# Compare LLM Models — Price, Context Window and Capability

> Every current model side by side: price per million tokens, context window, max output, modalities and cache rates.

This table compares 25 current models from 7 providers on the specifications that decide a choice: price per million input and output tokens, cached-input rate, context window, maximum output, supported modalities and tokeniser. It ranks them by the cost of a workload with your own input-to-output ratio, because a summarisation workload and a generation workload rank models in a completely different order.

**URL:** https://convertto.tech/t/llm-model-comparison
**Category:** AI & LLM Tools (https://convertto.tech/c/ai-tools)
**Privacy:** Runs entirely in the browser; no upload
**Cost:** Free, no sign-up
**Last updated:** 2026-08-01

## Key facts

- **Models compared:** 25
- **Prices verified:** 2026-08-01
- **Typical output premium:** 4–5× the input rate
- **Privacy:** Runs entirely in your browser — nothing is uploaded
- **Cost:** Free, unlimited, no sign-up

## How to use

1. Select one or more providers.
2. Choose the sort by.
3. Turn "Hide embedding models" on or off as needed.
4. Set the typical input.
5. Set the typical output.
6. The result appears immediately — copy or download it.

## FAQ

### Which model is cheapest?

It depends entirely on your input-to-output ratio, which is why this tool asks for it. Output costs four to five times input, so a model with cheap input and expensive output wins on summarisation and loses on generation. Sorting by a single price column will mislead you.

### Why compare context windows if they are all a million now?

Because the smaller and cheaper tiers are not, and because filling a large window is expensive even when it is allowed. A million-token window at $3 per million costs $3 a call to use fully — the window tells you what is possible, the price tells you what is affordable.

### Are open-weight models really cheaper?

On a hosted API, often yes. Self-hosted, the licence is free and you pay in GPU time and operational work instead — which is only cheaper above a fairly high, steady volume. The VRAM calculator sizes the hardware side of that decision.

## Sources

- [Anthropic pricing](https://platform.claude.com/docs/en/pricing) — Anthropic
- [OpenAI pricing](https://openai.com/api/pricing/) — OpenAI
- [Google pricing](https://ai.google.dev/gemini-api/docs/pricing) — Google
- [Meta pricing](https://llama.developer.meta.com/docs/pricing/) — Meta
- [Mistral AI pricing](https://mistral.ai/pricing) — Mistral AI
- [DeepSeek pricing](https://api-docs.deepseek.com/quick_start/pricing) — DeepSeek
- [xAI pricing](https://docs.x.ai/docs/models) — xAI

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