 300+ models with one composite score for quick comparison.
- +Side-by-side cost-per-token next to benchmark scores saves time.
- +Playground lets you test models live before committing to an API.
- +Filters by use case (coding, writing, math) directly address buyer needs.
- +Open LLM leaderboard helps compare open-weight alternatives fairly.
Recurring frustrations
- −Update frequency is unclear, worrying users about stale data.
- −No integrations with tools like Raycast, limiting workflow use.
- −Third-party model providers may introduce latency or pricing gaps.
- −Support responsiveness is unknown due to limited community feedback.
- −Data sources behind benchmarks aren't fully transparent.
Patterns worth knowing
Great for quick model comparison, but data freshness is a concern
Seen on Product Hunt, Hacker News
Free tier is generous and useful for exploratory research
Seen on Product Hunt
Playground and chat features are valued for hands-on testing
Seen on Product Hunt
Learning curve
beginnerProductive in ~5 minutes
Hidden costs people mention
- • Playground credits may run out on free tier requiring upgrade
- • API usage beyond fair use may incur additional fees
Viability Score
77/100
Safe BetHow likely is LLM Stats to still be operational in 12 months? Based on 4 signals — momentum (how recently it shipped), wrapper dependency, revenue model, and web presence.
momentum
55
funding runway
80
website health
90
wrapper dependency
100
Last calculated: July 2026
How we score →Key Features
- Composite LLM Stats Score aggregating multiple benchmarks
- Leaderboard filterable by category (coding, writing, math, etc.)
- Model comparison tool with side-by-side scores
- Playground for live interaction with hundreds of models
- API access to model data and evaluation
- Real-time pricing and speed metrics
- Open LLM Leaderboard for open-weight models
- Arena for community-driven model evaluation (Chat, Coding, Image, Video)
- News and blog covering model releases and benchmarks
- Search and filter models by organization, context length, and license
- Filter by time range (30d, 90d, all)
- Dedicated leaderboards for coding, writing, math, research, long context, tool calling, reasoning, image gen, video gen
About LLM Stats
FreemiumIntermediateAPI availableWeb · API
LLM Stats is a model comparison platform that aggregates public benchmarks and live API metrics into a unified, continuously updated leaderboard. It scores models on reasoning, coding, writing, math, long context, tool calling, and more, producing a composite LLM Stats Score. You can filter by category (coding, writing, math, research, image gen, video gen), time range (30d, 90d, all), and model type (proprietary, open-weight). Features include a side-by-side model comparison tool, a playground for live testing across hundreds of models, an Open LLM Leaderboard, arenas (Chat, Coding, Image, Video) for community-driven evaluation, real-time pricing and speed metrics, and an API for programmatic access. The platform also hosts news and blog articles covering model releases and benchmarks. It's designed for developers, researchers, and AI buyers who need an independent, up-to-date ranking to inform model selection, including comparisons between proprietary leaders (GPT, Claude, Gemini) and open-weight alternatives (Llama, DeepSeek, Qwen, GLM). Data is sourced from public benchmarks (GPQA, MMLU, SWE-Bench, AIME, etc.) and live API metrics, with a transparent methodology. As of mid-2026, the leaderboard shows models like Claude Mythos Preview, GPT-5.5, Gemini 3.1 Pro, and GLM-5.2.
Behind the Verdict
LLM Stats stands out for its breadth and freshness. The leaderboard includes over 300 models, from frontier closed-source (Claude, GPT, Gemini) to open-weight (Llama, DeepSeek, Qwen, GLM). The composite LLM Stats Score aggregates multiple benchmarks (GPQA, MMLU, SWE-Bench, AIME, etc.) into a single number, but you can drill into sub-scores for reasoning, coding, writing, math, long context, tool calling, and more. The platform updates continuously as new benchmarks and API data arrive, so you won't be looking at stale numbers. The speed and pricing data, pulled from each provider's public price list and cross-checked via the LLM Stats proxy, is especially valuable for cost-conscious buyers. The Open LLM Leaderboard highlights the best open-weight models, and the arenas let you participate in community-driven evaluations. The playground is useful for quick prompt testing across many models. On the downside, the free tier may have API rate limits, and advanced features (e.g., detailed model comparisons, arenas) may require verification. Also, benchmarks don't always reflect real-world performance, especially for niche tasks. For security/compliance analysis, you'll need to go elsewhere. Overall, LLM Stats is a powerful tool for model selection, but you should supplement it with your own testing for critical use cases.
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Real-world workflow fit
Concrete scenarios for the personas LLM Stats actually fits — and what changes day-one when you adopt it.
Developer evaluating a model for a coding agent
You want to pick a model that excels at coding and is cost-efficient. You open the coding leaderboard, filter by 30-day window, and sort by price. You see Gemini 3.1 Pro has a high coding-arena score at a moderate price. You then use the side-by-side comparison tool to compare it with GPT-5.5 and Claude Sonnet 5, checking speed and context. Finally, you test a few prompts in the playground.
Outcome: You confidently choose Gemini 3.1 Pro for your coding agent, saving costs compared to GPT-5.5 while maintaining high performance.
Researcher tracking open-weight model progress
You're studying open-weight models. You go to the Open LLM Leaderboard, see GLM-5.2 leads with 91.2% GPQA. You filter by reasoning and compare it with Llama and Qwen models. You read the blog for recent releases, then export the comparison data via the API for your paper.
Outcome: You document the current state of open-weight models and use the data to support your research findings.
AI buyer comparing cost vs. capability
You have a budget of $2/M tok and need a model for long-context reasoning. You filter the leaderboard by context length > 1M tokens and price < $2/M tok. Grok 4 Fast appears with 2M context at a low price. You then check its reasoning score and verify via the playground.
Outcome: You select Grok 4 Fast, meeting your budget and context requirements, validated by benchmarks and live testing.
Use Cases
- Compare the latest Claude, GPT, and Gemini models on coding and reasoning benchmarks.
- Identify the most cost-efficient model for your budget using speed and pricing columns.
- Filter leaderboard by 30-day window to see recent performance changes.
- Use the playground to test prompts across dozens of models simultaneously.
- Track new model announcements and their initial benchmark scores via the blog.
- Choose between open-weight and proprietary models using the Open LLM Leaderboard.
Models Under the Hood
Claude Mythos PreviewGPT-5.5GPT-5.2 ProClaude Opus 4.8GLM-5.2Gemini 3.1 ProQwen3.7 MaxGrok 4 FastMercury 2Seed 2.1 Pro
as of 2026-07-06
Limitations
- The free tier may have API rate limits; advanced features like detailed model comparisons and arenas may require verification.
- The platform relies on public benchmarks which may not capture real-world performance for specialized tasks.
as of 2026-07-06
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Annual total
Free
Over 12 months
Effective monthly
Free
Billed monthly
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Plans compared
For each published LLM Stats tier: who it actually fits, and what it adds vs. the previous tier. Cross-reference the cost calculator above for projected annual outlay.
Free
$0/mo
Ideal for
Individual researchers, hobbyists, or developers who need basic leaderboard access and occasional comparisons.
What this tier adds
Free tier provides full leaderboard access, model comparison, and playground with potential rate limits; no cost entry point.
Integrations
Where the pricing makes sense
The company stage and team size where LLM Stats's pricing actually pencils out — and where peers do it cheaper.
LLM Stats is free for basic leaderboard access and comparisons, making it ideal for individual researchers and hobbyists. For heavy API usage or advanced features, costs may emerge through rate limits or verification requirements. Compared to other model comparison tools that charge for API access to benchmarks, LLM Stats is cost-effective for casual use.
Setup time & first value
How long it actually takes to get something useful out of LLM Stats — broken out by persona, not the marketing-page minute.
For developers, you can access the leaderboard and comparisons immediately without signup. Signing up for the free account takes 1 minute. API access requires a free account and key generation, about 5 minutes to start pulling data.
Switching to or from LLM Stats
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
Migrating in
- →From spreadsheets or manual tracking: Use the export feature or API to pull benchmark data for your reports.
- →From other leaderboards (e.g., ELO ratings): Map your current model selections to LLM Stats' composite score for a more rounded view.
Migrating out
- ↗To a custom solution: Export benchmark data via API to build your own internal dashboard.
- ↗To alternative trackers: Use the open data to populate other tools if you prefer different aggregation methods.
Resources & Guides
Frequently Asked Questions
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