Llama 3.2 3B Instruct v1.0 (US)
Amazon Bedrock chat Deprecated 120d agomodel fallback routing to swap models without code changes.
Llama 3.2 3B Instruct v1.0 (US) is an Amazon Bedrock chat model.It supports a 128,000-token context windowwith up to 4,096 output tokens.Input is priced at $0.150/M tokens and output at $0.150/M tokens. Capabilities include function calling. Route Llama 3.2 3B Instruct v1.0 (US) via Future AGI's Agent Command Center for unified observability, caching, and 15 routing strategies including cost-optimized fallback.
Estimate Llama 3.2 3B Instruct v1.0 (US) spend
Pick a workload, fine-tune the sliders, and see the monthly bill.
Estimate uses $0.1500/M input · $0.1500/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 Oct 22, 2026.
| Input | $0.150/M | |
| Output | $0.150/M | |
| Blended (3:1 in:out) | $0.150/M Cross-model comparable price |
Context & 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
Strengths & caveats — when to pick Llama 3.2 3B Instruct v1.0 (US)
Data-driven from Llama 3.2 3B Instruct v1.0 (US)'s percentile within chat peers.
Where it's strong
- +agentic workflows that depend on reliable tool calls
Watch out for
- !high cost — input + output rates are in the top 91% of priced chat peers; consider a cheaper sibling for high-volume workloads
- !strict structured output — no JSON-schema enforcement, expect retry loops
- !already deprecated — provider stopped accepting new traffic 120 days ago
Benchmarks pending
We haven't logged public benchmark scores for Llama 3.2 3B Instruct v1.0 (US) yet. Have one to contribute? Submit a source — citations help us prioritise.
Call Llama 3.2 3B Instruct v1.0 (US) 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.# Llama 3.2 3B Instruct v1.0 (US) 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/us-meta-llama3-2-3b-instruct-v1-0",
messages=[{"role": "user", "content": "Hello, Llama 3.2 3B Instruct v1.0 (US)!"}],
)
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/us-meta-llama3-2-3b-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 ↗Explore similar models
Llama 3.2 3B Instruct v1.0 (US) doesn't have a public Arena ELO score yet, so we group by provider only — quality-tier comparisons need a benchmark. Side-by-side links appear where a comparison page exists; otherwise, open the model profile.
- DeepSeek v3.2Amazon Bedrock · $0.620/M in · $1.85/M out · 164,000 ctxView model →
- Claude Haiku 4.5 (2025-10-01)Amazon Bedrock · $1.00/M in · $5.00/M out · 200,000 ctxView model →
- Amazon Nova 2 Lite v1.0Amazon Bedrock · $0.300/M in · $2.50/M out · 1,000,000 ctxView model →
- Amazon Nova 2 Pro preview v1.0 (2025-12-02)Amazon Bedrock · $1.25/M in · $10.00/M out · 1,000,000 ctxView model →
FAQ
How much does Llama 3.2 3B Instruct v1.0 (US) cost?
Input is priced at $0.150 per 1M tokens and output at $0.150 per 1M tokens (Amazon Bedrock, last verified Oct 22, 2026).
What is the context window of Llama 3.2 3B Instruct v1.0 (US)?
Llama 3.2 3B Instruct v1.0 (US) supports a 128,000-token context window with up to 4,096 output tokens.
Does Llama 3.2 3B Instruct v1.0 (US) support function calling?
Yes — Llama 3.2 3B Instruct v1.0 (US) supports function (tool) calling.
Is Llama 3.2 3B Instruct v1.0 (US) good for production?
Llama 3.2 3B Instruct v1.0 (US) is well-suited for agentic workflows that depend on reliable tool calls. Consider alternatives if you need high cost — input + output rates are in the top 91% of priced chat peers; consider a cheaper sibling for high-volume workloads.
How can I route to Llama 3.2 3B Instruct v1.0 (US) 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 Llama 3.2 3B Instruct v1.0 (US)
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.