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Capability with less spendMonthly token estimate · log scale
| Shortlist | Model / provider | index points | USD / month | tokens / sec | seconds | tokens | Endpoint details |
|---|---|---|---|---|---|---|---|
| Anthropic | 64.6 | $3,600 | 101 | 4.1 min | 1M | ||
| Anthropic | 62.2 | $3,600 | 105 | 1.2 min | 1M | ||
| Anthropic | 61.8 | $3,600 | 96 | 30.02 s | 1M | ||
| 60 | $1,800 | 78 | 1.1 min | 1M | |||
| Anthropic / Amazon Bedrock | 59.1 | $1,800 | 111 | 6.1 min | 1M | ||
| 58.3 | $1,800 | 72 | 23.21 s | 1M | |||
| Anthropic | 57.4 | $1,800 | 110 | 1.4 min | 1M | ||
| OpenAI | 56.9 | $2,250 | 63 | 2.4 min | 1M |
About these measurements
Model variants show the lowest-cost priced host that fits your workload, where available. Monthly estimates use your input and output token assumptions; speed and latency are measured on the source workload. Benchmarks describe evaluation performance. Read the methodology
Start with the job
The “best” model depends on what you need done.
Use-case rankings combine the benchmarks and constraints that matter for the task, then explain why each model earned its place.
The fine print
Questions worth asking.
How the data is sourced, what the rankings mean, and where the methodology has limits.
Using saved data exported 11 Nov 2026, 10:48 UTC. Fetch and snapshot dates describe WhatLLM data access, not when a benchmark was measured. Data freshness and sources · Dataset usage terms
Ranking library
Current live rankings
Focused rankings for the decisions engineers actually make.
Why WhatLLM.org exists
Live LLM comparison, with the evidence attached.
The AI landscape moves fast. Every few weeks a new model launches — sometimes multiple in a single day — each claiming state-of-the-art results on different benchmarks. For developers choosing an LLM for production, for researchers evaluating the field, or for teams deciding where to invest their API budget, keeping track of it all is exhausting.
WhatLLM.org was built to solve that problem. We aggregate benchmark data, real-world pricing, and throughput metrics for over 94 model families from 52+ normalized providers into one place. Instead of opening dozens of tabs to compare OpenAI, Anthropic, Google, Meta, DeepSeek, and Mistral side by side, you get a unified interface where models can be filtered, sorted, and compared on the dimensions that actually matter to your use case.
How We Compare Models
Every model on WhatLLM.org is evaluated across four core dimensions: quality, speed, price, and context length. Quality is measured using the Artificial Analysis Intelligence Index, a composite capability score. Its evaluation set and weights depend on the methodology version. Compare scores from the same version and consult task-level results when choosing for a specific workload.
Speed is measured in output tokens per second, reflecting real-world throughput under typical load. Price is tracked per million tokens for both input and output, with blended cost calculations. Context length reflects the maximum number of tokens a model can process in a single request, ranging from 8K tokens on older models to 10M tokens on the latest architectures.
Finding the Right Model for Your Use Case
There is no single "best" LLM. The right choice depends on your priorities. If you need the highest reasoning quality for complex tasks, frontier models like GPT-5, Gemini 3 Pro, or Claude Opus 4.5 lead the benchmarks. If cost efficiency matters most, open-source models like DeepSeek V3, Qwen3, or Kimi K2 deliver strong performance at a fraction of the price. For latency-sensitive applications, speed-optimized endpoints on providers like Groq, Fireworks, or Cerebras can deliver hundreds of tokens per second.
Our LLM Selector tool walks you through a few quick questions about your use case — coding, analysis, creative writing, agentic workflows — and recommends a shortlist of models ranked by fit. The Compare page lets you pick any 2–4 models and see their benchmarks, pricing, and speed side by side in a detailed breakdown.
Original Analysis and Research
Beyond the comparison tools, WhatLLM.org publishes original analysis on model releases, benchmark trends, and the economics of AI deployment. Our blog covers topics from detailed model face-offs (like Kimi K2 Thinking vs. ChatGPT 5.1) to broader industry analysis (the open-source vs. proprietary cost curve, the rise of agentic coding models, and whether benchmark saturation is making traditional evaluation frameworks obsolete). Each article is written with original commentary grounded in the data we track.
Data Sources and Transparency
Benchmark and quality data is sourced from Artificial Analysis, an independent research organization. Provider pricing comes from the same data source, with official documentation cited in reviewed model guides. Fetch status and editorial review dates are separate; our methodology is documented publicly. WhatLLM.org does not run its own benchmarks; we focus on making existing high-quality data accessible, interactive, and actionable.