nousresearch/hermes-4-405b
131,072 context · $1.00/M input tokens · $3.00/M output tokens
Hermes 4 is a large-scale reasoning model built on Meta-Llama-3.1-405B and released by Nous Research. It introduces a hybrid reasoning mode, where the model can choose to deliberate internally with...
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Keine Vorabkosten, zahlen Sie nur, was Sie nutzen
Verwenden Sie die folgenden Codebeispiele zur Integration mit unserer API:
from openai import OpenAI
client = OpenAI(
api_key="YOUR_API_KEY",
base_url="https://llm.wavespeed.ai/v1"
)
response = client.chat.completions.create(
model="nousresearch/hermes-4-405b",
messages=[
{"role": "user", "content": "Hello!"}
]
)
print(response.choices[0].message.content)Hermes 4 is a large-scale reasoning model built on Meta-Llama-3
Hermes 4 is a large-scale reasoning model built on Meta-Llama-3.1-405B and released by Nous Research. It introduces a hybrid reasoning mode, where the model can choose to deliberate internally with <think>...</think> traces or respond directly, offering flexibility between speed and depth. Users can control the reasoning behaviour with the reasoning enabled boolean. Learn more in our docs
The model is instruction-tuned with an expanded post-training corpus (~60B tokens) emphasizing reasoning traces, improving performance in math, code, STEM, and logical reasoning, while retaining broad assistant utility. It also supports structured outputs, including JSON mode, schema adherence, function calling, and tool use. Hermes 4 is trained for steerability, lower refusal rates, and alignment toward neutral, user-directed behavior.
| Specification | Value |
|---|---|
| Provider | Nousresearch |
| Model Type | Large Language Model (LLM) |
| Architecture | N/A |
| Context Window | 131072 tokens |
| Max Output | tokens |
| Input | Text |
| Output | Text |
| Vision | Supported |
| Function Calling | Supported |
| Token Type | Cost per Million Tokens |
|---|---|
| Input | $1.1 |
| Output | $3.3 |
Base URL: https://llm.wavespeed.ai/v1 API Endpoint: chat/completions Model ID: nousresearch/hermes-4-405b
from openai import OpenAI
client = OpenAI(
api_key="YOUR_API_KEY",
base_url="https://llm.wavespeed.ai/v1"
)
response = client.chat.completions.create(
model="nousresearch/hermes-4-405b",
messages=[
{"role": "user", "content": "Hello!"}
]
)
print(response.choices[0].message.content)
curl https://llm.wavespeed.ai/v1/chat/completions \
-H "Content-Type: application/json" \
-H "Authorization: Bearer YOUR_API_KEY" \
-d '{
"model": "nousresearch/hermes-4-405b",
"messages": [{"role": "user", "content": "Hello!"}]
}'
nousresearch/hermes-4-405b
Hermes 4 is a large-scale reasoning model built on Meta-Llama-3.1-405B and released by Nous Research. It introduces a hybrid reasoning mode, where the model can choose to deliberate internally with...
Eingabe
$1 /M
Ausgabe
$3 /M
Kontext
131K
Zugriff auf Hermes 4 405b über unsere einheitliche API — OpenAI-kompatibel, keine Kaltstarts, transparente Preise.
Preise auf WaveSpeedAI: $1.00 pro Million Input-Tokens und $3.00 pro Million Output-Tokens. Prompt-Caching und Batch-Verarbeitung werden separat berechnet und reduzieren die effektiven Kosten bei langen, sich wiederholenden Workloads.
Hermes 4 405b unterstützt bis zu 131K Kontext-Tokens und bis zu — Output-Tokens pro Anfrage.
Ja. WaveSpeedAI stellt Hermes 4 405b über einen OpenAI-kompatiblen Endpunkt unter https://llm.wavespeed.ai/v1 bereit. Richten Sie das offizielle OpenAI SDK mit Ihrem WaveSpeedAI-API-Schlüssel auf diese Base-URL — keine weiteren Codeänderungen erforderlich.
Bei WaveSpeedAI anmelden, in Access Keys einen API-Schlüssel erstellen und eine Anfrage an https://llm.wavespeed.ai/v1/chat/completions mit der oben angezeigten Model-ID senden. Neue Konten erhalten kostenlose Credits, um Hermes 4 405b zu testen.