Llama-xLAM-2 70B fc-r
meta/llama-xlam-2-70b-fc-r出品方： Meta · 系列: llama · 发布 2025-04-13
Prices in USD per 1M tokens. Unknown means the provider does not publish per-token pricing.
能力清单
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.
Coding3
- Tool calling0/40
- Structured output0/20
- Reasoning0/10
- Context window (100K → 1M)2/20
- Provider availability1/10
Agents11
- Tool calling0/35
- Structured output0/25
- Reasoning0/15
- Output token limit10/15
- Provider availability1/10
JSON / structured output10
- Structured output / JSON mode0/50
- Tool calling0/20
- Temperature control0/10
- Price-friendly for high-volume10/20
Cost efficiency45
- Headline price (log-scaled)45/95
- Has prompt-cache pricing0/5
Long context45
- Context window (100K → 2M)35/90
- Has published price for full window10/10
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.
| Scenario | Cost | Assumption |
|---|---|---|
RAG answer per 1,000 RAG answers | $13.75 $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 | $27.50 < $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 | $6.25 < $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 | $22.50 $0.02 per request | 8K input tokens (diff + surrounding files) and a 1K-token review comment. PR-bot workloads. |
Agent step per 1,000 steps | $31.50 $0.03 per request | 12K input tokens (long-running tool history) and a 600-token tool-call decision. Cost per agent step. |
定价详情
推荐定价来自 nano-gpt · Salesforce/Llama-xLAM-2-70b-fc-r
在 1 家渠道可用
| 服务商 | 服务商模型 ID | 输入 / 1M | 输出 / 1M | 上下文 | 发布日期 |
|---|---|---|---|---|---|
| NanoGPT nano-gpt | Salesforce/Llama-xLAM-2-70b-fc-r | $2.50 | $2.50 | 128K | 2025-04-13 |
Frequently asked questions
How much does Llama-xLAM-2 70B fc-r cost?
Llama-xLAM-2 70B fc-r costs $2.50 per 1M input tokens and $2.50 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 Llama-xLAM-2 70B fc-r?
Llama-xLAM-2 70B fc-r has a context window of 128K tokens, with a max output of 16K tokens per reply. This is the total combined size of prompt + completion.
Does Llama-xLAM-2 70B fc-r support tool calling?
No. Llama-xLAM-2 70B fc-r does not support tool calling (function calling). If your workflow requires it, look at the /capabilities/tool-calling list for alternatives.
Does Llama-xLAM-2 70B fc-r support structured output / JSON mode?
No. Llama-xLAM-2 70B fc-r does not support structured output / JSON-schema-constrained decoding. If your workflow requires it, look at the /capabilities/structured-output list for alternatives.
Can Llama-xLAM-2 70B fc-r accept image input?
No. Llama-xLAM-2 70B fc-r only accepts text as input. If you need image input, see our /capabilities/vision list for current vision-capable models.
Is Llama-xLAM-2 70B fc-r open-weight?
No. Llama-xLAM-2 70B fc-r is a proprietary model — only Meta (and any approved hosting partners) can serve it. The pricing above reflects the cheapest API access.
What are the best alternatives to Llama-xLAM-2 70B fc-r?
If Llama-xLAM-2 70B fc-r doesn't fit, consider Meta-Llama-3.1-8B-Instruct, Llama-3.3-70B-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 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.
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最近更新：
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.