Anthracite: Magnum v4 72B

Public pricingIntelligence 79/100Small memory

Magnum v4 72B é um modelo texto criado para chat geral, análise e uso em produção. Ele combina desempenho geral estável, um contexto de 16K tokens e um perfil premium para entregar trabalho confiável em general chat, analysis, and production workloads.

Input

$3.00/1M

Output

$5.00/1M

Cached

$0.30/1M

Batch

$1.50/1M

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What would Magnum v4 72B cost you?

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Technical specifications

Magnum v4 72B at a glance.

Memory

16,384

tokens

Max reply

2,048

tokens

Memory tier

Small

a few emails or a short document

Tokenizer

qwen

Released

Oct 2024

Training cutoff

Jun 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 Magnum v4 72B

  • Magnum v4 72B fits general-purpose text tasks within its memory tier.

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

Magnum v4 72B — 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, Magnum v4 72B costs roughly $425 per month. Input is $3.00 /1M tokens and output is $5.00 /1M tokens.
Magnum v4 72B has a 16,384-token context window (small memory — a few emails or a short document). That means you can fit about 3,072 words of input and history in a single call.
Magnum v4 72B was released in October 2024, with training data cut off around June 2024.
Models in a similar class include Claude 3.7 Sonnet, Claude 3.7 Sonnet (thinking), Claude Sonnet 4. The "Similar models" section below this FAQ links into each.

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