Aion Labs: Aion-RP 1.0 (8B)

Public pricingIntelligence 79/100Medium memory

AionLabs: Aion-RP 1.0 (8B)은 일반 대화, 분석, 운영 환경에 맞춘 텍스트 모델입니다. 안정적인 범용 성능, 33K tokens의 컨텍스트, 균형형 비용 특성을 결합해 general chat, analysis, and production workloads에서 안정적인 작업을 돕습니다. 특히 품질, 속도, 비용가 중요한 경우에 잘 맞으며, 안정적인 출력, 유연한 배포, 확장성을 중시하는 팀에 실용적입니다. 안정적인 응답, 넓은 문맥 처리, 그리고 시제품부터 운영까지 이어지는 유연성이 필요할 때 유용합니다. 안정적인 응답, 넓은 문맥 처리, 그리고 시제품부터 운영까지 이어지는 유연성이 필요할 때 유용합니다.

Input

$0.80/1M

Output

$1.60/1M

Cached

$0.02/1M

Batch

$0.10/1M

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What would Aion-RP 1.0 (8B) cost you?

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Technical specifications

Aion-RP 1.0 (8B) at a glance.

Memory

32,768

tokens

Max reply

32,768

tokens

Memory tier

Medium

a long report or a codebase file

Tokenizer

—

Released

Feb 2025

Training cutoff

Oct 2024

Availability

Public pricing

Status

active

What it can do

Capabilities & limits.

  • Understands images
  • Deep step-by-step thinking
  • Uses tools / calls functions
  • Strict JSON output
  • Streams replies
  • Fine-tunable on your data

When to pick Aion-RP 1.0 (8B)

  • High-volume workloads where unit cost matters.

When to look elsewhere

  • Your workload involves images — pick a vision-capable model instead.
  • You need tool-use / function calling for agent workflows.

Alternatives

Similar models to compare.

FAQ

Aion-RP 1.0 (8B) — the questions we see most.

Pricing, capabilities, alternatives — generated from the same data that powers the calculator above.

Get instant answers from our AI agent

At a typical workload of 50,000 conversations a month with 1,500-token prompts and 800-token replies, Aion-RP 1.0 (8B) costs roughly $124 per month. Input is $0.80 /1M tokens and output is $1.60 /1M tokens.
Aion-RP 1.0 (8B) has a 32,768-token context window (medium memory — a long report or a codebase file). That means you can fit about 6,144 words of input and history in a single call.
Aion-RP 1.0 (8B) was released in February 2025, with training data cut off around October 2024.
Models in a similar class include Aion-2.0, Aion-1.0-Mini, Aion-1.0. The "Similar models" section below this FAQ links into each.

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