Meta-Llama-3.1-8B-Instruct
meta/llama-3-1-8b-instructPar Meta · famille: llama · sorti 2024-07-23 · fin de connaissance: 2023-12
Prices in USD per 1M tokens. Unknown means the provider does not publish per-token pricing.
Capacités
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.
Coding52
- Tool calling40/40
- Structured output0/20
- Reasoning0/10
- Context window (100K → 1M)2/20
- Provider availability10/10
Agents60
- Tool calling35/35
- Structured output0/25
- Reasoning0/15
- Output token limit15/15
- Provider availability10/10
JSON / structured output50
- Structured output / JSON mode0/50
- Tool calling20/20
- Temperature control10/10
- Price-friendly for high-volume20/20
Cost efficiency93
- Headline price (log-scaled)93/95
- Has prompt-cache pricing0/5
Long context45
- Context window (100K → 2M)35/90
- Has published price for full window10/10
Production-readiness96
- Number of independent providers40/40
- Has published per-token price20/20
- Context window ≥ 8K15/15
- 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 | $0.12 < $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 | $0.23 < $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 | $0.06 < $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 | $0.19 < $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 | $0.26 < $0.01 per request | 12K input tokens (long-running tool history) and a 600-token tool-call decision. Cost per agent step. |
Détail des tarifs
Tarif recommandé de nano-gpt · Meta-Llama-3-1-8B-Instruct-FP8
Fournisseur le moins cher : nvidia · Unknown entrée + Unknown sortie
Disponible chez 25 fournisseurs
| Fournisseur | ID modèle fournisseur | Entrée / 1M | Sortie / 1M | Contexte | Publié le |
|---|---|---|---|---|---|
| Azure azure | meta-llama-3.1-8b-instruct | $0.300 | $0.610 | 128K | 2024-07-23 |
| Deep Infra deepinfra | meta-llama/Llama-3.1-8B-Instruct | $0.020 | $0.050 | 131K | 2024-07-23 |
| Scaleway scaleway | llama-3.1-8b-instruct | $0.200 | $0.200 | 128K | 2025-01-01 |
| NanoGPT nano-gpt | Meta-Llama-3-1-8B-Instruct-FP8 | $0.020 | $0.030 | 128K | 2024-07-23 |
| NanoGPT nano-gpt | meta-llama/llama-3.1-8b-instruct | $0.054 | $0.054 | 131K | 2024-07-23 |
| Abacus abacus | meta-llama/Meta-Llama-3.1-8B-Instruct | $0.020 | $0.050 | 128K | 2024-07-23 |
| NovitaAI novita-ai | meta-llama/llama-3.1-8b-instruct | $0.020 | $0.050 | 16K | 2024-07-24 |
| Weights & Biases wandb | meta-llama/Llama-3.1-8B-Instruct | $0.220 | $0.220 | 128K | 2024-07-23 |
| Kilo Gateway kilo | meta-llama/llama-3.1-8b-instruct | $0.020 | $0.050 | 16K | 2024-07-23 |
| Cloudflare AI Gateway cloudflare-ai-gateway | workers-ai/@cf/meta/llama-3.1-8b-instruct-fp8 | $0.150 | $0.290 | 128K | 2025-04-03 |
| Cloudflare AI Gateway cloudflare-ai-gateway | workers-ai/@cf/meta/llama-3.1-8b-instruct-awq | $0.120 | $0.270 | 128K | 2025-04-03 |
| Cloudflare AI Gateway cloudflare-ai-gateway | workers-ai/@cf/meta/llama-3.1-8b-instruct | $0.280 | $0.830 | 128K | 2025-04-03 |
| Nebius Token Factory nebius | meta-llama/Meta-Llama-3.1-8B-Instruct | $0.020 | $0.060 | 128K | 2024-07-23 |
| Helicone helicone | llama-3.1-8b-instruct | $0.020 | $0.050 | 16K | 2024-07-23 |
| Azure Cognitive Services azure-cognitive-services | meta-llama-3.1-8b-instruct | $0.300 | $0.610 | 128K | 2024-07-23 |
| Synthetic synthetic | hf:meta-llama/Llama-3.1-8B-Instruct | $0.200 | $0.200 | 128K | 2024-07-23 |
| Nvidia nvidia | meta/llama-3.1-8b-instruct | Unknown | Unknown | 16K | 2025-01-01 |
| Inference inference | meta/llama-3.1-8b-instruct | $0.025 | $0.025 | 16K | 2025-01-01 |
| OVHcloud AI Endpoints ovhcloud | llama-3.1-8b-instruct | $0.110 | $0.110 | 131K | 2025-06-11 |
| Friendli friendli | meta-llama/Llama-3.1-8B-Instruct | $0.100 | $0.100 | 131K | 2024-08-01 |
| SiliconFlow siliconflow | meta-llama/Meta-Llama-3.1-8B-Instruct | $0.060 | $0.060 | 33K | 2025-04-23 |
| LLM Gateway llmgateway | llama-3.1-8b-instruct | $0.220 | $0.220 | 128K | 2024-07-23 |
| STACKIT stackit | neuralmagic/Meta-Llama-3.1-8B-Instruct-FP8 | $0.160 | $0.270 | 128K | 2024-07-23 |
| GitHub Models github-models | meta/meta-llama-3.1-8b-instruct | Unknown | Unknown | 128K | 2024-07-23 |
| Regolo AI regolo-ai | llama-3.1-8b-instruct | $0.050 | $0.250 | 120K | 2025-04-07 |
Incohérences de données entre fournisseurs
- context_window varies: 120000, 128000, 131072, 16000, 16384, 33000
- release_date varies (span 323d): 2024-07-23, 2024-07-24, 2024-08-01, 2025-01-01, 2025-04-03, 2025-04-07, 2025-04-23, 2025-06-11
Les fournisseurs rapportent des valeurs différentes pour ce modèle. Les infos clés ci-dessus utilisent un fournisseur représentatif ; voir le tableau pour le détail par fournisseur.
Frequently asked questions
How much does Meta-Llama-3.1-8B-Instruct cost?
Meta-Llama-3.1-8B-Instruct costs $0.020 per 1M input tokens and $0.030 per 1M output tokens, sourced from nano-gpt. 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-3.1-8B-Instruct?
Meta-Llama-3.1-8B-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-3.1-8B-Instruct support tool calling?
Yes. Meta-Llama-3.1-8B-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-3.1-8B-Instruct support structured output / JSON mode?
Support for structured output / JSON-schema-constrained decoding is not reported for Meta-Llama-3.1-8B-Instruct in our data source. Verify with Meta's official documentation before relying on it in production.
Can Meta-Llama-3.1-8B-Instruct accept image input?
No. Meta-Llama-3.1-8B-Instruct only accepts text as input. If you need image input, see our /capabilities/vision list for current vision-capable models.
Is Meta-Llama-3.1-8B-Instruct open-weight?
Yes. Meta-Llama-3.1-8B-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-3.1-8B-Instruct?
If Meta-Llama-3.1-8B-Instruct doesn't fit, consider Llama-3.3-70B-Instruct, Llama 4 Maverick 17B 128E Instruct FP8, 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 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.
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- Meta-Llama-3.1-70B-Instruct$0.40 in / $0.40 out
- Llama 3.2 3b Instruct$0.01 in / $0.01 out
Capability lists this model is in
Dernière mise à jour :
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.