Baidu

ERNIE 3.5

Estimated pricingSmall memory

ERNIE 3.5 is Baidu's small-memory model. This page shows current pricing, an interactive cost calculator, and a side-by-side with similar models.

Input

$0.40/1M tokens

Output

$0.80/1M tokens

Cached

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Batch

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Interactive

Calculate your ERNIE 3.5 bill.

Adjust the workload below and watch the monthly cost update in real time.

What would ERNIE 3.5 cost you?

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1,00010,00050,000250,0001M10M

Technical specifications

ERNIE 3.5 at a glance.

Memory

8,000

tokens

Max reply

2,048

tokens

Memory tier

Small

a few emails or a short document

Tokenizer

default

Released

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Training cutoff

—

Availability

Estimated

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 ERNIE 3.5

  • 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

ERNIE 3.5 — 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, ERNIE 3.5 costs roughly $62 per month. Input is $0.40 /1M tokens and output is $0.80 /1M tokens.
ERNIE 3.5 has a 8,000-token context window (small memory — a few emails or a short document). That means you can fit about 1,500 words of input and history in a single call.
Models in a similar class include nemotron-nano-9b-v2, GPT-4.1 mini, nova-micro-v1. The "Similar models" section below this FAQ links into each.
Open-weight model — price from a common hosting provider (Together, Fireworks, Replicate). We source estimates from the cheapest public hosting provider for that model and note it on the page.

Still unsure?

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