Meta Llama 3.3 70B Instruct Fp8 Dynamic

Oracle Cloud chat

Meta Llama 3.3 70B Instruct Fp8 Dynamic is an Oracle Cloud chat model.It supports a 128,000-token context windowwith up to 4,000 output tokens.Input is priced at $0.720/M tokens and output at $0.720/M tokens. Capabilities include function calling. Route Meta Llama 3.3 70B Instruct Fp8 Dynamic via Future AGI's Agent Command Center for unified observability, caching, and 15 routing strategies including cost-optimized fallback.

Pricing source: litellm Last verified: May 12, 2026 View source ↗
Cost calculator

Estimate Meta Llama 3.3 70B Instruct Fp8 Dynamic spend

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

~3K in / ~400 out · 5K req/day
3,000
0128,000
400
04,000
5,000
01,000,000
Per request
$0.002448
in $0.002160 · out $0.000288
Per day
$12.24
5,000 requests
Per month
$373
152,188 requests

Estimate uses $0.7200/M input · $0.7200/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 $0.720/M
Output $0.720/M

Limits

Context window
128,000 tokens
Max input
128,000 tokens
Max output
4,000 tokens
Modalities
text

Capabilities

  • Function calling ✓ supported
  • 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

  • +agentic workflows that depend on reliable tool calls

Watch out for

  • !strict structured output — no JSON-schema enforcement, expect retry loops

Benchmarks pending

We haven't logged public benchmark scores for Meta Llama 3.3 70B Instruct Fp8 Dynamic yet. Have one to contribute? Submit a source — citations help us prioritise.

Try it

Call Meta Llama 3.3 70B Instruct Fp8 Dynamic 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.
# Meta Llama 3.3 70B Instruct Fp8 Dynamic 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="oci/meta-llama-3-3-70b-instruct-fp8-dynamic",
    messages=[{"role": "user", "content": "Hello, Meta Llama 3.3 70B Instruct Fp8 Dynamic!"}],
)

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="oci/meta-llama-3-3-70b-instruct-fp8-dynamic",
    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 ↗

Compare with similar models

Meta Llama 3.3 70B Instruct Fp8 Dynamic doesn't have a public Arena ELO score yet, so we group by provider only — quality-tier comparisons need a benchmark.

FAQ

How much does Meta Llama 3.3 70B Instruct Fp8 Dynamic cost?

Input is priced at $0.720 per 1M tokens and output at $0.720 per 1M tokens (Oracle Cloud, last verified May 12, 2026).

What is the context window of Meta Llama 3.3 70B Instruct Fp8 Dynamic?

Meta Llama 3.3 70B Instruct Fp8 Dynamic supports a 128,000-token context window with up to 4,000 output tokens.

Does Meta Llama 3.3 70B Instruct Fp8 Dynamic support function calling?

Yes — Meta Llama 3.3 70B Instruct Fp8 Dynamic supports function (tool) calling.

Is Meta Llama 3.3 70B Instruct Fp8 Dynamic good for production?

Meta Llama 3.3 70B Instruct Fp8 Dynamic is well-suited for agentic workflows that depend on reliable tool calls. Consider alternatives if you need strict structured output — no JSON-schema enforcement, expect retry loops.

How can I route to Meta Llama 3.3 70B Instruct Fp8 Dynamic 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 Meta Llama 3.3 70B Instruct Fp8 Dynamic

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