Llama2.70b Chat

Snowflake Cortex chat

Llama2.70b Chat is a Snowflake Cortex chat model.It supports a 4,096-token context windowwith up to 8,192 output tokens. Route Llama2.70b Chat 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 Llama2.70b Chat 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
4,096 tokens
Max input
4,096 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 — 4,096-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 Llama2.70b Chat yet. Have one to contribute? Submit a source — citations help us prioritise.

Try it

Call Llama2.70b Chat 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.
# Llama2.70b Chat 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/llama2-70b-chat",
    messages=[{"role": "user", "content": "Hello, Llama2.70b Chat!"}],
)

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/llama2-70b-chat",
    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 Llama2.70b Chat cost?

Public per-token pricing for Llama2.70b Chat is not yet published. Submit a source on this page to help us add it.

What is the context window of Llama2.70b Chat?

Llama2.70b Chat supports a 4,096-token context window with up to 8,192 output tokens.

Does Llama2.70b Chat support function calling?

Llama2.70b Chat 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 Llama2.70b Chat good for production?

Llama2.70b Chat 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 Llama2.70b Chat 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 Llama2.70b Chat

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