Prompt Analyzer
Score a prompt on the parts that change the answer, and see what is missing.
LLM Model Comparison
Compare GPT, Claude, Gemini, DeepSeek and Llama by price, context and monthly cost.
Quick Tool Overview
The LLM Model Comparison table lines up models from OpenAI, Anthropic, Google, DeepSeek, Mistral and hosted Llama by context window, output limit and token prices, then calculates the per-request and monthly cost of your own traffic for each one.
- What it does
- Compares LLM prices and limits side by side
- You give it
- Your monthly requests and tokens per request
- You get back
- A sortable table with your monthly cost per model
- Best for
- Choosing a model and forecasting an API budget
About the LLM Model Comparison
Model pricing pages list a price per million input tokens and a different one for output, which makes comparing providers harder than it should be: a model that is cheap to read can be expensive to write, and the one that wins depends entirely on the shape of your requests.
This table puts current models from OpenAI, Anthropic, Google, DeepSeek, Mistral and hosted Llama side by side with their context window, maximum output, input and output prices. Enter your own traffic — requests a month, tokens in and tokens out per request — and every row gains a per-request and a monthly cost, so the ranking reflects your workload rather than a headline rate.
Filter by provider, minimum context window or reasoning models, and sort by monthly cost, input price, output price or window size. Prices change often, so treat the figures as a starting point and confirm them on the provider pricing page; batch APIs and prompt caching can cut the real bill substantially.
Key features of the LLM Model Comparison
Costs for your workload
Input and output prices are applied to your own traffic, not a headline rate.
Filter and sort
By provider, minimum context window, reasoning models, price or monthly cost.
Cheapest match highlighted
The lowest monthly cost is marked, with how many times cheaper it is than the dearest.
Output-heavy hints
Tells you when output price will dominate your bill.
Transparent prices
List prices are shown as a starting point with a clear reminder to confirm them.
How to use the LLM Model Comparison
-
1
Describe your traffic
Requests per month, and the typical input and output tokens of each request.
-
2
Filter the models
Narrow by provider, minimum context window, or reasoning models only.
-
3
Sort the table
By monthly cost to find the cheapest, or by context window or output length.
-
4
Shortlist and test
Take the cheapest few that fit, and run them against your own examples before switching.
Frequently asked questions
Generating a token requires a full pass through the model for each one, while input tokens are processed together in parallel. Providers price output several times higher to match.
It depends on your requests. Small models such as GPT-5 nano, Gemini Flash and hosted Llama 8B are the cheapest per token, but a cheaper model that needs retries or longer prompts can cost more per finished task.
They are list prices used as a starting point. Providers change prices and release models frequently, so confirm the current rate on the provider pricing page before committing a budget.
Cache repeated prompt prefixes, use batch APIs for work that does not need an instant answer, trim system prompts and retrieved context, and route simple requests to a smaller model.
You pay for the tokens you send, not the size of the window. Some providers charge a higher rate once a single request passes a length threshold.
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