Meta Llama Llama 3.3 70B Instruct Turbo
DeepInfra chatMeta Llama Llama 3.3 70B Instruct Turbo is a DeepInfra chat model.It supports a 131,072-token context windowwith up to 131,072 output tokens.Input is priced at $0.130/M tokens and output at $0.390/M tokens. Capabilities include function calling. Route Meta Llama Llama 3.3 70B Instruct Turbo via Future AGI's Agent Command Center for unified observability, caching, and 15 routing strategies including cost-optimized fallback.
Estimate Meta Llama Llama 3.3 70B Instruct Turbo spend
Pick a workload, fine-tune the sliders, and see the monthly bill.
Estimate uses $0.1300/M input · $0.3900/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 | $0.130/M | |
| Output | $0.390/M |
Limits
- Context window
- 131,072 tokens
- Max input
- 131,072 tokens
- Max output
- 131,072 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
- +long-form generation — 131,072-token max output, top-10% of peers
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 Llama 3.3 70B Instruct Turbo yet. Have one to contribute? Submit a source — citations help us prioritise.
Call Meta Llama Llama 3.3 70B Instruct Turbo 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.# Meta Llama Llama 3.3 70B Instruct Turbo 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="deepinfra/meta-llama-llama-3-3-70b-instruct-turbo",
messages=[{"role": "user", "content": "Hello, Meta Llama Llama 3.3 70B Instruct Turbo!"}],
)
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="deepinfra/meta-llama-llama-3-3-70b-instruct-turbo",
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 ↗Same model on other providers
meta-llama-llama-3-3-70b-instruct-turbo is also available via 1 other route. Pricing, regions, and capabilities can differ — compare before routing production traffic.
| Provider | Input / 1M | Output / 1M | Verified |
|---|---|---|---|
| Together AI | $0.880/M | $0.880/M | May 12, 2026 |
Compare with similar models
Meta Llama Llama 3.3 70B Instruct Turbo 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 Llama 3.3 70B Instruct Turbo cost?
Input is priced at $0.130 per 1M tokens and output at $0.390 per 1M tokens (DeepInfra, last verified May 12, 2026).
What is the context window of Meta Llama Llama 3.3 70B Instruct Turbo?
Meta Llama Llama 3.3 70B Instruct Turbo supports a 131,072-token context window with up to 131,072 output tokens.
Does Meta Llama Llama 3.3 70B Instruct Turbo support function calling?
Yes — Meta Llama Llama 3.3 70B Instruct Turbo supports function (tool) calling.
Is Meta Llama Llama 3.3 70B Instruct Turbo good for production?
Meta Llama Llama 3.3 70B Instruct Turbo is well-suited for long-form generation — 131,072-token max output, top-10% of peers. Consider alternatives if you need strict structured output — no JSON-schema enforcement, expect retry loops.
How can I route to Meta Llama Llama 3.3 70B Instruct Turbo 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 Llama 3.3 70B Instruct Turbo
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.