Meta Llama Llama 2.7B Chat Hf
Anyscale chatMeta Llama Llama 2.7B Chat Hf is an Anyscale chat model.It supports a 4,096-token context windowwith up to 4,096 output tokens.Input is priced at $0.150/M tokens and output at $0.150/M tokens. Route Meta Llama Llama 2.7B Chat Hf via Future AGI's Agent Command Center for unified observability, caching, and 15 routing strategies including cost-optimized fallback.
Estimate Meta Llama Llama 2.7B Chat Hf 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 May 12, 2026.
| Input | $0.150/M | |
| Output | $0.150/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
- !high cost — input + output rates are in the top 89% of priced chat peers; consider a cheaper sibling for high-volume workloads
- !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 Llama Llama 2.7B Chat Hf yet. Have one to contribute? Submit a source — citations help us prioritise.
Call Meta Llama Llama 2.7B Chat Hf 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 2.7B Chat Hf 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="anyscale/meta-llama-llama-2-7b-chat-hf",
messages=[{"role": "user", "content": "Hello, Meta Llama Llama 2.7B Chat Hf!"}],
)
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="anyscale/meta-llama-llama-2-7b-chat-hf",
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 ↗FAQ
How much does Meta Llama Llama 2.7B Chat Hf cost?
Input is priced at $0.150 per 1M tokens and output at $0.150 per 1M tokens (Anyscale, last verified May 12, 2026).
What is the context window of Meta Llama Llama 2.7B Chat Hf?
Meta Llama Llama 2.7B Chat Hf supports a 4,096-token context window with up to 4,096 output tokens.
Does Meta Llama Llama 2.7B Chat Hf support function calling?
Meta Llama Llama 2.7B Chat Hf 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 Llama Llama 2.7B Chat Hf good for production?
Meta Llama Llama 2.7B Chat Hf 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 Llama Llama 2.7B Chat Hf 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 2.7B Chat Hf
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.