Relace: Relace Search

Public pricingIntelligence 79/100Large memory

Relace: Relace Search is een tekst-model voor coderen, software engineering en agent-workflows. Het combineert sterke codeprestaties en betrouwbaar toolgebruik en agentgedrag, een context van 256K tokens en een evenwichtig geprijsd-profiel voor betrouwbaar werk in coderen, software engineering en agent-workflows.

Input

$1.00/1M

Output

$3.00/1M

Cached

$0.05/1M

Batch

$0.25/1M

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What would Relace Search cost you?

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Technical specifications

Relace Search at a glance.

Memory

256,000

tokens

Max reply

128,000

tokens

Memory tier

Large

an entire book or large codebase

Tokenizer

—

Released

Sep 2025

Training cutoff

Jun 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 Relace Search

  • Long documents, full codebases, or extensive chat histories.

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

Relace Search — 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, Relace Search costs roughly $195 per month. Input is $1.00 /1M tokens and output is $3.00 /1M tokens.
Relace Search has a 256,000-token context window (large memory — an entire book or large codebase). That means you can fit about 48,000 words of input and history in a single call.
Relace Search was released in September 2025, with training data cut off around June 2025.
Models in a similar class include Relace Apply 3, Claude Haiku 4.5, MiMo-V2-Pro. The "Similar models" section below this FAQ links into each.

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