LLM Model Comparison
13 models · Updated 2025 · Prices per 1M tokens
Prices shown are standard API rates as of July 2026. Batch/cached rates may be lower; some providers charge premium rates above a long-context threshold. Open-source models shown as "Free / Self-host" — inference costs vary by provider.
Head-to-head comparisons
Detailed side-by-side pages with pricing math, verdicts, and use-case guidance.
About
This tool provides a side-by-side comparison of 13 leading large language models (as of July 2026) including GPT-5.5, GPT-5.4, o3, Claude Fable 5, Claude Opus 4.8, Claude Sonnet 5, Claude Haiku 4.5, Gemini 3.1 Pro, Gemini 3.5 Flash, DeepSeek V4 Pro, DeepSeek V4 Flash, Llama 5, and Mistral Large 3. Each model entry includes context window size, input/output pricing per million tokens, supported modalities (text/image/audio/video/code), knowledge cutoff date, open/closed source status, license, key strengths, and release date. Filter by provider, open-source status, or modality. Sort by any column. Dedicated head-to-head pages add verdicts and workload cost math. All data is embedded client-side — no API calls or server needed.
How to use
- 1 Browse the table to compare all 13 models at a glance.
- 2 Type in the search box to filter by model name, provider, or keyword.
- 3 Click a column header to sort by that property (click again to reverse).
- 4 Use the filter chips to show only open-source models or filter by modality.
- 5 Click any model row to expand its full details including strengths and license.
- Which LLM has the largest context window?
- As of July 2026, Meta's open-weights Llama 5 leads with a 5 million token context window. Among proprietary models, GPT-5.5, GPT-5.4, Claude Fable 5, Claude Opus 4.8, Claude Sonnet 5, and the Gemini 3 family all offer roughly 1 million tokens. Note that some providers bill long prompts at premium rates — GPT-5.x doubles input pricing past 272K tokens, and Gemini 3.1 Pro steps up past 200K.
- What does input vs output pricing mean?
- LLM APIs charge separately for prompt tokens (input) and generated tokens (output). Output typically costs 3-5x more than input because generation is computationally heavier. Pricing is quoted per 1 million tokens.
- What is the difference between open-source and closed-source LLMs?
- Open-weights LLMs (Llama 5, Mistral Large 3, DeepSeek V4) publish their weights publicly — you can run them locally or fine-tune them without per-token API costs. Licenses vary: DeepSeek V4 is MIT, Mistral Large 3 is Apache 2.0, and Llama 5 uses Meta's community license. Closed-source models (GPT-5.x, Claude, Gemini) are accessed only through paid APIs.
- What are modalities in the context of LLMs?
- Modalities refer to the types of input a model supports: text (all models), image input (GPT-5.x, Claude, Gemini, Llama 5), audio (GPT-5.5, Gemini 3), and video (Gemini 3 family only among the compared models). Multimodal models process more than one type; DeepSeek V4 remains text-and-code only.