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.
Coding72
- Tool calling40/40
- Structured output20/20
- Reasoning0/10
- Context window (100K → 1M)2/20
- Provider availability10/10
Agents85
- Tool calling35/35
- Structured output25/25
- Reasoning0/15
- Output token limit15/15
- Provider availability10/10
JSON / structured output100
- Structured output / JSON mode50/50
- Tool calling20/20
- Temperature control10/10
- Price-friendly for high-volume20/20
Cost efficiency98
- Headline price (log-scaled)93/95
- Has prompt-cache pricing5/5
Long context46
- Context window (100K → 2M)36/90
- 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 18 fournisseurs
| Fournisseur | ID modèle fournisseur | Entrée / 1M | Sortie / 1M | Contexte | Publié le |
|---|---|---|---|---|---|
| OpenRouter openrouter | meta-llama/llama-3.1-8b-instruct | $0.050 | $0.080 | 131K | 2024-07-23 |
| Cloudflare Workers AI cloudflare-workers-ai | @cf/meta/llama-3.1-8b-instruct-fp8 | $0.152 | $0.287 | 32K | 2024-07-25 |
| Nvidia nvidia | meta/llama-3.1-8b-instruct | Unknown | Unknown | 16K | 2025-01-01 |
| Cortecs cortecs | llama-3.1-8b-instruct | $0.167 | $0.167 | 128K | 2024-04-09 |
| NanoGPT nano-gpt | Meta-Llama-3-1-8B-Instruct-FP8 | $0.020 | $0.030 | 128K | 2024-01-01 |
| NanoGPT nano-gpt | meta-llama/llama-3.1-8b-instruct | $0.054 | $0.085 | 131K | 2024-01-01 |
| 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 | $0.280 | $0.830 | 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 |
| Inference inference | meta/llama-3.1-8b-instruct | $0.025 | $0.025 | 16K | 2025-01-01 |
| Helicone helicone | llama-3.1-8b-instruct | $0.020 | $0.050 | 16K | 2024-07-23 |
| Neon neon | meta-llama-3-1-8b-instruct | $0.150 | $0.450 | 131K | 2024-07-23 |
| Atomic Chat atomic-chat | Meta-Llama-3_1-8B-Instruct-GGUF | Unknown | Unknown | 131K | 2024-07-23 |
| NovitaAI novita-ai | meta-llama/llama-3.1-8b-instruct | $0.020 | $0.050 | 16K | 2024-07-24 |
| Kilo Gateway kilo | meta-llama/llama-3.1-8b-instruct | $0.020 | $0.040 | 131K | 2024-07-23 |
| Pioneer pioneer | meta-llama/Llama-3.1-8B-Instruct | $0.200 | $0.200 | 131K | 2024-06-30 |
| Weights & Biases wandb | meta-llama/Llama-3.1-8B-Instruct | $0.220 | $0.220 | 128K | 2024-07-23 |
| Abacus abacus | meta-llama/Meta-Llama-3.1-8B-Instruct | $0.020 | $0.050 | 128K | 2024-07-23 |
Incohérences de données entre fournisseurs
- context_window varies: 128000, 131072, 16000, 16384, 32000
- release_date varies (span 458d): 2024-01-01, 2024-04-09, 2024-06-30, 2024-07-23, 2024-07-24, 2024-07-25, 2025-01-01, 2025-04-03
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 Llama 3.1 8B Instruct cost?
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 Llama 3.1 8B Instruct?
Llama 3.1 8B Instruct has a context window of 131K tokens, with a max output of 131K tokens per reply. This is the total combined size of prompt + completion.
Does Llama 3.1 8B Instruct support tool calling?
Yes. 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 Llama 3.1 8B Instruct support structured output / JSON mode?
Yes. Llama 3.1 8B 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 Llama 3.1 8B Instruct accept image input?
No. 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 Llama 3.1 8B Instruct open-weight?
Yes. 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 Llama 3.1 8B Instruct?
If Llama 3.1 8B Instruct doesn't fit, consider Llama-3.3-70B-Instruct, Llama 4 Maverick 17B 128E Instruct FP8, Llama 3.2 3B 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.
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Capability lists this model is in
Dernière mise à jour :
Pricing and capabilities are refreshed daily and reconciled against each provider's official documentation. Always verify critical production decisions with the provider directly.