Evaluate and compare AI models for your applications.
Test models on your own prompts. Compare quality, cost, tokens, and context side by side — then choose the right model before production.
No installation. Free includes model comparison in up to 5 chats with one provider. You'll need your own provider API key — it stays encrypted in your browser and is used directly with the provider.
Review this TypeScript function and suggest the most important improvement:
async function fetchUser(id: string) {
const response = await fetch(`/api/users/${id}`);
return response.json();
}- Check
response.okbefore parsing JSON; otherwise 4xx/5xx responses may be treated as valid data. - Encode the ID to prevent malformed URL paths.
async function fetchUser(id: string) {
const response = await fetch(`/api/users/$${encodeURIComponent(id)}`);
if (!response.ok) throw new Error(`Failed to fetch user: $${response.status}`);
return response.json();
}Most important improvement: error handling. fetch doesn't throw on HTTP errors (404, 500), so failures pass silently.
async function fetchUser(id: string): Promise<User> {
const response = await fetch(`/api/users/$${id}`);
if (!response.ok) {
throw new Error(`Failed to fetch user $${id}: $${response.statusText}`);
}
return response.json() as Promise<User>;
}- Check
response.ok - Add return type for type safety
Send to both models simultaneously… (Shift+Enter for new line)
The problem
Stop Guessing Which AI Model to Use
Public benchmarks tell you which model performs well in general. They do not tell you which model is best for your application, your prompts, your system instructions, and your workload.
01
The Best Model on a Benchmark May Not Be the Best Model for Your Application
A model that wins a public benchmark may perform worse on your own prompts or context.
02
A cheaper model may still be the right model
A newer model may be cheaper — but will it maintain the quality you need?
03
A more expensive model may not be worth it
A more expensive model may perform slightly better — but is the improvement worth the additional cost?
The decision
Which model works best for my application?
Without testing, you are guessing. EidoStack helps you answer the question that actually matters.
Make the decision with evidence.
Compare the same workload
Run the same prompt across supported models under the same test conditions.
Inspect the cost trade-off
See token usage and estimated cost for the workload you are actually testing.
Test prompt behavior
Compare how models react to your system instructions and prompt versions.
Understand context impact
See how prompts and conversation history consume available context and affect cost.
Switching models
Know When Switching Models Is Actually Worth It
New models appear constantly. Some are cheaper. Some are faster. Some claim better benchmark performance. But switching production traffic is not free.
Before you move from one model to another, EidoStack lets you run the same workload across both and compare:
Current modelBaseline
Alternative modelCompare
Instead of asking “Is this model cheaper?” ask: “Is it cheaper while still giving me the result I need?”
Multi-provider workflow
Compare Models Without Switching Between Provider Dashboards
Model evaluation often means opening multiple tools: OpenAI. Anthropic. Google. Different tabs. Different interfaces. The same prompt copied again and again.
Free accounts can compare models from one configured provider in up to five chats. Pro accounts can compare across their configured providers without chat or message limits.
01
One workspace
EidoStack brings those evaluations into one place.
02
Same conditions
Run the same prompt across supported models and inspect the outputs side by side under the same test conditions.
03
Less manual work
Less tab switching. Less manual comparison. More consistent evaluation.
Real workloads
Test Your Own Workload — Not a Generic Leaderboard
A leaderboard cannot reproduce your application.
Your user prompts
Evaluate the inputs your users actually send.
Your system prompt
See how your instructions behave across different models.
Your conversation structure
Evaluate the interaction pattern your application really uses.
Your context strategy
Compare the impact of different context configurations.
Your model parameters
Test under the conditions your application will actually use.
Your application requirements
Make the decision against the constraints that matter to your product.
Your prompts. Your workload. Your decision.
Prompt regression
Know Whether a Prompt Change Actually Improved Your Application
Prompt changes are easy to make. They are much harder to evaluate properly.
A new system prompt may produce a better answer in one test and create worse behavior somewhere else.
It may also increase token usage, consume more context, or behave differently across models.
Output behavior
Compare how the response changes.
Quality
Inspect whether the change actually produces a better result.
Token usage
See whether a prompt change increases consumption.
Estimated cost
Understand the financial effect of the change.
Context consumption
See whether the new prompt consumes more of the context window.
Stop deciding whether a prompt is “better” by looking at one response.
Test it. Compare it. Keep the result.
System prompts
Experiment With System Prompts Across Models
The same system prompt can behave differently across OpenAI, Anthropic, and Google models.
Instead of tuning a prompt for one model and assuming it will behave the same elsewhere, test it directly.
Different system instructions
Test instruction changes directly instead of assuming behavior will carry across providers.
Different prompt versions
Compare prompt revisions under consistent conditions.
Different conversation strategies
Evaluate the interaction structure used by your application.
Different context configurations
Find the combination that works for your use case.
Cost visibility
Understand the Real Cost of Each Model
Token pricing alone does not tell you what your application will cost.
Different models may consume different amounts of input and output tokens for the same workload. Your context strategy can change the cost again.
Input token usage
See how much input each evaluation consumes.
Output token usage
Compare output consumption across models.
Estimated model cost
Understand the approximate cost of the tested workload.
Context consumption
See how context strategy affects usage and cost.
What does this model actually cost for my workload?
Context
Understand What Is Consuming Your Context Window
Context is not just a technical limit.
Cost
More context can mean more input tokens and higher inference cost.
Available conversation history
See how much of the model's window your conversation consumes.
Model behavior
Understand context as a variable that can change responses.
Response quality
Compare strategies rather than treating context as invisible infrastructure.
Application scalability
See how context decisions can affect the economics of your application.
Intentional context decisions
Compare different context strategies and see how they affect both response and cost.
Experiment history
Never Lose the Experiment That Led to Your Decision
Model evaluation is rarely one test.
Without a structured history, it becomes difficult to remember:
- What you tested
- Which prompt version you used
- Which model performed better
- What it cost
- Why you made the final decision
Your model decision should not disappear into browser tabs and temporary notes.
Keep the evidence behind it.
One workspace
One Workspace for AI Model Evaluation
Compare Models
Run the same prompt across different AI models and inspect the results side by side.
Evaluate Prompt Changes
Test system prompts and prompt versions under consistent conditions.
Analyze Costs
See token usage and estimated spending across your model evaluations.
Understand Context Usage
Visualize how prompts and conversation history consume the available context window.
Preserve Your Experiments
Return to previous tests instead of recreating them from memory.
Work Across Providers
Evaluate supported models from OpenAI, Anthropic, and Google in one place.
BYOK security
Bring Your Own API Keys Without Giving Them Away
Using an evaluation platform should not require giving permanent access to your provider credentials.
For normal model usage, EidoStack uses a client-side encrypted API-key vault.
Your keys are encrypted in the browser using the Web Crypto API.
- Your provider accounts
- Your API credentials
- Your inference spending
Use EidoStack as the evaluation workspace without turning it into the owner of your AI infrastructure.
Who it is for
Built for AI Engineers Making Real Production Decisions
AI Engineers
LLM Developers
AI Startups
Prompt Engineers
AI Consultants
Teams evaluating models before production
Whether you are building an AI agent, chatbot, coding assistant, internal tool, or AI-powered SaaS, the core questions are the same:
EidoStack helps you answer those questions with your own workload.
Decision history
From “This Model Feels Better” to “We Know Why We Chose It”
Without structured evaluation
Decide by feeling
“This one seems better.”
With EidoStack
Decide with evidence
“This model performs well on our prompts, costs less for our workload, uses fewer tokens, behaves correctly with our system prompt, and fits our context requirements.”
That is the difference between experimenting with AI and making an engineering decision.
Pricing
Start evaluating without changing your AI stack.
Use your own provider keys. Start free, then upgrade when EidoStack becomes part of your regular evaluation workflow.
Free
Evaluate AI models with your own API key, inspect token and cost usage, understand context usage, and experience the core EidoStack workflow within these limits.
- Maximum configured API providers: 1
- Maximum chats: 5
- Maximum messages per chat: 50
- Compare models in up to 5 chats
- Inspect token usage and costs
- Understand context usage
Pro
Save with annual billing
Compare AI models, analyze costs and token usage, and unlock the full EidoStack workflow.
- Unlimited API providers
- Unlimited chats
- Unlimited messages per chat
- Compare models across configured providers
- Full access to EidoStack functionality
- Analyze costs, token usage, and context usage
Billed annually. Full access. Cancel anytime.
Make the decision with evidence
Stop Guessing. Start Evaluating.
Test AI models on your own workload. Compare quality, cost, tokens, prompts, and context. Keep the evidence behind your decision. Choose the model that makes sense before you ship it to production.
Start FreeYou'll need your own provider API key. It's stored in an encrypted browser vault and used directly with the provider. No installation required.