Llama 3.1 70B Hanami x1
meta/l3-1-70b-hanami-x1Por Meta · familia: llama · lanzado 2025-01-08 · fecha de conocimiento: 2023-12-31
Prices in USD per 1M tokens. Unknown means the provider does not publish per-token pricing.
Capacidades
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.
Coding23
- Tool calling0/40
- Structured output20/20
- Reasoning0/10
- Context window (100K → 1M)0/20
- Provider availability3/10
Agents38
- Tool calling0/35
- Structured output25/25
- Reasoning0/15
- Output token limit10/15
- Provider availability3/10
JSON / structured output78
- Structured output / JSON mode50/50
- Tool calling0/20
- Temperature control10/10
- Price-friendly for high-volume18/20
Cost efficiency63
- Headline price (log-scaled)63/95
- Has prompt-cache pricing0/5
Long context0
- Context ≥ 100K0/100
Production-readiness64
- Number of independent providers15/40
- 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 | $2.71 < $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 | $5.42 < $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 | $1.23 < $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 | $4.44 < $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 | $6.21 < $0.01 per request | 12K input tokens (long-running tool history) and a 600-token tool-call decision. Cost per agent step. |
Detalle de precios
Precio recomendado de nano-gpt · Sao10K/L3.1-70B-Hanami-x1
Disponible en 3 proveedores
| Proveedor | ID de modelo del proveedor | Entrada / 1M | Salida / 1M | Contexto | Lanzado |
|---|---|---|---|---|---|
| OpenRouter openrouter | sao10k/l3.1-70b-hanami-x1 | $3.00 | $3.00 | 16K | 2025-01-08 |
| Kilo Gateway kilo | sao10k/l3.1-70b-hanami-x1 | $3.00 | $3.00 | 16K | 2025-01-08 |
| NanoGPT nano-gpt | Sao10K/L3.1-70B-Hanami-x1 | $0.493 | $0.493 | 16K | 2024-07-23 |
Inconsistencias de datos entre proveedores
- context_window varies: 16000, 16384
- release_date varies (span 169d): 2024-07-23, 2025-01-08
Los proveedores reportan valores distintos para este modelo. Los datos clave de arriba usan un proveedor representativo; consulta la tabla para detalles por proveedor.
Frequently asked questions
How much does Llama 3.1 70B Hanami x1 cost?
Llama 3.1 70B Hanami x1 costs $0.493 per 1M input tokens and $0.493 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 70B Hanami x1?
Llama 3.1 70B Hanami x1 has a context window of 16K tokens, with a max output of 16K tokens per reply. This is the total combined size of prompt + completion.
Does Llama 3.1 70B Hanami x1 support tool calling?
No. Llama 3.1 70B Hanami x1 does not support tool calling (function calling). If your workflow requires it, look at the /capabilities/tool-calling list for alternatives.
Does Llama 3.1 70B Hanami x1 support structured output / JSON mode?
Yes. Llama 3.1 70B Hanami x1 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 70B Hanami x1 accept image input?
No. Llama 3.1 70B Hanami x1 only accepts text as input. If you need image input, see our /capabilities/vision list for current vision-capable models.
Is Llama 3.1 70B Hanami x1 open-weight?
Yes. Llama 3.1 70B Hanami x1'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 70B Hanami x1?
If Llama 3.1 70B Hanami x1 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.
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Capability lists this model is in
Última actualización:
Pricing and capabilities are refreshed daily and reconciled against each provider's official documentation. Always verify critical production decisions with the provider directly.