NOUMENOpen app

baidu · Balanced

ERNIE 5.1

Added May 10, 2026

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Use in the app

ERNIE 5.1 is the latest Wenxin model, with broad upgrades to agentic workflows, knowledge, reasoning, and deep search. ⚠️ Note: privacy and logging guarantees may be limited.

Input
Text
Output
Text
Discovery tier
Balanced

Pricing

USD per 1M tokens
Input
$0.75
Output
$3.00
Cache read
$0.75

Reference prices as published by the model provider. API example

Specifications

Context window
119K
Max output
64K
Average output (7 days)
1.9K tokens
Longer than 90% of 468 models

Side by side

ERNIE 5.1 compared

Specifications, prices, speed and benchmark scores next to each other. Benchmarks below show every compared model too.

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ERNIE 5.1baiduThis modelDoubao Seed 2.0 Code PreviewdoubaoRemove
Providerbaidudoubao
Discovery tierBalancedBalanced
AddedMay 10, 2026Feb 14, 2026
Limits
Context window119K256KBest
Max output64K128KBest
Average output1.9K tokens—
Pricing, USD per 1M tokens
Input$0.75Best$0.78
Output$3.00Best$3.89
Cache read$0.75—
Capabilities
Reasoning effort——

Public evaluations

Benchmarks

Independent evaluation results, with how each score ranks among the models in the Noumen catalog.

  • ERNIE 5.1
  • Doubao Seed 2.0 Code Preview
LeaderboardERNIE 5.1Doubao Seed 2.0 Code Preview
Search Overall#9 · 1226.7Not in top 20

Ranks among Noumen catalog models on LMArena leaderboards. Only top-20 placements are listed.LMArena

Developers

Use it through the API

Noumen’s API is OpenAI-compatible: any Chat Completions client works with a base URL and a key.

Read the quickstart
curl https://www.noumen.xyz/api/v1/chat/completions \
  -H "Authorization: Bearer $NOUMEN_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "ernie-5.1",
    "messages": [{ "role": "user", "content": "Explain this stack trace." }]
  }'
import os
from openai import OpenAI

client = OpenAI(base_url="https://www.noumen.xyz/api/v1", api_key=os.environ["NOUMEN_API_KEY"])
reply = client.chat.completions.create(
    model="ernie-5.1",
    messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)

baidu

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