Llama 3.3 70B Instruct

Novita AI chat

Llama 3.3 70B Instruct is a Novita AI chat model.It supports a 12,288-token context windowwith up to 12,288 output tokens.Input is priced at $0.135/M tokens and output at $0.400/M tokens. Capabilities include function calling. Route Llama 3.3 70B Instruct via Future AGI's Agent Command Center for unified observability, caching, and 15 routing strategies including cost-optimized fallback.

Pricing source: litellm Last verified: Oct 22, 2026 View source ↗
Cost calculator

Estimate Llama 3.3 70B Instruct spend

Pick a workload, fine-tune the sliders, and see the monthly bill.

~3K in / ~400 out · 5K req/day
3,000
012,288
400
012,288
5,000
01,000,000
Per request
$0.000565
in $0.000405 · out $0.000160
Per day
$2.82
5,000 requests
Per month
$85.99
152,188 requests

Estimate uses $0.1350/M input · $0.4000/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 Oct 22, 2026.

Input $0.135/M
Output $0.400/M
Blended (3:1 in:out) $0.201/M
Cross-model comparable price

Context & limits

Context window
12,288 tokens
Max input
12,288 tokens
Max output
12,288 tokens
Modalities
text

Capabilities

  • Function calling ✓ supported
  • Parallel tool calls ✓ supported
  • Vision input — not advertised
  • Audio input — not advertised
  • Audio output — not advertised
  • PDF input — not advertised
  • Streaming ✓ supported
  • Structured output ✓ supported
  • Prompt caching — not advertised
  • Reasoning — not advertised

Strengths & caveats — when to pick Llama 3.3 70B Instruct

Data-driven from Llama 3.3 70B Instruct's percentile within chat peers.

Where it's strong

  • +parallel tool calls — only 18% of chat models on Future AGI advertise this

Watch out for

  • !limited context — 12,288-token window is in the bottom quartile; not ideal for long documents or large RAG
  • !small context (under 16K tokens)

Benchmarks pending

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

Try it

Call Llama 3.3 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.

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.
# Llama 3.3 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="novita-ai/meta-llama-llama-3-3-70b-instruct",
    messages=[{"role": "user", "content": "Hello, Llama 3.3 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="novita-ai/meta-llama-llama-3-3-70b-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"))
Set AGENTCC_API_KEY with a key fromapp.futureagi.com.Gateway docs ↗

Same model on other providers

meta-llama-llama-3-3-70b-instruct is also available via 6 other routes. Pricing, regions, and capabilities can differ — compare before routing production traffic.

ProviderInput / 1MOutput / 1MVerified
W&B Inference$0.710/M$0.710/MOct 22, 2026
DeepInfra$0.230/M$0.400/MOct 22, 2026
Hyperbolic$0.120/M$0.300/MOct 22, 2026
IBM watsonx$0.753/M$0.753/MOct 22, 2026
Nebius$0.130/M$0.400/MOct 22, 2026
Nscale$0.200/M$0.200/MOct 22, 2026

Explore similar models

Llama 3.3 70B Instruct doesn't have a public Arena ELO score yet, so we group by provider only — quality-tier comparisons need a benchmark. Side-by-side links appear where a comparison page exists; otherwise, open the model profile.

FAQ

How much does Llama 3.3 70B Instruct cost?

Input is priced at $0.135 per 1M tokens and output at $0.400 per 1M tokens (Novita AI, last verified Oct 22, 2026).

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

Llama 3.3 70B Instruct supports a 12,288-token context window with up to 12,288 output tokens.

Does Llama 3.3 70B Instruct support function calling?

Yes — Llama 3.3 70B Instruct supports function (tool) calling, including parallel tool calls.

Is Llama 3.3 70B Instruct good for production?

Llama 3.3 70B Instruct is well-suited for parallel tool calls — only 18% of chat models on Future AGI advertise this. Consider alternatives if you need limited context — 12,288-token window is in the bottom quartile; not ideal for long documents or large RAG.

How can I route to Llama 3.3 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 Llama 3.3 70B Instruct

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