Llama3 70B Instruct
meta/llama-3-70b-instruct出品方： Meta · 系列: llama · 发布 2024-04-25
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.
Coding22
- Tool calling0/40
- Structured output20/20
- Reasoning0/10
- Context window (100K → 1M)0/20
- Provider availability2/10
Agents32
- Tool calling0/35
- Structured output25/25
- Reasoning0/15
- Output token limit5/15
- Provider availability2/10
JSON / structured output78
- Structured output / JSON mode50/50
- Tool calling0/20
- Temperature control10/10
- Price-friendly for high-volume18/20
Cost efficiency60
- Headline price (log-scaled)60/95
- Has prompt-cache pricing0/5
Long context0
- Context ≥ 100K0/100
Production-readiness63
- Number of independent providers10/40
- 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.
| Scenario | Cost | Assumption |
|---|---|---|
RAG answer per 1,000 RAG answers | $2.92 < $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 | $5.84 < $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 | $1.39 < $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 | $4.82 < $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 | $6.56 < $0.01 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 novita-ai · meta-llama/llama-3-70b-instruct
在 2 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 |
|---|---|---|---|---|---|
| NovitaAI novita-ai | meta-llama/llama-3-70b-instruct | $0.510 | $0.740 | 8K | 2024-04-25 |
| LLM Gateway llmgateway | llama-3-70b-instruct | $0.510 | $0.740 | 8K | 2024-04-18 |
Frequently asked questions
How much does Llama3 70B Instruct cost?
Llama3 70B Instruct costs $0.510 per 1M input tokens and $0.740 per 1M output tokens, sourced from novita-ai. Cache reads, audio tokens and >200K-context tiers (where applicable) are listed in the Pricing detail block above.
What is the context window of Llama3 70B Instruct?
Llama3 70B Instruct has a context window of 8K tokens, with a max output of 8K tokens per reply. This is the total combined size of prompt + completion.
Does Llama3 70B Instruct support tool calling?
No. Llama3 70B Instruct does not support tool calling (function calling). If your workflow requires it, look at the /capabilities/tool-calling list for alternatives.
Does Llama3 70B Instruct support structured output / JSON mode?
Yes. Llama3 70B Instruct supports structured output / JSON-schema-constrained decoding. This makes it suitable for production agent and automation workloads where the model has to invoke external functions reliably.
Can Llama3 70B Instruct accept image input?
No. Llama3 70B Instruct only accepts text as input. If you need image input, see our /capabilities/vision list for current vision-capable models.
Is Llama3 70B Instruct open-weight?
Yes. Llama3 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 Llama3 70B Instruct?
If Llama3 70B Instruct 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.
Explore more
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- Llama 3.2 3B Instruct$0.02 in / $0.02 out
- Llama 4 Scout 17B 16E Instruct$0.10 in / $0.30 out
Capability lists this model is in
最近更新：
Pricing and capabilities are refreshed daily and reconciled against each provider's official documentation. Always verify critical production decisions with the provider directly.