Meta Llama2.70b Chat v1
Amazon Bedrock chatMeta Llama2.70b Chat v1 is an Amazon Bedrock chat model.It supports a 4,096-token context windowwith up to 4,096 output tokens.Input is priced at $1.95/M tokens and output at $2.56/M tokens. Route Meta Llama2.70b Chat v1 via Future AGI's Agent Command Center for unified observability, caching, and 15 routing strategies including cost-optimized fallback.
Estimate Meta Llama2.70b Chat v1 spend
Pick a workload, fine-tune the sliders, and see the monthly bill.
Estimate uses $1.95/M input · $2.56/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 | $1.95/M | |
| Output | $2.56/M |
Limits
- Context window
- 4,096 tokens
- Max input
- 4,096 tokens
- Max output
- 4,096 tokens
- Modalities
- text
Capabilities
- Function calling — not advertised
- 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
Watch out for
- !limited context — 4,096-token window is in the bottom quartile; not ideal for long documents or large RAG
- !agentic workflows — no advertised function-calling; use a tool-capable model and route via Agent Command Center for fallback
- !small context (under 16K tokens)
- !strict structured output — no JSON-schema enforcement, expect retry loops
Benchmarks pending
We haven't logged public benchmark scores for Meta Llama2.70b Chat v1 yet. Have one to contribute? Submit a source — citations help us prioritise.
Call Meta Llama2.70b Chat v1 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 Llama2.70b Chat v1 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-llama2-70b-chat-v1",
messages=[{"role": "user", "content": "Hello, Meta Llama2.70b Chat v1!"}],
)
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-llama2-70b-chat-v1",
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 Llama2.70b Chat v1 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 Llama2.70b Chat v1 cost?
Input is priced at $1.95 per 1M tokens and output at $2.56 per 1M tokens (Amazon Bedrock, last verified May 12, 2026).
What is the context window of Meta Llama2.70b Chat v1?
Meta Llama2.70b Chat v1 supports a 4,096-token context window with up to 4,096 output tokens.
Does Meta Llama2.70b Chat v1 support function calling?
Meta Llama2.70b Chat v1 does not currently advertise function-calling support. For agentic workloads, prefer a tool-calling-capable model and route via Agent Command Center for fallback.
Is Meta Llama2.70b Chat v1 good for production?
Meta Llama2.70b Chat v1 is best evaluated against your own production traces. Pipe traffic through Agent Command Center to compare it head-to-head against alternatives in shadow mode.
How can I route to Meta Llama2.70b Chat v1 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 Llama2.70b Chat v1
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.