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ERNIE X1.1

nano-gpt/ernie-x1-1-preview

من nano-gpt · أُصدِر 2025-09-10

$0.150
الإدخال / 1M رمز
$0.600
الإخراج / 1M رمز
64K
نافذة السياق
8K
أقصى إخراج

Prices in USD per 1M tokens. Unknown means the provider does not publish per-token pricing.

القدرات

استدعاء الأدواتتفكيرإخراج منظمالمرفقاتأوزان مفتوحة? التحكم بالحرارة
الوسائط المدعومة: إدخال text, pdf · إخراج text

Model fit scores

0–100 · higher is better

These scores reward declared capabilities, context size, price and provider availability — they are not benchmark results. Use them as a directional signal alongside your own evaluation.

Coding1
  • Tool calling0/40
  • Structured output0/20
  • Reasoning0/10
  • Context window (100K → 1M)0/20
  • Provider availability1/10
Agents6
  • Tool calling0/35
  • Structured output0/25
  • Reasoning0/15
  • Output token limit5/15
  • Provider availability1/10
JSON / structured output19
  • Structured output / JSON mode0/50
  • Tool calling0/20
  • Temperature control0/10
  • Price-friendly for high-volume19/20
Cost efficiency65
  • Headline price (log-scaled)65/95
  • Has prompt-cache pricing0/5
Long context0
  • Context ≥ 100K0/100
Production-readiness65
  • Number of independent providers5/40
  • Has published per-token price20/20
  • Context window ≥ 8K15/15
  • No data inconsistencies across providers10/10
  • Official model (not derivative)15/15

Cost Efficiency Index

Open full calculator →

Estimated cost using the recommended provider's headline rate. Each scenario fixes average input/output tokens — the assumptions are shown in the third column.

ScenarioCostAssumption
RAG answer
per 1,000 RAG answers
$1.05
< $0.01 per request
5K input tokens (query + 4 retrieved chunks of ~1K each) and a 500-token answer. Typical SaaS knowledge-base bot.
Support ticket triage
per 10,000 tickets
$2.10
< $0.01 per request
1K input tokens (ticket body + system prompt) and a 100-token JSON classification reply. High-volume customer support.
Data extraction
per 1,000 documents
$0.60
< $0.01 per request
2K input tokens (a single document page) and a 500-token JSON extraction. ETL / invoice / form pipelines.
Code review
per 1,000 PRs
$1.80
< $0.01 per request
8K input tokens (diff + surrounding files) and a 1K-token review comment. PR-bot workloads.
Agent step
per 1,000 steps
$2.16
< $0.01 per request
12K input tokens (long-running tool history) and a 600-token tool-call decision. Cost per agent step.

تفاصيل التسعير

السعر المُوصى به من nano-gpt · ernie-x1.1-preview

$0.150
إدخال
$0.600
إخراج

متاح لدى 1 مزود

المزودمعرف نموذج المزودإدخال / 1Mإخراج / 1Mالسياقتاريخ الإصدار
NanoGPT
nano-gpt
ernie-x1.1-preview$0.150$0.60064K2025-09-10

Frequently asked questions

How much does ERNIE X1.1 cost?

ERNIE X1.1 costs $0.150 per 1M input tokens and $0.600 per 1M output tokens, sourced from nano-gpt. Cache reads, audio tokens and >200K-context tiers (where applicable) are listed in the Pricing detail block above.

What is the context window of ERNIE X1.1?

ERNIE X1.1 has a context window of 64K tokens, with a max output of 8K tokens per reply. This is the total combined size of prompt + completion.

Does ERNIE X1.1 support tool calling?

No. ERNIE X1.1 does not support tool calling (function calling). If your workflow requires it, look at the /capabilities/tool-calling list for alternatives.

Does ERNIE X1.1 support structured output / JSON mode?

No. ERNIE X1.1 does not support structured output / JSON-schema-constrained decoding. If your workflow requires it, look at the /capabilities/structured-output list for alternatives.

Can ERNIE X1.1 accept image input?

No. ERNIE X1.1 only accepts text, pdf as input. If you need image input, see our /capabilities/vision list for current vision-capable models.

Is ERNIE X1.1 open-weight?

No. ERNIE X1.1 is a proprietary model — only nano-gpt (and any approved hosting partners) can serve it. The pricing above reflects the cheapest API access.

What are the best alternatives to ERNIE X1.1?

If ERNIE X1.1 doesn't fit, consider Brave (Answers), Exa (Research), Auto model (Basic). Each one targets the same use case — see the Related links below for direct head-to-head pages.

Where does this data come from?

All numbers come from the public models.dev API and are normalised into a single canonical model record. We re-pull daily and write any changes (price, context, capability) to the /changelog page.

More nano-gpt models

آخر تحديث:

Data is sourced from models.dev and normalized for comparison. Prices and capabilities may change. Always verify critical production decisions with the provider's official documentation.