Snowflake Llama 3.3 70B

Snowflake Cortex chat

Snowflake Llama 3.3 70B is a Snowflake Cortex chat model.It supports a 8,000-token context windowwith up to 8,192 output tokens. Route Snowflake Llama 3.3 70B via Future AGI's Agent Command Center for unified observability, caching, and 15 routing strategies including cost-optimized fallback.

Pricing source: unknown Last verified: May 12, 2026 View source ↗
Pricing not yet public

We don't have verified per-token pricing for Snowflake Llama 3.3 70B yet. If you have a source from Snowflake Cortex's documentation, help us add it — your submission gets reviewed within 48 hours.

Pricing

Per-token rates, expressed in USD per 1M tokens. Verified May 12, 2026.

Input
Output

Limits

Context window
8,000 tokens
Max input
8,000 tokens
Max output
8,192 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

  • !limited context — 8,000-token window is in the bottom quartile; not ideal for long documents or large RAG
  • !agentic workflows — no advertised function-calling; use a tool-capable model and route via Agent Command Center for fallback
  • !small context (under 16K tokens)
  • !strict structured output — no JSON-schema enforcement, expect retry loops

Benchmarks pending

We haven't logged public benchmark scores for Snowflake Llama 3.3 70B yet. Have one to contribute? Submit a source — citations help us prioritise.

Try it

Call Snowflake Llama 3.3 70B 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.

SDK
Native Future AGI client (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.
# Snowflake Llama 3.3 70B 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="snowflake-cortex/snowflake-llama-3-3-70b",
    messages=[{"role": "user", "content": "Hello, Snowflake Llama 3.3 70B!"}],
)

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="snowflake-cortex/snowflake-llama-3-3-70b",
    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"))
Set AGENTCC_API_KEY with a key fromapp.futureagi.com.Gateway docs ↗

FAQ

How much does Snowflake Llama 3.3 70B cost?

Public per-token pricing for Snowflake Llama 3.3 70B is not yet published. Submit a source on this page to help us add it.

What is the context window of Snowflake Llama 3.3 70B?

Snowflake Llama 3.3 70B supports a 8,000-token context window with up to 8,192 output tokens.

Does Snowflake Llama 3.3 70B support function calling?

Snowflake Llama 3.3 70B 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 Snowflake Llama 3.3 70B good for production?

Snowflake Llama 3.3 70B 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 Snowflake Llama 3.3 70B 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 Snowflake Llama 3.3 70B

Official sources, independent benchmarks, and pricing aggregators — no random search-engine guesses.