OpenRouter: Elephant

Public pricingIntelligence 79/100Large memory

Elephant は テキスト モデルで、汎甚チャット、分析、業務利甚 に向いおいたす。䜎遅延で効率的な掚論、262K tokensのコンテキスト、無料たたは䜎コストの特性を組み合わせ、general chat, analysis, and production workloads で安定した動䜜を支えたす。遅延・コスト・スルヌプット が重芁な堎面に合っおおり、安定した出力、柔軟な導入、拡匵性を求めるチヌムに実甚的です。 安定した応答、長い文脈凊理、そしお詊䜜から本番たで広く䜿える柔軟さが必芁な堎面で圹立ちたす。 安定した応答、長い文脈凊理、そしお詊䜜から本番たで広く䜿える柔軟さが必芁な堎面で圹立ちたす。 安定した応答、長い文脈凊理、そしお詊䜜から本番たで広く䜿える柔軟さが必芁な堎面で圹立ちたす。

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What would Elephant cost you?

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1,00010,00050,000250,0001M10M

Technical specifications

Elephant at a glance.

Memory

262,144

tokens

Max reply

32,768

tokens

Memory tier

Large

an entire book or large codebase

Tokenizer

—

Released

Apr 2026

Training cutoff

—

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 Elephant

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

Alternatives

Similar models to compare.

FAQ

Elephant — 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

Elephant has a 262,144-token context window (large memory — an entire book or large codebase). That means you can fit about 49,152 words of input and history in a single call.
Elephant was released in April 2026.
Models in a similar class include Auto Router, Free Models Router, Gemma 4 26B A4B. The "Similar models" section below this FAQ links into each.

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