Nousresearch Hermes 3 Llama 3.1 70B

Hyperbolic chat

Nousresearch Hermes 3 Llama 3.1 70B is a Hyperbolic chat model.It supports a 32,768-token context windowwith up to 32,768 output tokens.Input is priced at $0.120/M tokens and output at $0.300/M tokens. Capabilities include function calling. Route Nousresearch Hermes 3 Llama 3.1 70B 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 Nousresearch Hermes 3 Llama 3.1 70B spend

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

~3K in / ~400 out · 5K req/day
3,000
032,768
400
032,768
5,000
01,000,000
Per request
$0.000480
in $0.000360 · out $0.000120
Per day
$2.40
5,000 requests
Per month
$73.05
152,188 requests

Estimate uses $0.1200/M input · $0.3000/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 $0.120/M
Output $0.300/M

Limits

Context window
32,768 tokens
Max input
32,768 tokens
Max output
32,768 tokens
Modalities
text

Capabilities

  • Function calling ✓ supported
  • Parallel tool calls ✓ supported
  • 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

  • +parallel tool calls — only 21% of chat models on Future AGI advertise this

Watch out for

  • !limited context — 32,768-token window is in the bottom quartile; not ideal for long documents or large RAG
  • !strict structured output — no JSON-schema enforcement, expect retry loops

Benchmarks pending

We haven't logged public benchmark scores for Nousresearch Hermes 3 Llama 3.1 70B yet. Have one to contribute? Submit a source — citations help us prioritise.

Try it

Call Nousresearch Hermes 3 Llama 3.1 70B 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.
# Nousresearch Hermes 3 Llama 3.1 70B 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="hyperbolic/nousresearch-hermes-3-llama-3-1-70b",
    messages=[{"role": "user", "content": "Hello, Nousresearch Hermes 3 Llama 3.1 70B!"}],
)

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="hyperbolic/nousresearch-hermes-3-llama-3-1-70b",
    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

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

ProviderInput / 1MOutput / 1MVerified
DeepInfra$0.300/M$0.300/MMay 12, 2026

FAQ

How much does Nousresearch Hermes 3 Llama 3.1 70B cost?

Input is priced at $0.120 per 1M tokens and output at $0.300 per 1M tokens (Hyperbolic, last verified May 12, 2026).

What is the context window of Nousresearch Hermes 3 Llama 3.1 70B?

Nousresearch Hermes 3 Llama 3.1 70B supports a 32,768-token context window with up to 32,768 output tokens.

Does Nousresearch Hermes 3 Llama 3.1 70B support function calling?

Yes — Nousresearch Hermes 3 Llama 3.1 70B supports function (tool) calling, including parallel tool calls.

Is Nousresearch Hermes 3 Llama 3.1 70B good for production?

Nousresearch Hermes 3 Llama 3.1 70B is well-suited for parallel tool calls — only 21% of chat models on Future AGI advertise this. Consider alternatives if you need limited context — 32,768-token window is in the bottom quartile; not ideal for long documents or large RAG.

How can I route to Nousresearch Hermes 3 Llama 3.1 70B 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 Nousresearch Hermes 3 Llama 3.1 70B

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