Morph: Morph V3 Fast

Public pricingIntelligence 79/100Medium memory

Morph: Morph V3 Fast ist ein Text-Modell für Programmierung, Debugging und technische Arbeit. Es verbindet starke Coding-Leistung und geringe Latenz und effiziente Inferenz, einen Kontext von 82K tokens und ein ausgewogen-Profil für zuverlässige Arbeit über coding, debugging, and technical writing.

Input

$0.80/1M

Output

$1.20/1M

Cached

$0.08/1M

Batch

$0.40/1M

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Technical specifications

Morph V3 Fast at a glance.

Memory

81,920

tokens

Max reply

38,000

tokens

Memory tier

Medium

a long report or a codebase file

Tokenizer

—

Released

Sep 2025

Training cutoff

May 2025

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 Morph V3 Fast

  • High-volume workloads where unit cost matters.
  • Code generation, review, or refactoring.

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

Morph V3 Fast — 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, Morph V3 Fast costs roughly $108 per month. Input is $0.80 /1M tokens and output is $1.20 /1M tokens.
Morph V3 Fast has a 81,920-token context window (medium memory — a long report or a codebase file). That means you can fit about 15,360 words of input and history in a single call.
Morph V3 Fast was released in September 2025, with training data cut off around May 2025.
Models in a similar class include Morph V3 Large, CodeLLaMa 7B Instruct Solidity, GPT-5.4 Mini. The "Similar models" section below this FAQ links into each.

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