Meta-Llama-3.1-70B-Instruct
meta/llama-3-1-70b-instruct出品方： Meta · 系列: llama · 發布 2024-07-23 · 知識截止: 2023-12
Prices in USD per 1M tokens. Unknown means the provider does not publish per-token pricing.
能力清單
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.
Coding51
- Tool calling40/40
- Structured output0/20
- Reasoning0/10
- Context window (100K → 1M)2/20
- Provider availability9/10
Agents59
- Tool calling35/35
- Structured output0/25
- Reasoning0/15
- Output token limit15/15
- Provider availability9/10
JSON / structured output48
- Structured output / JSON mode0/50
- Tool calling20/20
- Temperature control10/10
- Price-friendly for high-volume18/20
Cost efficiency65
- Headline price (log-scaled)65/95
- Has prompt-cache pricing0/5
Long context45
- Context window (100K → 2M)35/90
- Has published price for full window10/10
Production-readiness98
- Number of independent providers40/40
- Has published per-token price20/20
- Context window ≥ 8K15/15
- No data inconsistencies across providers8/10
- 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 | $2.20 < $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 | $4.40 < $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 | $1.00 < $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 | $3.60 < $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 | $5.04 < $0.01 per request | 12K input tokens (long-running tool history) and a 600-token tool-call decision. Cost per agent step. |
定價詳情
推薦定價來自 deepinfra · meta-llama/Llama-3.1-70B-Instruct
最便宜的渠道： nvidia · Unknown 輸入 + Unknown 輸出
於 9 家供應商可用
| 服務商 | 服務商模型 ID | 輸入 / 1M | 輸出 / 1M | 上下文 | 發布日期 |
|---|---|---|---|---|---|
| Azure azure | meta-llama-3.1-70b-instruct | $2.68 | $3.54 | 128K | 2024-07-23 |
| Deep Infra deepinfra | meta-llama/Llama-3.1-70B-Instruct | $0.400 | $0.400 | 131K | 2024-07-23 |
| Weights & Biases wandb | meta-llama/Llama-3.1-70B-Instruct | $0.800 | $0.800 | 128K | 2024-07-23 |
| Kilo Gateway kilo | meta-llama/llama-3.1-70b-instruct | $0.400 | $0.400 | 131K | 2024-07-16 |
| Azure Cognitive Services azure-cognitive-services | meta-llama-3.1-70b-instruct | $2.68 | $3.54 | 128K | 2024-07-23 |
| Synthetic synthetic | hf:meta-llama/Llama-3.1-70B-Instruct | $0.900 | $0.900 | 128K | 2024-07-23 |
| Nvidia nvidia | meta/llama-3.1-70b-instruct | Unknown | Unknown | 128K | 2024-07-16 |
| LLM Gateway llmgateway | llama-3.1-70b-instruct | $0.720 | $0.720 | 128K | 2024-07-23 |
| GitHub Models github-models | meta/meta-llama-3.1-70b-instruct | Unknown | Unknown | 128K | 2024-07-23 |
各渠道資料存在不一致
- context_window varies: 128000, 131072
各服務商對此模型的回報值不一致。上方「核心數據」採用代表性服務商的值；逐項請以下表為準。
Frequently asked questions
How much does Meta-Llama-3.1-70B-Instruct cost?
Meta-Llama-3.1-70B-Instruct costs $0.400 per 1M input tokens and $0.400 per 1M output tokens, sourced from deepinfra. Cache reads, audio tokens and >200K-context tiers (where applicable) are listed in the Pricing detail block above.
What is the context window of Meta-Llama-3.1-70B-Instruct?
Meta-Llama-3.1-70B-Instruct has a context window of 128K tokens, with a max output of 33K tokens per reply. This is the total combined size of prompt + completion.
Does Meta-Llama-3.1-70B-Instruct support tool calling?
Yes. Meta-Llama-3.1-70B-Instruct supports tool calling (function calling). This makes it suitable for production agent and automation workloads where the model has to invoke external functions reliably.
Does Meta-Llama-3.1-70B-Instruct support structured output / JSON mode?
Support for structured output / JSON-schema-constrained decoding is not reported for Meta-Llama-3.1-70B-Instruct in our data source. Verify with Meta's official documentation before relying on it in production.
Can Meta-Llama-3.1-70B-Instruct accept image input?
No. Meta-Llama-3.1-70B-Instruct only accepts text as input. If you need image input, see our /capabilities/vision list for current vision-capable models.
Is Meta-Llama-3.1-70B-Instruct open-weight?
Yes. Meta-Llama-3.1-70B-Instruct'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 Meta-Llama-3.1-70B-Instruct?
If Meta-Llama-3.1-70B-Instruct 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.
Explore more
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Capability lists this model is in
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.