Llama 3.1 70B Hanami x1 APIOPEN WEIGHTS16K context

Pricing & specs mirror our live pricing as of July 2026 (pay-as-you-go).

What is Llama 3.1 70B Hanami x1?

This is [Sao10K](/sao10k)'s experiment over [Euryale v2.2](/sao10k/l3.1-euryale-70b).

Llama 3.1 70B Hanami x1 on the release timeline

Llama 3.1 70B Hanami x1 pricing

Pay-as-you-go on DataLLM Lab — these are our live list prices (identical to the pricing page):

On output price, Llama 3.1 70B Hanami x1 is cheaper than 34% of the 309 models in the catalog.

What Llama 3.1 70B Hanami x1 costs per month

Cost = input price × input volume + output price × output volume. The same five workloads run on every model page, so any two compare directly.

Llama 3.1 70B Hanami x1 vs alternatives

When not to use Llama 3.1 70B Hanami x1

• It is served by a single provider, so there is less failover headroom during an outage.
• Its 16K context is small — not ideal for large documents or big codebases.

Specs

How to call Llama 3.1 70B Hanami x1

Copyfrom openai import OpenAI
client = OpenAI(base_url="https://api.datallmlab.com/v1", api_key="YOUR_DATALLM_LAB_KEY")
resp = client.chat.completions.create(
    model="sao10k/l3.1-70b-hanami-x1",
    messages=[{"role": "user", "content": "Hello"}],
)
print(resp.choices[0].message.content)

Copycurl https://api.datallmlab.com/v1/chat/completions \
  -H "Authorization: Bearer YOUR_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"sao10k/l3.1-70b-hanami-x1","messages":[{"role":"user","content":"Hello"}]}'

Copyimport OpenAI from "openai";
const client = new OpenAI({ baseURL: "https://api.datallmlab.com/v1", apiKey: process.env.DATALLM_KEY });
const r = await client.chat.completions.create({
  model: "sao10k/l3.1-70b-hanami-x1",
  messages: [{ role: "user", content: "Hello" }],
});
console.log(r.choices[0].message.content);

The same key routes Llama 3.1 70B Hanami x1 and 300+ other models — switch models by changing one string. How OpenAI-compatible APIs work.

Prompting tips for Llama 3.1 70B Hanami x1

• Be explicit and show the shape of the answer. Spell out format, tone and constraints up front, and include one short example of the output you want — Llama 3.1 70B Hanami x1 follows concrete instructions better than abstract ones.
• Constrain JSON with a schema. Use response_format / structured outputs rather than "reply in JSON" to get valid, parseable objects every time.

Tips are derived from Llama 3.1 70B Hanami x1's actual capabilities — context window, tool & JSON support, modality and price tier — not generic advice.

Open-source tools for Llama 3.1 70B Hanami x1

Popular open-source projects for running and building with Llama 3.1 70B Hanami x1 — star counts pulled from GitHub (July 2026).

Listed by GitHub stars; inclusion is by ecosystem relevance (inference engines, agent frameworks and SDKs), not affiliation. Stars change — see each repo for current numbers.

Migrating to Llama 3.1 70B Hanami x1

Coming from OpenAI or another gateway? On an OpenAI-compatible setup the only changes are base_url → https://api.datallmlab.com/v1 and model → sao10k/l3.1-70b-hanami-x1. Messages, streaming and the rest of your code stay the same. Routing & failover guide.

Self-host or use the API?

Llama 3.1 70B Hanami x1 ships open weights (Sao10K/L3.1-70B-Hanami-x1, ~72 downloads and 45 likes in the last 30 days), with community quantizations (bf16) for smaller GPUs, so you can run it yourself. Most teams still use the API: no GPU to provision or keep warm, no inference ops, and instant access across 1 providers with automatic failover. Self-host when data residency or fixed per-token economics matter most.

Rate limits & reliability

Rate limits here are the DataLLM Lab gateway's, not the upstream vendor's. On 429 (rate limited) or 503 (provider busy), retry with exponential backoff. See the error-code guide and failover setup.

Related reading

Frequently asked questions