LLM Stats
Independent AI leaderboard ranking 335+ models by intelligence, speed, and price
LLM Stats is the freshest, most comprehensive model comparison we've seen, with a transparent composite score and live pricing/speed data. It's an excellent first stop for choosing a model, though benchmarks don't always predict real-world task performance and the free tier may require human verification. For a deeper dive, pair it with vendor docs and your own load tests. Alternatives like Artificial Analysis and LMArena offer different angles, but LLM Stats' blend of cost, speed, and benchmark scores makes it uniquely operational.
Verified 7d ago · liveness 59/100 · cite: rightaichoice.com/tools/llm-stats
- Developers choosing a model for integration into applications
- Researchers comparing benchmark performance across models
- AI buyers evaluating cost vs. capability for deployment
- Hobbyists tracking the latest model releases and rankings
- Users needing in-depth security or compliance analysis for AI models
- Those looking for real-time model output quality ratings beyond benchmarks
- People who want a no-code AI assistant rather than a comparison tool
We scan live Reddit threads, YouTube comments, X posts, G2 reviews and other communities — and hand you an honest verdict in under a minute.
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- Real pros & cons from real users
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Skip LLM Stats if you need granular latency per region, in-depth security/compliance analysis, or real-time quality ratings beyond benchmarks—it focuses on high-level comparison, not operational specifics.
Free tier may require human verification, which could be a barrier for automated or anonymous access.
LLM Stats is freemium. The free tier gives full leaderboard access, filters, comparison, and arenas—enough for evaluation. For heavier programmatic use, the API likely has a paid plan, but details aren't public yet. Compared to Artificial Analysis (also free), it offers similar data but adds speed/pricing.
In short
LLM Stats — Independent AI leaderboard ranking 335+ models by intelligence, speed, and price. Best for Developers choosing a model for integration into applications, Researchers comparing benchmark performance across models, AI buyers evaluating cost vs. capability for deployment. Free to use.
What people actually say about LLM Stats — is it worth it?
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) · researched Jul 3, 2026.
- +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.
- −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.
- • Playground credits may run out on free tier requiring upgrade
- • API usage beyond fair use may incur additional fees
Viability Score
How well maintained and how widely used is LLM Stats? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: August 2026
How we score →Key Features
- Composite LLM Stats Score aggregating GPQA, SWE-Bench, coding-arena, and pricing
- Leaderboard filterable by 13 categories (reasoning, coding, writing, math, research, long context, tool calling, image gen, video gen)
- Open LLM Leaderboard for open-weights models
- Side-by-side model comparison with pricing, context, speed, and benchmark scores
- Playground for live interaction with hundreds of models
- Real-time pricing and speed metrics (tokens/sec) updated hourly with 7-day rolling average
- Community arenas for head-to-head evaluation (Chat, Coding, Image, Video)
- Cheapest model filter for top performers
- Longest context window and fastest output highlights
- News and blog covering model releases and benchmark analyses
- API access to model data and evaluation
- Search and filter by organization, context length, and license
- Dedicated leaderboards for reasoning, coding, writing, math, research, long context, tool calling, image gen, video gen
- Performance Index with composite TrueSkill ratings across published benchmarks
- Methodology page explaining LLM Stats Score computation
About LLM Stats
LLM Stats is an independent AI model comparison platform that ranks 335+ language models from every major lab and provider using a single composite score. The LLM Stats Score blends verified public benchmarks (GPQA Diamond, SWE-Bench Verified, coding-arena) with live performance metrics (output speed, time-to-first-token) and per-token pricing into one comparable number. It's built for developers, researchers, and AI buyers who need an up-to-date, unbiased view of the model landscape to inform selection and procurement. The platform offers full leaderboards with advanced filters by category (reasoning, coding, writing, math, research, long context, tool calling, image gen, video gen), time range (30d, 90d, all), and model type (proprietary vs open-weight). A dedicated Open LLM Leaderboard isolates models with publicly released weights for self-hosting and fine-tuning. The side-by-side comparison tool lets you pit any two models across pricing, context length, speed, and benchmark scores. A playground provides live interaction with hundreds of models, and community arenas (Chat, Coding, Image, Video) capture head-to-head evaluation. Real-time pricing and output speed (tokens/second) are measured over a 7-day rolling average, with the cheapest frontier model and fastest output highlighted. The leaderboard refreshes continuously as new benchmarks land, and pricing revalidates hourly. Recent additions include Claude Mythos Preview (leading GPQA at 94.6%), GPT-5.6 Sol, Claude Opus 5, and GLM-5.2. Grok 4.5 is currently the cheapest top-10 model at $2.00/M tokens, and Mercury 2 leads output speed at 1286 tok/s. The platform also publishes news and blog posts covering model releases and benchmark analyses, plus an API for programmatic access. Unlike static leaderboards or vendor-published numbers, LLM Stats blends benchmark intelligence with live performance and cost, giving a more operational view. It covers both proprietary leaders (GPT, Claude, Gemini) and open-weight models (Llama, Qwen, DeepSeek) on one platform. You get a transparent, continuously refreshed picture of the model landscape, so you can make procurement decisions with current data rather than stale vendor claims.
Behind the Verdict
LLM Stats stands out as a genuinely independent and current source for model comparison. The composite score that blends benchmark intelligence (GPQA, SWE-Bench) with live performance metrics (tokens/sec) and pricing is a smart way to reduce the noise of single-number leaderboards. You get filters for category, time range, open-weight vs. proprietary, which is essential for tailoring to your use case—whether that's coding, reasoning, or long-context processing. The side-by-side comparison tool is particularly useful; you can pit two models on price, context, speed, and benchmarks before you ever touch an API key. The playground and community arenas add a practical dimension, letting you test prompts and see qualitative results beyond benchmarks. However, there are blind spots. Benchmarks are a proxy, not a guarantee. GPQA and SWE-Bench can be gamed or not reflect your specific domain. The live speed and pricing metrics are rolling averages, so they smooth over spikes that might matter for latency-sensitive applications. For granular latency per region or provider, you'll need your own testing. The free tier might require human verification, which could be a barrier for automated or anonymous access. Pricing is updated hourly, but model performance and rankings shift as new models drop, so you need to re-check frequently. Where it fits: if you're a developer choosing between GPT-5.6 Sol and Claude Opus 5, or a buyer comparing cost-per-token across providers, this is a fast, reliable first filter. For an AI buyer at a startup, the cost and speed columns alone can save hours of research. It's less suitable if you need deep security or compliance analysis, or if you're looking for a no-code AI assistant rather than a comparison tool. For latency-critical real-time apps, pair it with your own load tests. Overall, it's a must-bookmark for anyone serious about model selection, as long as you remember benchmarks are a starting point, not the final word.
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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.
You're building a coding assistant and need to pick between GPT-5.6, Claude Opus 5, and open-weight options. You filter the leaderboard for coding category, compare side-by-side on benchmarks and price, then test a few prompts in the playground.
Outcome: You shortlist 2-3 models and have concrete cost/speed numbers to present to your team.
You need a cost-effective model for high-volume inference. You use the pricing filter to find the cheapest top-10 model (e.g., Grok 4.5 at $2/M tokens) and sort by speed to find the fastest for your streaming use case.
Outcome: You identify a model that meets your budget and performance requirements, and you have a clear rationale for the decision.
You monitor the 30-day leaderboard changes and read the blog for new model announcements. You use the API to pull historical data for a paper.
Outcome: You have current, structured data on model performance and pricing for your research.
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.
- Find the fastest model for streaming chat UIs or agentic loops with the speed metric.
- Check the cheapest top-tier model for high-volume inference costs.
Models Under the Hood
as of 2026-08-19
Limitations
- The leaderboard relies on public benchmarks and live API metrics, which may not capture real-world performance for specialized tasks.
- Free access may require human verification.
- Pricing and speed metrics are updated hourly, but rankings can change as new models are added.
as of 2026-08-17
Verification history
We have re-verified LLM Stats 5 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — re-checked, vendor evidence unchanged
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
Free to cite with attribution — this page re-verifies continuously.
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
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
Anyone—developers, researchers, hobbyists—who needs up-to-date leaderboard rankings, comparisons, and playground access without paying.
What this tier adds
Free entry point gives full access to all core features: leaderboard, filters, comparison, arenas, and playground.
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 freemium. The free tier gives full leaderboard access, filters, comparison, and arenas—enough for evaluation. For heavier programmatic use, the API likely has a paid plan, but details aren't public yet. Compared to Artificial Analysis (also free), it offers similar data but adds speed/pricing.
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 a developer: 5-10 minutes to explore the site, filter by your needs, and run a comparison. For a procurement lead: 15-30 minutes to review pricing and speed columns and shortlist models. Playground testing takes additional time depending on how many models you try.
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.
- →From Artificial Analysis: Import your model shortlist manually and use LLM Stats' side-by-side comparison to get additional pricing and speed metrics.
- ↗To vendor docs: For detailed latency or region-specific data, use the vendor's own documentation and testing.
Integrations
Resources & Guides
Tutorials & Learning
Official links
Tools that pair well with LLM Stats
Common stack mates teams adopt alongside LLM Stats, with the specific reason each pairing earns its keep.
Featured Head-to-Head Comparisons
Llm Stats vs Praktika
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Llm Stats vs Screenplayiq
Choose LLM Stats if you need an up-to-date, benchmark-driven comparison of AI models for development or research; choose ScreenplayIQ if you are a film professional seeking data-backed screenplay analysis and box office predictions. They serve entirely different domains with no overlap, so the decision hinges on your role and need.
Llm Stats vs Semantic Scholar
If you're choosing an LLM for your app or research, LLM Stats gives you the real-time benchmark and pricing data you need to compare 300+ models. If you're a scientist or student hunting down papers, Semantic Scholar's free AI search and TLDR summaries are unmatched. They solve different problems, so pick the one that matches your workflow.
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