Llama Nemotron Embed 1B v2
meta/llama-nemotron-embed-1bمن Meta · العائلة: nemotron · أُصدِر 2025-10-16
Prices in USD per 1M tokens. Unknown means the provider does not publish per-token pricing.
القدرات
Model fit scores
0–100 · higher is betterThese 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 output0
- Structured output / JSON mode0/50
- Tool calling0/20
- Temperature control0/10
- Price-friendly for high-volume0/20
Cost efficiency0
- Has published price0/100
Long context0
- Context ≥ 100K0/100
Production-readiness38
- Number of independent providers5/40
- Has published per-token price0/20
- 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.
This model has no published per-token price, so we can't compute a cost estimate. See the provider's official pricing page for current rates.
متاح لدى 1 مزود
| المزود | معرف نموذج المزود | إدخال / 1M | إخراج / 1M | السياق | تاريخ الإصدار |
|---|---|---|---|---|---|
| SAP AI Core sap-ai-core | nvidia--llama-nemotron-embed-1b | Unknown | Unknown | 8K | 2025-10-16 |
Frequently asked questions
How much does Llama Nemotron Embed 1B v2 cost?
Llama Nemotron Embed 1B v2 does not have a publicly published per-token price in our data source. This usually means it is gated behind enterprise sales or invite access. Check Meta's official pricing page for the most current rates.
What is the context window of Llama Nemotron Embed 1B v2?
Llama Nemotron Embed 1B v2 has a context window of 8K tokens, with a max output of 2K tokens per reply. This is the total combined size of prompt + completion.
Does Llama Nemotron Embed 1B v2 support tool calling?
No. Llama Nemotron Embed 1B v2 does not support tool calling (function calling). If your workflow requires it, look at the /capabilities/tool-calling list for alternatives.
Does Llama Nemotron Embed 1B v2 support structured output / JSON mode?
Support for structured output / JSON-schema-constrained decoding is not reported for Llama Nemotron Embed 1B v2 in our data source. Verify with Meta's official documentation before relying on it in production.
Can Llama Nemotron Embed 1B v2 accept image input?
No. Llama Nemotron Embed 1B v2 only accepts text as input. If you need image input, see our /capabilities/vision list for current vision-capable models.
Is Llama Nemotron Embed 1B v2 open-weight?
Yes. Llama Nemotron Embed 1B v2'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 Llama Nemotron Embed 1B v2?
If Llama Nemotron Embed 1B v2 doesn't fit, consider Llama-3.3-70B-Instruct, Llama-3.1-8B-Instruct, Llama 4 Maverick 17B 128E Instruct FP8. 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 are normalised into a single canonical model record and reconciled with each provider's official documentation. We re-pull daily and write any changes (price, context, capability) to the /changelog page.
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Capability lists this model is in
آخر تحديث:
Pricing and capabilities are refreshed daily and reconciled against each provider's official documentation. Always verify critical production decisions with the provider directly.