What is an LLM comparison?
It compares large language models using matched benchmark results, pricing, specifications, availability, and source dates.
Gradually LLM Index
Compare LLMs without mixing incompatible test setups
Compare 28 leading language models across 378 head-to-head matchups. Every result includes shared benchmark cohorts, current API pricing, key specifications, and source links.
- models
- 28
- head-to-head matchups
- 378
- active benchmarks
- 80
- active results
- 14,135
Short answer
Which LLM is the best?
There is no single best LLM for every workload. A coding agent, a low-cost extraction pipeline, and a long-context research task reward different strengths.
Choose the two models you are considering. The result page shows only shared measurements and keeps pricing, context, architecture, and availability separate from benchmark scores.
Decision criteria
What this LLM comparison lets you check
- Benchmarks
- Shared tasks and versions for reasoning, coding, agents, research, and other capabilities.
- API pricing
- Input, output, cache-read, and cache-write pricing where the provider publishes it.
- Context
- Published context windows and maximum output limits without imprecise rounding.
- Architecture
- Known parameters, active parameters, dense or MoE architecture, and open-weight status.
- Availability
- API access, lifecycle status, release date, and documented web-search support.
- Sources
- A retrieval or verification date and a direct source for each volatile result.
Need a broader directory? Browse the open-source LLM directory. For workload-specific cost estimates, use the API cost calculator.
Popular matchups
Ten matchups that matter right now
Every matchup uses the same data logic. A result appears only when the benchmark, version, task, metric, cohort, and documented test setup align.
Claude Opus 5 vs. Claude Fable 5Open matchupClaude Opus 5 vs. GPT-5.6 SolOpen matchupGPT-5.6 Sol vs. Gemini 3.7 FlashOpen matchupDeepSeek-V4-Pro vs. Kimi K3Open matchupGrok 4.6 vs. Gemini 3.7 FlashOpen matchupGLM-5.2 vs. MiMo-V2.5-ProOpen matchupClaude Opus 5 vs. Gemini 3.7 FlashOpen matchupClaude Opus 5 vs. Grok 4.6Open matchupGPT-5.6 Sol vs. DeepSeek-V4-ProOpen matchupClaude Sonnet 5 vs. GPT-5.6 TerraOpen matchup
Model selection
The 28 most relevant models
The database focuses on current frontier models and relevant lower-cost options. Superseded generations are excluded.
Anthropic
Claude Opus 5Claude Fable 5Claude Sonnet 5Claude Mythos 5
OpenAI
GPT-5.6 SolGPT-5.6 TerraGPT-5.6 LunaGPT-5.5GPT-5.4GPT-5.4 mini
Gemini 3.7 FlashGemini 3.6 FlashGemini 3.5 FlashGemini 3.1 Pro PreviewGemini 3.5 Flash-Lite
xAI
Grok 4.6Grok 4.5Grok 4.3
DeepSeek
DeepSeek-V4-ProDeepSeek-V4-Flash
Moonshot AI
Kimi K3Kimi K2.6
Z.ai
GLM-5.2GLM-5.1
Mistral AI
Mistral Large 3Mistral Medium 3.5
Xiaomi
MiMo-V2.5MiMo-V2.5-Pro
Methodology
A comparison is only as sound as its cohort
Central database
28 sources, 14,135 active results, and one consistent model schema.
Matched test setup
Results are paired only when benchmark version, task, metric, cohort, and documented setup align.
Missing data stays missing
If two models have no shared measurement series, the gap stays visible. We do not estimate scores.
Evidence, not one overall ranking
Benchmarks, pricing, and specifications sit side by side. The right model depends on your workload.
Database retrieved on August 16, 2026. Each model price has its own verification date and linked primary source.
FAQ
Questions about the LLM comparison
Why do some matchups show few benchmarks?
We do not combine different benchmark versions, tasks, metrics, cohorts, reasoning levels, or agent setups into one score.
Are benchmark winners always better?
No. A measured lead applies to the documented task and setup. Cost, latency, context, and tool access can change the practical choice.
How current is the database?
The benchmark snapshot was retrieved on August 16, 2026. Volatile model facts carry their own source and verification dates.