Meta-Llama-3.1-405B-Instruct
meta/llama-3-1-405b-instructVon Meta · Familie: llama · veröffentlicht 2024-07-23 · Wissensstand: 2023-12
Prices in USD per 1M tokens. Unknown means the provider does not publish per-token pricing.
Fähigkeiten
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.
Coding46
- Tool calling40/40
- Structured output0/20
- Reasoning0/10
- Context window (100K → 1M)2/20
- Provider availability4/10
Agents54
- Tool calling35/35
- Structured output0/25
- Reasoning0/15
- Output token limit15/15
- Provider availability4/10
JSON / structured output30
- Structured output / JSON mode0/50
- Tool calling20/20
- Temperature control10/10
- Price-friendly for high-volume0/20
Cost efficiency29
- Headline price (log-scaled)29/95
- Has prompt-cache pricing0/5
Long context45
- Context window (100K → 2M)35/90
- Has published price for full window10/10
Production-readiness80
- Number of independent providers20/40
- 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 | $34.65 $0.03 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 | $69.30 < $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 | $18.66 $0.02 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 | $58.64 $0.06 per request | 8K input tokens (diff + surrounding files) and a 1K-token review comment. PR-bot workloads. |
Agent step per 1,000 steps | $73.56 $0.07 per request | 12K input tokens (long-running tool history) and a 600-token tool-call decision. Cost per agent step. |
Preis-Details
Empfohlene Preise von azure · meta-llama-3.1-405b-instruct
Günstigster Anbieter: cortecs · Unknown Eingabe + Unknown Ausgabe
Bei 4 Anbietern verfügbar
| Anbieter | Anbieter-Modell-ID | Eingabe / 1M | Ausgabe / 1M | Kontext | Veröffentlicht |
|---|---|---|---|---|---|
| Azure azure | meta-llama-3.1-405b-instruct | $5.33 | $16.00 | 128K | 2024-07-23 |
| Cortecs cortecs | llama-3.1-405b-instruct | Unknown | Unknown | 128K | 2024-07-23 |
| GitHub Models github-models | meta/meta-llama-3.1-405b-instruct | Unknown | Unknown | 128K | 2024-07-23 |
| Azure Cognitive Services azure-cognitive-services | meta-llama-3.1-405b-instruct | $5.33 | $16.00 | 128K | 2024-07-23 |
Frequently asked questions
How much does Meta-Llama-3.1-405B-Instruct cost?
Meta-Llama-3.1-405B-Instruct costs $5.33 per 1M input tokens and $16.00 per 1M output tokens, sourced from azure. 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-3.1-405B-Instruct?
Meta-Llama-3.1-405B-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-3.1-405B-Instruct support tool calling?
Yes. Meta-Llama-3.1-405B-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-3.1-405B-Instruct support structured output / JSON mode?
Support for structured output / JSON-schema-constrained decoding is not reported for Meta-Llama-3.1-405B-Instruct in our data source. Verify with Meta's official documentation before relying on it in production.
Can Meta-Llama-3.1-405B-Instruct accept image input?
No. Meta-Llama-3.1-405B-Instruct only accepts text as input. If you need image input, see our /capabilities/vision list for current vision-capable models.
Is Meta-Llama-3.1-405B-Instruct open-weight?
Yes. Meta-Llama-3.1-405B-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-3.1-405B-Instruct?
If Meta-Llama-3.1-405B-Instruct doesn't fit, consider Llama-3.3-70B-Instruct, Meta-Llama-3.1-8B-Instruct, Llama 4 Scout 17B 16E Instruct. 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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- Meta-Llama-3-8B-Instruct$0.03 in / $0.04 out
Capability lists this model is in
Zuletzt aktualisiert:
Pricing and capabilities are refreshed daily and reconciled against each provider's official documentation. Always verify critical production decisions with the provider directly.