Model profile
nousresearch

Nous: Hermes 3 70B Instruct

Nous: Hermes 3 70B Instruct is a budget text-first model from nousresearch with a balanced runtime profile, standard context posture, and the clearest fit around long-context research / agent workflows.

Best for: Long-context research / Agent workflowsBalanced latencyStandard contextBudget pricing
Intelligence
10.6

Benchmark blend

Coding
0.188

Dev workflow signal

Context
66K Tokens

Standard

Input Price
$0.30

Budget tier

Decision snapshot
42

Nous: Hermes 3 70B Instruct currently reads as a budget text-first option with standard context and a balanced runtime profile.

Overall profile
Use-case specific
Best for
Long-context research / Agent workflows
Latency tier
Balanced
Price tier
Budget
Source coverage
OpenRouterArtificial Analysis

Decision Strip

Decision rail before the raw tables

Core buy-side signals stay in one pass. The rest of the page expands only after intelligence, speed, context, and price are clear.

Intelligence
10.6
11

General reasoning and benchmark headroom.

Limited
Speed
44 tok/s
66

TTFT 0.28s

Competitive
Context
66K Tokens
64

How much prompt and task state can stay in view.

Competitive
Price
$0.30
86

$0.30 output / 1M

Efficient

Editorial Profile

Nous: Hermes 3 70B Instruct in one narrative

Positioning, tradeoffs, and fit are consolidated into one read instead of repeating the same story across separate cards.

Use-case specificCoding score 19Math score 54

Hermes 3 is a generalist language model with many improvements over [Hermes 2](/models/nousresearch/nous-hermes-2-mistral-7b-dpo), including advanced agentic capabilities, much better roleplaying, reasoning, multi-turn conversation, long context coherence, and improvements across the board. Hermes 3 70B is a competitive, if not superior finetune of the [Llama-3.1 70B foundation model](/models/meta-llama/llama-3.1-70b-instruct), focused on aligning LLMs to the user, with powerful steering capabilities and control given to the end user. The Hermes 3 series builds and expands on the Hermes 2 set of capabilities, including more powerful and reliable function calling and structured output capabilities, generalist assistant capabilities, and improved code generation skills.

Identity

nousresearch text-first profile

Positioning

Long-context research / Agent workflows with standard context and balanced runtime.

Cost posture

Efficient spend profile. More comfortable for sustained prompt volume if the capability fit is right.

Strengths
  • The available source data suggests a balanced profile rather than one dominant edge.

Tradeoffs
  • Budget-friendly input pricing is a strength, but raw capability may vary by workload.

  • Latency is balanced rather than ultra-fast, which is fine for most workflows but not the snappiest tier.

  • Current metadata points to a text-first profile rather than a broad multimodal one.

  • Context window is more comfortable for focused tasks than extremely long sessions.

Best fit
  • Focused chat, retrieval-augmented flows, and narrower production tasks.

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Benchmarks

Grouped by job-to-be-done

Only benchmark categories with actual signal are shown. Secondary values stay as simple definitions instead of nested micro-cards.

General intelligence

Broad reasoning, knowledge depth, and flagship benchmark posture.

Intelligence Index
10.6
MMLU Pro
57.1%
GPQA
40.1%
HLE
4.1%
Coding

Software implementation, debugging quality, and coding benchmark signal.

LiveCodeBench
0.188
SciCode
23.1%
Math

Formal reasoning, structured problem solving, and competition-style math.

AIME
2.3%
Math 500
53.8%

Specs & Pricing

Technical snapshot and cost posture

Specs stay neutral, pricing gets emphasis through values rather than extra containers. Raw provider internals remain in metadata at the end.

Technical snapshot
Context Window
66K Tokens
Vision
Text-first
Modalities
text->text, text
Tokenizer
Llama3
Max Completion
65536
Moderation
No
Supported Parameters
frequency_penaltylogprobsmax_tokensmin_ppresence_penaltyrepetition_penaltyresponse_formatseedstopstructured_outputstemperaturetop_ktop_logprobstop_p
Input Modalities
text
Output Modalities
text
Price architecture
Input
per 1M input tokens
$0.30
Output
per 1M output tokens
$0.30
Blended
AA 3:1 mix
$0.30

This model is relatively efficient on price. It is the easier fit when sustained prompt volume matters.

Metadata

Raw source tables at the end

Verification details remain available, but the page no longer forces them ahead of the editorial read.