Meta-Llama-3-70B-Instruct
meta/llama-3-70b-instruct제공: Meta · 패밀리: llama · 출시 2024-04-18 · 지식 컷오프: 2023-12
Prices in USD per 1M tokens. Unknown means the provider does not publish per-token pricing.
기능
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.
Coding6
- Tool calling0/40
- Structured output0/20
- Reasoning0/10
- Context window (100K → 1M)0/20
- Provider availability6/10
Agents6
- Tool calling0/35
- Structured output0/25
- Reasoning0/15
- Output token limit0/15
- Provider availability6/10
JSON / structured output28
- Structured output / JSON mode0/50
- 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-readiness81
- Number of independent providers30/40
- Has published per-token price20/20
- Context window ≥ 8K8/15
- 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 | $2.92 < $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. |
가격 상세
추천 가격 제공자: novita-ai · meta-llama/llama-3-70b-instruct
가장 저렴한 제공자: github-models · Unknown 입력 + Unknown 출력
6곳 제공사에서 이용 가능
| 제공자 | 제공자 모델 ID | 입력 / 1M | 출력 / 1M | 컨텍스트 | 출시일 |
|---|---|---|---|---|---|
| Azure azure | meta-llama-3-70b-instruct | $2.68 | $3.54 | 8K | 2024-04-18 |
| NovitaAI novita-ai | meta-llama/llama-3-70b-instruct | $0.510 | $0.740 | 8K | 2024-04-25 |
| Kilo Gateway kilo | meta-llama/llama-3-70b-instruct | $0.510 | $0.740 | 8K | 2024-07-23 |
| Azure Cognitive Services azure-cognitive-services | meta-llama-3-70b-instruct | $2.68 | $3.54 | 8K | 2024-04-18 |
| LLM Gateway llmgateway | llama-3-70b-instruct | $0.510 | $0.740 | 8K | 2024-04-18 |
| GitHub Models github-models | meta/meta-llama-3-70b-instruct | Unknown | Unknown | 8K | 2024-04-18 |
제공자 간 데이터 불일치
- release_date varies (span 96d): 2024-04-18, 2024-04-25, 2024-07-23
제공자별로 이 모델의 값이 다릅니다. 위의 핵심 정보는 대표 제공자 기준이며, 제공자별 상세는 표를 참고하세요.
Frequently asked questions
How much does Meta-Llama-3-70B-Instruct cost?
Meta-Llama-3-70B-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 Meta-Llama-3-70B-Instruct?
Meta-Llama-3-70B-Instruct 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 Meta-Llama-3-70B-Instruct support tool calling?
No. Meta-Llama-3-70B-Instruct does not support tool calling (function calling). If your workflow requires it, look at the /capabilities/tool-calling list for alternatives.
Does Meta-Llama-3-70B-Instruct support structured output / JSON mode?
Support for structured output / JSON-schema-constrained decoding is not reported for Meta-Llama-3-70B-Instruct in our data source. Verify with Meta's official documentation before relying on it in production.
Can Meta-Llama-3-70B-Instruct accept image input?
No. Meta-Llama-3-70B-Instruct only accepts text as input. If you need image input, see our /capabilities/vision list for current vision-capable models.
Is Meta-Llama-3-70B-Instruct open-weight?
Yes. Meta-Llama-3-70B-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-3-70B-Instruct?
If Meta-Llama-3-70B-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
More Meta models
- Meta-Llama-3.1-8B-Instruct$0.02 in / $0.03 out
- Llama-3.3-70B-Instruct$0.05 in / $0.23 out
- Llama 4 Maverick 17B 128E Instruct FP8$0.14 in / $0.59 out
- Llama 4 Scout 17B 16E Instruct$0.08 in / $0.30 out
- Meta-Llama-3.1-70B-Instruct$0.40 in / $0.40 out
Capability lists this model is in
마지막 업데이트:
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.