Meta-Llama-3-8B-Instruct
meta/llama-3-8b-instruct提供： Meta · ファミリー: llama · リリース 2025-04-03 · 知識カットオフ: 2023-12
Prices in USD per 1M tokens. Unknown means the provider does not publish per-token pricing.
機能一覧
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.
Coding8
- Tool calling0/40
- Structured output0/20
- Reasoning0/10
- Context window (100K → 1M)0/20
- Provider availability8/10
Agents8
- Tool calling0/35
- Structured output0/25
- Reasoning0/15
- Output token limit0/15
- Provider availability8/10
JSON / structured output30
- Structured output / JSON mode0/50
- Tool calling0/20
- Temperature control10/10
- Price-friendly for high-volume20/20
Cost efficiency90
- Headline price (log-scaled)90/95
- Has prompt-cache pricing0/5
Long context0
- Context ≥ 100K0/100
Production-readiness89
- Number of independent providers40/40
- Has published per-token price20/20
- Context window ≥ 8K8/15
- No data inconsistencies across providers6/10
- 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 | $0.17 < $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 | $0.34 < $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 | $0.08 < $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 | $0.28 < $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 | $0.38 < $0.01 per request | 12K input tokens (long-running tool history) and a 600-token tool-call decision. Cost per agent step. |
料金詳細
推奨料金 (提供元): kilo · meta-llama/llama-3-8b-instruct
最安プロバイダー: github-models · Unknown 入力 + Unknown 出力
8 か所で利用可能
| プロバイダー | プロバイダーモデルID | 入力 / 1M | 出力 / 1M | コンテキスト | リリース日 |
|---|---|---|---|---|---|
| Azure azure | meta-llama-3-8b-instruct | $0.300 | $0.610 | 8K | 2024-04-18 |
| NovitaAI novita-ai | meta-llama/llama-3-8b-instruct | $0.040 | $0.040 | 8K | 2024-04-25 |
| Kilo Gateway kilo | meta-llama/llama-3-8b-instruct | $0.030 | $0.040 | 8K | 2024-04-25 |
| Cloudflare AI Gateway cloudflare-ai-gateway | workers-ai/@cf/meta/llama-3-8b-instruct-awq | $0.120 | $0.270 | 128K | 2025-04-03 |
| Cloudflare AI Gateway cloudflare-ai-gateway | workers-ai/@cf/meta/llama-3-8b-instruct | $0.280 | $0.830 | 128K | 2025-04-03 |
| Azure Cognitive Services azure-cognitive-services | meta-llama-3-8b-instruct | $0.300 | $0.610 | 8K | 2024-04-18 |
| LLM Gateway llmgateway | llama-3-8b-instruct | $0.040 | $0.040 | 8K | 2025-04-03 |
| GitHub Models github-models | meta/meta-llama-3-8b-instruct | Unknown | Unknown | 8K | 2024-04-18 |
プロバイダー間でデータに差異
- context_window varies: 128000, 8192
- release_date varies (span 350d): 2024-04-18, 2024-04-25, 2025-04-03
プロバイダーごとに本モデルの値が異なります。上部の「主要数値」は代表的プロバイダーを使用しています。詳細は表をご確認ください。
Frequently asked questions
How much does Meta-Llama-3-8B-Instruct cost?
Meta-Llama-3-8B-Instruct costs $0.030 per 1M input tokens and $0.040 per 1M output tokens, sourced from kilo. 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-3-8B-Instruct?
Meta-Llama-3-8B-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-3-8B-Instruct support tool calling?
No. Meta-Llama-3-8B-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-3-8B-Instruct support structured output / JSON mode?
Support for structured output / JSON-schema-constrained decoding is not reported for Meta-Llama-3-8B-Instruct in our data source. Verify with Meta's official documentation before relying on it in production.
Can Meta-Llama-3-8B-Instruct accept image input?
No. Meta-Llama-3-8B-Instruct only accepts text as input. If you need image input, see our /capabilities/vision list for current vision-capable models.
Is Meta-Llama-3-8B-Instruct open-weight?
Yes. Meta-Llama-3-8B-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-3-8B-Instruct?
If Meta-Llama-3-8B-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
最終更新：
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.