Mancer: Weaver (alpha)

Public pricingIntelligence 79/100Small memory

Mancer: Weaver (alpha) es un modelo texto pensado para chat general, análisis y uso en producción. Combina un rendimiento general estable, un contexto de 8K tokens y un perfil costo equilibrado para trabajar con fiabilidad en general chat, analysis, and production workloads.

Input

$0.75/1M

Output

$1.00/1M

Cached

$0.15/1M

Batch

$0.75/1M

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What would Weaver (alpha) cost you?

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Technical specifications

Weaver (alpha) at a glance.

Memory

8,000

tokens

Max reply

2,000

tokens

Memory tier

Small

a few emails or a short document

Tokenizer

llama2

Released

Nov 2023

Training cutoff

Jul 2023

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 Weaver (alpha)

  • 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.
  • Your inputs routinely exceed short documents.

Alternatives

Similar models to compare.

FAQ

Weaver (alpha) — 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, Weaver (alpha) costs roughly $96 per month. Input is $0.75 /1M tokens and output is $1.00 /1M tokens.
Weaver (alpha) has a 8,000-token context window (small memory — a few emails or a short document). That means you can fit about 1,500 words of input and history in a single call.
Weaver (alpha) was released in November 2023, with training data cut off around July 2023.
Models in a similar class include Virtuoso Large, GPT-5.4 Mini, Qwen3 Max. The "Similar models" section below this FAQ links into each.

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