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Ask Session – Parallel Multi-Model LLM Runner
Ask Session – Parallel Multi-Model LLM Runner is a paid API for AI agents from ask-session.withzero.ai, paid per call via MPP, $0.02/call, status unknown (last checked 2026-10-13).
Runs a single prompt against up to 5 LLMs in parallel and returns all responses, token counts, latencies, and costs in one call
Price / call$0.02session · ~$0.05 to open
Success—
StatusUnknown
How this service works
Run a prompt against 1-5 models in parallel. Per-voucher: $0.02. Buyer opens a session channel (recommended deposit $0.05) and signs cumulative vouchers per call. Each call advances the channel by $0.02. Close the session to get unspent deposit refunded. Pre-flight: free `GET /models` lists every supported model with per-million token costs and an estimated per-call cost — use it to choose the `models` array before opening the channel.
Output
Returns an array of per-model objects each containing the model ID, the text response, input/output token counts, latency in milliseconds, and cost in USD. Top-level fields include the original prompt, number of models queried vs succeeded, total latency, and summed token cost across all models.
Example use cases
Compare LLM answers on a coding question
“Ask GPT-4.1, Claude Haiku, and Mistral Large at the same time: 'Write a Python function that checks if a string is a palindrome' — show me all three answers and which one was fastest.”
Multi-model fact-check for research
“Run this question through GPT-4.1, Gemini 2.5 Pro, and DeepSeek V3 simultaneously and tell me if they all agree: 'What year was the Treaty of Westphalia signed and what did it establish?'”
Pick best model for creative writing
“Send the prompt 'Write a two-sentence horror story about a lighthouse' to Claude Sonnet 4.5, Gemini 2.5 Flash, and Llama 3.3 70B at the same time so I can see which model produces the most compelling output.”
Technical details
Endpoint
POSThttps://ask-session.withzero.ai/run?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need to compare outputs from multiple LLMs on the same prompt in a single roundtrip, or when you want to pick the best model for a given task without running sequential calls. It is ideal for benchmarking, quality comparison, consensus checking, or any workflow where seeing 2–5 model responses side-by-side adds value. Prefer it over single-model endpoints when model diversity or redundancy matters more than minimizing cost.
Side effects
- Individual model may fail (error field populated per model) while others succeed — modelsSucceeded < modelsQueried
- Prompt exceeds 10,000 character limit — validation error
- maxTokens outside 50–4096 range — validation error
- Invalid or unrecognized model ID in models array — error per affected model
- Insufficient channel deposit to cover voucher — payment rejected
- Session channel not open or expired — authentication/payment failure
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| models | array | Model IDs to query (1-5). Defaults to GPT-4.1, Claude Sonnet 4.5, Gemini 2.5 Pro. Available: openai/gpt-4.1, openai/gpt-4.1-mini, openai/gpt-4.1-nano, anthropic/claude-opus-4-6, anthropic/claude-sonnet-4-5, anthropic/claude-haiku-4-5, google/gemini-2.5-pro, google/gemini-2.5-flash, meta-llama/llama-3.3-70b-instruct, mistralai/mistral-large, mistralai/mistral-small, deepseek/deepseek-v3, deepseek/deepseek-r1 |
| promptrequired | string | The question or prompt to send to the models |
| maxTokens | number | Max tokens per model response (default: 1024) |
| systemPrompt | string | Optional system prompt to set context for all models |
Output
Schema · Response
| Field | Type | Description |
|---|---|---|
| promptrequired | string | |
| responsesrequired | array | |
| totalCostUsdrequired | number | Estimated raw token cost summed across all models (USD) |
| modelsQueriedrequired | number | |
| totalLatencyMsrequired | number | |
| modelsSucceededrequired | number |
Reviews
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Zero review prompt (fires after every successful call)
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