Aion Labs: Aion-1.0

Public pricingIntelligence 79/100Medium memoryRaciocínio profundo

AionLabs: Aion-1.0 é um modelo texto criado para programação, depuração e trabalho técnico. Ele combina bom desempenho em programação e raciocínio e planejamento profundos, um contexto de 131K tokens e um perfil premium para entregar trabalho confiável em coding, debugging, and technical writing.

Input

$4.00/1M

Output

$8.00/1M

Cached

$0.40/1M

Batch

$2.00/1M

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Technical specifications

Aion-1.0 at a glance.

Memory

131,072

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-1.0

  • Multi-step reasoning, research agents, or hard math.

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-1.0 — 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-1.0 costs roughly $620 per month. Input is $4.00 /1M tokens and output is $8.00 /1M tokens.
Aion-1.0 has a 131,072-token context window (medium memory — a long report or a codebase file). That means you can fit about 24,576 words of input and history in a single call.
Beyond text generation, Aion-1.0 supports deep step-by-step reasoning, strict JSON output. It streams replies by default.
Aion-1.0 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, Claude 3.7 Sonnet (thinking). The "Similar models" section below this FAQ links into each.

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