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.
| Qwen-Image-2.1-Pro alibaba/qwen-image-2.1-pro | Pareto unbiased/pareto | |
|---|---|---|
| Provider | Alibaba | Unbiased |
| Family | ||
| Modality | image | text |
| Context window | — | 262,144 tok |
| Max output | — | 32,768 tok |
| Released | 2026-10-10 | 2026-09-17 |
| Input price | $0.040 /img | $2.50 /1M |
| Output price | — | $7.50 /1M |
| Cache read | — | — |
| Tools | — | — |
| Streaming | — | 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.
# 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 Qwen-Image-2.1-Pro
client.chat.completions.create(
model="alibaba/qwen-image-2.1-pro",
messages=[{"role":"user","content":"hello"}],
)
# Try Pareto — same client, same key
client.chat.completions.create(
model="unbiased/pareto",
messages=[{"role":"user","content":"hello"}],
)