Undi95: ReMM SLERP 13B

Public pricingIntelligence 79/100Small memory

ReMM SLERP 13B est un modèle texte conçu pour conversation générale, analyse et usage en production. Il associe des performances générales stables, un contexte de 6K tokens et un profil à coût équilibré pour un travail fiable sur general chat, analysis, and production workloads.

Input

$0.45/1M

Output

$0.65/1M

Cached

$0.11/1M

Batch

$0.23/1M

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What would ReMM SLERP 13B cost you?

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Technical specifications

ReMM SLERP 13B at a glance.

Memory

6,144

tokens

Max reply

4,096

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 ReMM SLERP 13B

  • 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

ReMM SLERP 13B — 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, ReMM SLERP 13B costs roughly $60 per month. Input is $0.45 /1M tokens and output is $0.65 /1M tokens.
ReMM SLERP 13B has a 6,144-token context window (small memory — a few emails or a short document). That means you can fit about 1,152 words of input and history in a single call.
ReMM SLERP 13B was released in November 2023, with training data cut off around July 2023.
Models in a similar class include Kimi K2.5, Llama Guard 3 8B, ERNIE 4.5 VL 424B A47B. The "Similar models" section below this FAQ links into each.

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