Databricks Meta Llama 3.70B Instruct
Databricks chatDatabricks Meta Llama 3.70B Instruct is a Databricks chat model.It supports a 128,000-token context windowwith up to 128,000 output tokens.Input is priced at $1.00/M tokens and output at $3.00/M tokens. Route Databricks Meta Llama 3.70B Instruct via Future AGI's Agent Command Center for unified observability, caching, and 15 routing strategies including cost-optimized fallback.
Estimate Databricks Meta Llama 3.70B Instruct spend
Pick a workload, fine-tune the sliders, and see the monthly bill.
Estimate uses $1.00/M input · $3.00/M output. Provider pricing changes. Production costs vary with retries, streaming overhead, and tool-call rounds.
Want this for free? Cache + route via Agent Command Center — first 100K requests and 100K cache hits free every month.
Pricing
Per-token rates, expressed in USD per 1M tokens. Verified May 12, 2026.
| Input | $1.00/M | |
| Output | $3.00/M |
Limits
- Context window
- 128,000 tokens
- Max input
- 128,000 tokens
- Max output
- 128,000 tokens
- Modalities
- text
Capabilities
- Function calling — not advertised
- Parallel tool calls — not advertised
- Vision input — not advertised
- Audio input — not advertised
- Audio output — not advertised
- PDF input — not advertised
- Streaming ✓ supported
- Structured output — not advertised
- Prompt caching — not advertised
- Reasoning — not advertised
Where it's strong
Watch out for
- !agentic workflows — no advertised function-calling; use a tool-capable model and route via Agent Command Center for fallback
- !strict structured output — no JSON-schema enforcement, expect retry loops
Benchmarks pending
We haven't logged public benchmark scores for Databricks Meta Llama 3.70B Instruct yet. Have one to contribute? Submit a source — citations help us prioritise.
Call Databricks Meta Llama 3.70B Instruct via Agent Command Center
One OpenAI-compatible endpoint. Routing, fallback, semantic caching, guardrails, and cost tracking come along for the ride. First 100K requests + 100K cache hits free every month.
agentcc / @agentcc/client). Per-call metadata — provider, cost, latency, cache hit, request id — is returned on x-agentcc-* response headers, so any HTTP client can read it.# Databricks Meta Llama 3.70B Instruct via the Agent Command Center Python SDK
# pip install agentcc
import os
from agentcc import AgentCC
client = AgentCC(
api_key=os.environ["AGENTCC_API_KEY"], # from app.futureagi.com → Settings → API Keys
base_url="https://gateway.futureagi.com/v1",
)
resp = client.chat.completions.create(
model="databricks/databricks-meta-llama-3-70b-instruct",
messages=[{"role": "user", "content": "Hello, Databricks Meta Llama 3.70B Instruct!"}],
)
print(resp.choices[0].message.content)
print(f"Tokens: {resp.usage.total_tokens}")
# Per-call gateway metadata is returned on x-agentcc-* response headers.
# When you need it programmatically, use .with_raw_response to get them:
raw = client.chat.completions.with_raw_response.create(
model="databricks/databricks-meta-llama-3-70b-instruct",
messages=[{"role": "user", "content": "Same call, but I want the headers."}],
)
print("Provider:", raw.headers.get("x-agentcc-provider"))
print("Latency:", raw.headers.get("x-agentcc-latency-ms"), "ms")
print("Cost: ", raw.headers.get("x-agentcc-cost"), "USD")
print("Cache: ", raw.headers.get("x-agentcc-cache"))AGENTCC_API_KEY with a key fromapp.futureagi.com.Gateway docs ↗Compare with similar models
Databricks Meta Llama 3.70B Instruct doesn't have a public Arena ELO score yet, so we group by provider only — quality-tier comparisons need a benchmark.
- Databricks Claude 3.7 SonnetDatabricks · $3.00/M in · $15.00/M out · 200,000 ctx
- Databricks Claude Haiku 4.5Databricks · $1.00/M in · $5.00/M out · 200,000 ctx
- Databricks Claude Opus 4Databricks · $15.00/M in · $75.00/M out · 200,000 ctx
- Databricks Claude Opus 4.1Databricks · $15.00/M in · $75.00/M out · 200,000 ctx
FAQ
How much does Databricks Meta Llama 3.70B Instruct cost?
Input is priced at $1.00 per 1M tokens and output at $3.00 per 1M tokens (Databricks, last verified May 12, 2026).
What is the context window of Databricks Meta Llama 3.70B Instruct?
Databricks Meta Llama 3.70B Instruct supports a 128,000-token context window with up to 128,000 output tokens.
Does Databricks Meta Llama 3.70B Instruct support function calling?
Databricks Meta Llama 3.70B Instruct does not currently advertise function-calling support. For agentic workloads, prefer a tool-calling-capable model and route via Agent Command Center for fallback.
Is Databricks Meta Llama 3.70B Instruct good for production?
Databricks Meta Llama 3.70B Instruct is best evaluated against your own production traces. Pipe traffic through Agent Command Center to compare it head-to-head against alternatives in shadow mode.
How can I route to Databricks Meta Llama 3.70B Instruct with fallback?
Use Agent Command Center: a single OpenAI-compatible endpoint that supports cost-optimized routing, latency-aware retries, model fallback, and shadow traffic. Configure once, swap models without app changes.
Useful links for Databricks Meta Llama 3.70B Instruct
Official sources, independent benchmarks, and pricing aggregators — no random search-engine guesses.
Third-party evals — verify the marketing.
Cross-check our number against the rest of the ecosystem.