The AI Model Comparison tool puts flagship language models side by side on the numbers that drive an adoption decision: how much they cost at your token volume, how much context they can hold, and what each is known for. Pick any two of the built-in models and a token volume, and it returns their pricing, context limits, strengths, and the comparison cost for each at that volume.
The Models Compared
| Model | Context window | Input (per 1M) | Output (per 1M) | Known for | |---|---|---|---|---| | GPT-4o | 128,000 tokens | 10.00 | Broad reasoning and multimodal tasks | | Claude 3.5 Sonnet | 200,000 tokens | 15.00 | Long-context writing and coding | | Gemini 1.5 Pro | 1,000,000 tokens | 5.00 | Very large context analysis |
How the Cost Comparison Works
For each model, the comparison charges your token volume once at the model's input rate and once at its output rate, and adds the two:
That is a deliberately symmetric yardstick: it assumes a workload that both reads and writes at the volume you specify, so no model gains an advantage from your traffic happening to be read-heavy or write-heavy. At the default 1,000,000 tokens, the yardstick gives GPT-4o 18.00, and Gemini 1.5 Pro 125, 62.50 — the ratios never change, because the comparison is linear.
Real bills differ from the yardstick: outputs are priced 3 to 5 times higher than inputs on every model here, so a generation-heavy workload (long documents out, short prompts in) costs more than the yardstick suggests, and an analysis-heavy workload (long documents in, short answers out) costs less. For an exact bill at a known input/output split, use the LLM API cost calculator; use this tool to rank models before you get there.
Reading the Other Columns
Context window is the model's working memory — prompt, documents, and reply together must fit inside it. Gemini 1.5 Pro's million-token window swallows entire codebases or book-length documents in one request; the 128,000-token window of GPT-4o covers most document and chat workloads; Claude's 200,000 sits between, paired with the writing and coding strengths it is known for. Strengths are qualitative summaries, not benchmark scores — the right way to use them is to shortlist, then test your actual prompts on the finalists.
Limitations
Pricing and the model list are a stored snapshot of three flagship models from the three major providers; the market moves monthly, mini and budget tiers are not included, and no benchmark scores are shown — by design, since published benchmarks predict real-task performance poorly compared with testing your own workload. The comparison also prices a single request pattern; batch discounts, caching discounts, and fine-tuning costs sit outside it.
Frequently Asked Questions
Why is the cheapest model not automatically the best choice?
Because cost per token is not cost per successful task. A model that needs two retries, longer prompts, or human cleanup on your workload can cost more in total than a pricier model that succeeds first time. Use the comparison to rank raw price, then validate quality on a sample of your real requests.
How do I choose between context window sizes?
Add up what a single request must hold: system prompt, the documents or history you attach, and the reply you expect. If that fits in 128,000 tokens (roughly 96,000 words), every model here qualifies and price decides. If you routinely attach entire repositories or archives, the million-token window removes chunking and retrieval complexity entirely.
Why must the two models be different?
The tool is a comparison, so selecting the same model twice returns an error prompting you to choose two different models. If you want a single model's cost at several volumes, the LLM API cost calculator covers that directly.