Pricing, context window, capabilities, and release date — pulled from each provider's public docs. Both are available via the same AIgateway OpenAI-compatible endpoint; flip the model string to switch.
Both models stream in parallel through your own AIgateway key. Tokens, latency, and cost update as they arrive.
| Pareto unbiased/pareto | Jev 1.14 typesafe/jev-1.14.0 | |
|---|---|---|
| Provider | Unbiased | Typesafe |
| Family | ||
| Modality | text | text |
| Context window | 262,144 tok | 32,000 tok |
| Max output | 32,768 tok | 32,000 tok |
| Released | 2026-09-17 | 2026-10-16 |
| Input price | $2.50 /1M | $0.042 /1M |
| Output price | $7.50 /1M | $0.0000 /1M |
| Cache read | — | — |
| Tools | — | — |
| Streaming | yes | yes |
| Vision | — | — |
| JSON mode | — | — |
| Reasoning | — | — |
| Prompt caching | — | — |
Pareto is Unbiased's blended AI model. It engages multiple language models in parallel for each request, synthesizes one answer, and supports text and vision inputs through a single API response.
Jev 1.14 is TypeSafe's structured evaluation model. It evaluates one state against typed Noul, Choice, Score, and Box questions and returns calibrated answers with probabilities and confidence. Image inputs and Box questions are beta and dreamer-ready, not recommended for production.
# pip install aigateway-py openai
# aigateway-py: sub-accounts, evals, replays, jobs, webhook verify.
# openai SDK: chat/embeddings/images/audio — drop-in compat per our SDK's own guidance.
from openai import OpenAI
client = OpenAI(
base_url="https://api.aigateway.sh/v1",
api_key="sk-aig-...",
)
# Try Pareto
client.chat.completions.create(
model="unbiased/pareto",
messages=[{"role":"user","content":"hello"}],
)
# Try Jev 1.14 — same client, same key
client.chat.completions.create(
model="typesafe/jev-1.14.0",
messages=[{"role":"user","content":"hello"}],
)