Meta Llama Meta Llama 3.1 70B Instruct Turbo

DeepInfra chat

Meta Llama Meta Llama 3.1 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 $0.1000/M tokens and output at $0.280/M tokens. Capabilities include function calling. Route Meta Llama Meta Llama 3.1 70B Instruct Turbo 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 Meta Llama 3.1 70B Instruct Turbo spend

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

~3K in / ~400 out · 5K req/day
3,000
0131,072
400
0131,072
5,000
01,000,000
Per request
$0.000412
in $0.000300 · out $0.000112
Per day
$2.06
5,000 requests
Per month
$62.70
152,188 requests

Estimate uses $0.1000/M input · $0.2800/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 $0.1000/M
Output $0.280/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

  • !high cost — input + output rates are in the top 86% of priced chat peers; consider a cheaper sibling for high-volume workloads
  • !strict structured output — no JSON-schema enforcement, expect retry loops

Benchmarks pending

We haven't logged public benchmark scores for Meta Llama Meta Llama 3.1 70B Instruct Turbo yet. Have one to contribute? Submit a source — citations help us prioritise.

Try it

Call Meta Llama Meta Llama 3.1 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.

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 Meta Llama 3.1 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-meta-llama-3-1-70b-instruct-turbo",
    messages=[{"role": "user", "content": "Hello, Meta Llama Meta Llama 3.1 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-meta-llama-3-1-70b-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"))
Set AGENTCC_API_KEY with a key fromapp.futureagi.com.Gateway docs ↗

Same model on other providers

meta-llama-meta-llama-3-1-70b-instruct-turbo is also available via 1 other route. Pricing, regions, and capabilities can differ — compare before routing production traffic.

ProviderInput / 1MOutput / 1MVerified
Together AI$0.880/M$0.880/MMay 12, 2026

Compare with similar models

Meta Llama Meta Llama 3.1 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 Meta Llama 3.1 70B Instruct Turbo cost?

Input is priced at $0.1000 per 1M tokens and output at $0.280 per 1M tokens (DeepInfra, last verified May 12, 2026).

What is the context window of Meta Llama Meta Llama 3.1 70B Instruct Turbo?

Meta Llama Meta Llama 3.1 70B Instruct Turbo supports a 131,072-token context window with up to 131,072 output tokens.

Does Meta Llama Meta Llama 3.1 70B Instruct Turbo support function calling?

Yes — Meta Llama Meta Llama 3.1 70B Instruct Turbo supports function (tool) calling.

Is Meta Llama Meta Llama 3.1 70B Instruct Turbo good for production?

Meta Llama Meta Llama 3.1 70B Instruct Turbo is well-suited for long-form generation — 131,072-token max output, top-10% of peers. Consider alternatives if you need high cost — input + output rates are in the top 86% of priced chat peers; consider a cheaper sibling for high-volume workloads.

How can I route to Meta Llama Meta Llama 3.1 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 Meta Llama 3.1 70B Instruct Turbo

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