Meta Llama3.1 405B Instruct v1.0
Amazon Bedrock chatMeta Llama3.1 405B Instruct v1.0 is an Amazon Bedrock chat model.It supports a 128,000-token context windowwith up to 4,096 output tokens.Input is priced at $5.32/M tokens and output at $16.00/M tokens. Capabilities include function calling. Route Meta Llama3.1 405B Instruct v1.0 via Future AGI's Agent Command Center for unified observability, caching, and 15 routing strategies including cost-optimized fallback.
Estimate Meta Llama3.1 405B Instruct v1.0 spend
Pick a workload, fine-tune the sliders, and see the monthly bill.
Estimate uses $5.32/M input · $16.00/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 | $5.32/M | |
| Output | $16.00/M |
Limits
- Context window
- 128,000 tokens
- Max input
- 128,000 tokens
- Max output
- 4,096 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
- +pricing — cheaper than 91% of priced chat models on Future AGI
Watch out for
- !strict structured output — no JSON-schema enforcement, expect retry loops
Benchmarks pending
We haven't logged public benchmark scores for Meta Llama3.1 405B Instruct v1.0 yet. Have one to contribute? Submit a source — citations help us prioritise.
Call Meta Llama3.1 405B Instruct v1.0 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 Llama3.1 405B Instruct v1.0 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="bedrock/meta-llama3-1-405b-instruct-v1-0",
messages=[{"role": "user", "content": "Hello, Meta Llama3.1 405B Instruct v1.0!"}],
)
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="bedrock/meta-llama3-1-405b-instruct-v1-0",
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 ↗Compare with similar models
Meta Llama3.1 405B Instruct v1.0 doesn't have a public Arena ELO score yet, so we group by provider only — quality-tier comparisons need a benchmark.
- DeepSeek v3.2Amazon Bedrock · $0.620/M in · $1.85/M out · 163,840 ctx
- Anthropic Claude Haiku 4.5 (2025-10-01)Amazon Bedrock · $1.00/M in · $5.00/M out · 200,000 ctx
- Amazon Nova 2 Lite v1.0Amazon Bedrock · $0.300/M in · $2.50/M out · 1,000,000 ctx
- Amazon Nova 2 Pro preview 20251202 v1.0Amazon Bedrock · $2.19/M in · $17.50/M out · 1,000,000 ctx
FAQ
How much does Meta Llama3.1 405B Instruct v1.0 cost?
Input is priced at $5.32 per 1M tokens and output at $16.00 per 1M tokens (Amazon Bedrock, last verified May 12, 2026).
What is the context window of Meta Llama3.1 405B Instruct v1.0?
Meta Llama3.1 405B Instruct v1.0 supports a 128,000-token context window with up to 4,096 output tokens.
Does Meta Llama3.1 405B Instruct v1.0 support function calling?
Yes — Meta Llama3.1 405B Instruct v1.0 supports function (tool) calling.
Is Meta Llama3.1 405B Instruct v1.0 good for production?
Meta Llama3.1 405B Instruct v1.0 is well-suited for pricing — cheaper than 91% of priced chat models on Future AGI. Consider alternatives if you need strict structured output — no JSON-schema enforcement, expect retry loops.
How can I route to Meta Llama3.1 405B Instruct v1.0 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 Llama3.1 405B Instruct v1.0
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.