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GTE Large (v1.5)

digitalocean/gte-large-en-v1-5

من digitalocean · العائلة: text-embedding · أُصدِر 2024-03-27

$0.090
الإدخال / 1M رمز
Unknown
الإخراج / 1M رمز
8K
نافذة السياق
1K
أقصى إخراج

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

القدرات

✗ استدعاء الأدوات✗ تفكير? إخراج منظم✗ المرفقات✓ أوزان مفتوحة✗ التحكم بالحرارة
الوسائط المدعومة: إدخال text · 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
Agents1
  • Tool calling0/35
  • Structured output0/25
  • Reasoning0/15
  • Output token limit0/15
  • Provider availability1/10
JSON / structured output20
  • Structured output / JSON mode0/50
  • Tool calling0/20
  • Temperature control0/10
  • Price-friendly for high-volume20/20
Cost efficiency88
  • Headline price (log-scaled)88/95
  • Has prompt-cache pricing0/5
Long context0
  • Context ≥ 100K0/100
Production-readiness58
  • Number of independent providers5/40
  • Has published per-token price20/20
  • Context window ≥ 8K8/15
  • 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
$0.45
< $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
$0.90
< $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
$0.18
< $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
$0.72
< $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
$1.08
< $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 digitalocean · gte-large-en-v1.5

$0.090
إدخال
Unknown
إخراج

متاح لدى 1 مزود

المزودمعرف نموذج المزودإدخال / 1Mإخراج / 1Mالسياقتاريخ الإصدار
DigitalOcean
digitalocean
gte-large-en-v1.5$0.090Unknown8K2024-03-27

Frequently asked questions

How much does GTE Large (v1.5) cost?

GTE Large (v1.5) costs $0.090 per 1M input tokens and Unknown per 1M output tokens, sourced from digitalocean. Cache reads, audio tokens and >200K-context tiers (where applicable) are listed in the Pricing detail block above.

What is the context window of GTE Large (v1.5)?

GTE Large (v1.5) has a context window of 8K tokens, with a max output of 1K tokens per reply. This is the total combined size of prompt + completion.

Does GTE Large (v1.5) support tool calling?

No. GTE Large (v1.5) does not support tool calling (function calling). If your workflow requires it, look at the /capabilities/tool-calling list for alternatives.

Does GTE Large (v1.5) support structured output / JSON mode?

Support for structured output / JSON-schema-constrained decoding is not reported for GTE Large (v1.5) in our data source. Verify with digitalocean's official documentation before relying on it in production.

Can GTE Large (v1.5) accept image input?

No. GTE Large (v1.5) only accepts text as input. If you need image input, see our /capabilities/vision list for current vision-capable models.

Is GTE Large (v1.5) open-weight?

Yes. GTE Large (v1.5)'s weights are publicly available, so you can self-host or fine-tune. Note that open weights ≠ open source — the training data and code are typically not released.

What are the best alternatives to GTE Large (v1.5)?

If GTE Large (v1.5) doesn't fit, consider Multi-QA-mpnet-base-dot-v1, Wan2.2-T2V-A14B, E5 Large v2. 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.

Explore more

More digitalocean models

Capability lists this model is in

See also

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.