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Hermes 3 70B

Added Jan 7, 2026Open weights

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Hermes 3 is a generalist language model with many improvements over Hermes 2, including advanced agentic capabilities, better roleplaying, reasoning, multi-turn conversation, and long context coherence. This 70B model is a competitive finetune of Llama-3.1-70B focused on aligning LLMs to the user with powerful steering capabilities.

  • Tools
Input
Text
Output
Text
Discovery tier
Open weights

Pricing

USD per 1M tokens
Input
$0.41
Output
$0.41

Reference prices as published by the model provider. API example

Specifications

Context window
65.5K
Max output
8.2K
Average output (7 days)
177 tokens
Longer than 18% of 468 models

Side by side

Hermes 3 70B compared

Specifications, prices, speed and benchmark scores next to each other. Benchmarks below show every compared model too.

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Hermes 3 70BnousresearchThis modelMinistral 14BMistralRemove
ProvidernousresearchMistral
Discovery tierOpen weightsOpen weights
AddedJan 7, 2026Dec 4, 2025
Limits
Context window65.54K262.14KBest
Max output8.19K32.77KBest
Average output177 tokens176 tokens
Pricing, USD per 1M tokens
Input$0.41$0.20Best
Output$0.41$0.20Best
Performance
Throughput32.5 tok/s—
Time to first token1.9 s—
Request success (24h)100.0%—
Benchmark indexes
Intelligence Index6.06.0
Coding Index—14.4
Math Index—30.0
Capabilities
ToolsYesNo
Reasoning effort——

Public evaluations

Benchmarks

Independent evaluation results, with how each score ranks among the models in the Noumen catalog.

  • Hermes 3 70B
  • Ministral 14B

Intelligence Index

  • Hermes 3 70B6.0
  • Ministral 14B6.0

Hermes 3 70B: Better than 3% of 326 scored models

3.8Catalog models by score55.1

Indexes

  • Coding Index

    Composite of the coding evaluations

    • Hermes 3 70B: —
    • Ministral 14B: 14.4

  • Math Index

    Composite of the math evaluations

    • Hermes 3 70B: —
    • Ministral 14B: 30.0

Agentic work

  • AutomationBench-AA

    Workflow automation, penalized for guardrail violations

    • Hermes 3 70B: —
    • Ministral 14B: 0.6%

  • AutomationBench-AA tasks completed

    Workflows finished end to end without a guardrail violation

    • Hermes 3 70B: —
    • Ministral 14B: 0.2%

  • AA-Briefcasev1.1

    Agentic knowledge work, rated head to head

    • Hermes 3 70B: —
    • Ministral 14B: 131 Elo

  • GDPval-AAv2.1

    Economically valuable occupational tasks, rated head to head

    • Hermes 3 70B: —
    • Ministral 14B: 247 Elo

Document reasoning

  • GDP.pdf

    Professional reasoning over PDF documents, all-pass rate

    • Hermes 3 70B: —
    • Ministral 14B: 1.2%

  • AA-LCRv1.1

    Reasoning across long documents

    • Hermes 3 70B: —
    • Ministral 14B: 26.3%

  • MLCR-AA

    Long-context reasoning over medical records

    • Hermes 3 70B: —
    • Ministral 14B: 2.2%

Reasoning and knowledge

  • Humanity’s Last Exam

    Expert-level questions across academic fields

    • Hermes 3 70B: 4.0%
    • Ministral 14B: 4.6%Top

  • GPQA Diamond

    Graduate-level science questions

    • Hermes 3 70B: 40.1%
    • Ministral 14B: 57.2%Top

  • MMLU-Pro

    Multi-subject knowledge and reasoning

    • Hermes 3 70B: 57.1%
    • Ministral 14B: 69.3%Top

  • IFBench

    Following precise output instructions

    • Hermes 3 70B: —
    • Ministral 14B: 32.0%

Coding

  • Terminal-Benchv4.0

    Practical software engineering in a terminal

    • Hermes 3 70B: —
    • Ministral 14B: 0.0%

  • SciCode

    Scientific computing in Python

    • Hermes 3 70B: —
    • Ministral 14B: 23.8%

  • LiveCodeBench

    Recent competitive programming problems

    • Hermes 3 70B: 18.8%
    • Ministral 14B: 35.1%Top

Math

  • AIME 2025

    Competition mathematics, 2025 problems

    • Hermes 3 70B: —
    • Ministral 14B: 30.0%

  • AIME

    Competition mathematics

    • Hermes 3 70B: 2.3%
    • Ministral 14B: —

  • MATH-500

    Competition math problems

    • Hermes 3 70B: 53.8%
    • Ministral 14B: —

Legacy evaluations

  • Terminal-Bench Hard

    Earlier terminal task suite

    • Hermes 3 70B: —
    • Ministral 14B: 4.5%

  • τ²-Bench Telecom

    Tool use in a customer-support simulation

    • Hermes 3 70B: —
    • Ministral 14B: 27.2%

  • AA-LCR (unversioned)

    Long-context reasoning, earlier grading

    • Hermes 3 70B: —
    • Ministral 14B: 26.3%

Scores from Artificial Analysis, updated Oct 26, 2026. Artificial Analysis

Live routing data

Performance and reliability

Recent averages across requests routed for this model. Your own speed depends on prompt length, reasoning effort and load.

Throughput
32.5 tok/s
Faster than 22% of 263 models
Time to first token
1.9 s
Quicker than 84% of 263 models
Prompt cache hits
0.0%
Request success (24h)
100.0%
Reliable

Cache hits are the share of input tokens recently served from a prompt cache across all requests. They are not a forecast of your own hit rate.

Hourly request success · last 72 hoursLowest hour 100.0%
  • Healthy (98% or more)
  • Degraded (90–98%)
  • Down (under 90%)
  • No requests

Developers

Use it through the API

Noumen’s API is OpenAI-compatible: any Chat Completions client works with a base URL and a key.

Read the quickstart
curl https://www.noumen.xyz/api/v1/chat/completions \
  -H "Authorization: Bearer $NOUMEN_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "nousresearch/hermes-3-llama-3.1-70b",
    "messages": [{ "role": "user", "content": "Explain this stack trace." }]
  }'
import os
from openai import OpenAI

client = OpenAI(base_url="https://www.noumen.xyz/api/v1", api_key=os.environ["NOUMEN_API_KEY"])
reply = client.chat.completions.create(
    model="nousresearch/hermes-3-llama-3.1-70b",
    messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)

nousresearch

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