Best AI LLM Models 2026
AI models ranked by coding ability across benchmarks, real-world usage, and developer sentiment. Rankings are updated hourly using real-time data including benchmarks, Elo ratings, community sentiment, and adoption metrics.
| Rank | Model | Provider | Score | Status | Actions |
|---|---|---|---|---|---|
1 | Anthropic | 97 | |||
2 | Anthropic | 97 | New | ||
3 | Anthropic | 95 | May change | ||
4 | Anthropic | 95 | May change | ||
5 | Anthropic | 95 | |||
6 | Anthropic | 95 | |||
7 | Anthropic | 95 | |||
8 | Anthropic | 95 | |||
9 | Anthropic | 95 | New | ||
10 | Anthropic | 95 | New | ||
11 | OpenAI | 93 | |||
12 | OpenAI | 93 | New | ||
13 | OpenAI | 93 | May change | ||
14 | OpenAI | 93 | New | ||
15 | 92 | May change | |||
16 | 92 | May change | |||
17 | 92 | New | |||
18 | OpenAI | 92 | May change | ||
19 | OpenAI | 92 | New | ||
20 | OpenAI | 92 | May change | ||
21 | OpenAI | 92 | New | ||
22 | OpenAI | 91 | May change | ||
23 | OpenAI | 91 | |||
24 | OpenAI | 91 | May change | ||
25 | OpenAI | 91 | May change | ||
26 | OpenAI | 91 | May change | ||
27 | OpenAI | 91 | New | ||
28 | OpenAI | 91 | May change | ||
29 | OpenAI | 91 | New | ||
30 | Anthropic | 90 | May change | ||
31 | Anthropic | 90 | New | ||
32 | OpenAI | 89 | May change | ||
33 | OpenAI | 89 | New | ||
34 | OpenAI | 89 | May change | ||
35 | OpenAI | 89 | New | ||
36 | OpenAI | 89 | May change | ||
37 | OpenAI | 89 | New | ||
38 | OpenAI | 89 | May change | ||
39 | OpenAI | 89 | New | ||
40 | OpenAI | 89 | May change | ||
41 | OpenAI | 89 | New | ||
42 | OpenAI | 89 | May change | ||
43 | OpenAI | 89 | New | ||
44 | xAI | 89 | New | ||
45 | xAI | 89 | New | ||
46 | xAI | 89 | New | ||
47 | OpenAI | 89 | May change | ||
48 | OpenAI | 89 | May change | ||
49 | OpenAI | 89 | May change | ||
50 | OpenAI | 89 | May change |
Our llm model rankings use a composite scoring system that combines multiple signals to give you the most complete picture of each model's strengths and weaknesses.
Performance on standardized coding, reasoning, and category-specific benchmarks.
Head-to-head comparison ratings from AI chatbot arenas and blind testing.
Analysis of discussions on Reddit, Twitter/X, and developer forums.
Real-world usage data, API traffic patterns, and growth trajectories.
Search volume and interest trends for model-related queries.
Stars, forks, and contributor activity for open-source models and integrations.
Performance-per-dollar analysis based on API pricing and output quality.
Real-time API latency measurements and throughput testing.
Scores are normalized to a 0-100 scale. Rankings update hourly. Learn more aboutour methodology.
As of our latest rankings, Claude Fable 5 leads the llm category with a composite score of 97.1. Rankings are recalculated from benchmark, pricing, capability, and adoption signals as those sources refresh.
We use a composite scoring system that combines multiple signals: benchmark performance, Elo ratings, repository popularity, community sentiment, API latency, cost efficiency, adoption rates, and expert reviews. Each signal is normalized and weighted to produce a final score.
We currently track 50 AI models in the llm category. Our coverage is expanding as new models are released.
Ranking freshness depends on the underlying source. Pricing and provider catalog data refresh on a faster cadence, while benchmark and archived movement data update whenever new verified source data lands.
Yes! Click on any two models to see a detailed head-to-head comparison, including signal-by-signal breakdowns, pricing calculators, and personalized recommendations.