AI model comparison

Llama 3.1 8B Instruct vs Mistral Nemo

Pricing, context window, benchmarks, and features compared side by side. Or skip the guesswork and send one prompt to both.

Meta's latest class of model (Llama 3.1) launched with a variety of sizes & flavors. This 8B instruct-tuned version is fast and efficient.

Input / 1M
$0.05
Output / 1M
$0.08
Context
131K

A 12B parameter model with a 128k token context length built by Mistral in collaboration with NVIDIA.

Input / 1M
$0.03
Output / 1M
$0.03
Context
131K

At a glance

Quick verdict

How the two models stack up on the things people ask about most.

Lower price

mistralai logo

Mistral Nemo

1.9x cheaper for a typical chat

Higher intelligence score

Tie

Not enough benchmark data

Larger context window

Tie

131K vs 131K tokens

Newer release

meta-llama logo

Llama 3.1 8B Instruct

Released July 23, 2024

Benchmarks

Benchmark scores

Independent scores from Artificial Analysis. Higher is better.

Intelligence index

meta-llama logoLlama 3.1 8B Instruct6.9
mistralai logoMistral NemoNot available

Coding index

meta-llama logoLlama 3.1 8B Instruct5.4
mistralai logoMistral NemoNot available

Agentic index

meta-llama logoLlama 3.1 8B InstructNot available
mistralai logoMistral NemoNot available

Pricing

Llama 3.1 8B Instruct vs Mistral Nemo API pricing

Per-token API rates. Cheaper option highlighted.

Metricmeta-llama logoLlama 3.1 8B Instructmistralai logoMistral Nemo
Input tokensPer 1M tokens$0.05$0.03
Output tokensPer 1M tokens$0.08$0.03
Cached input (read)Per 1M tokens$0.03Not available
Cache writePer 1M tokensNot availableNot available

What it costs in practice

Estimated cost per 1,000 requests at standard rates. Coding and long-document figures also show the cost when the input is already cached.

Chat message

2K in, 500 out

meta-llama logoLlama 3.1 8B Instruct$0.14
mistralai logoMistral Nemo$0.07

Coding task

30K in, 4K out

meta-llama logoLlama 3.1 8B Instruct$1.82$1.07 cached
mistralai logoMistral Nemo$0.99

Long document summary

150K in, 2K out

meta-llama logoLlama 3.1 8B Instruct$7.66$3.91 cached
mistralai logoMistral Nemo$4.41

Specs

Context window and capabilities

How much each model can read, how much it can write, and what it accepts as input.

Metricmeta-llama logoLlama 3.1 8B Instructmistralai logoMistral Nemo
Context window131K tokens131K tokens
Max output118K tokens16K tokens
Input typesTextText
Output typesTextText
Reasoning effort levelsNot availableNot available
Default reasoning effortNot availableNot available
Release dateJuly 23, 2024July 19, 2024

Features

Supported features

API features available for each model.

Metricmeta-llama logoLlama 3.1 8B Instructmistralai logoMistral Nemo
Tool callingSupportedSupported
Structured outputsSupportedSupported
JSON modeSupportedSupported
ReasoningNot supportedNot supported
TemperatureSupportedSupported
Stop sequencesSupportedSupported
Deterministic seedSupportedSupported
Verbosity controlNot supportedNot supported

Our take

Mistral Nemo is 1.9x cheaper for a typical chat. The right pick depends on your workload, so the quickest way to settle it is to send the same prompt to both and compare.

FAQ

Frequently asked questions

Is Llama 3.1 8B Instruct or Mistral Nemo cheaper?

Mistral Nemo is cheaper for a typical chat (2K input tokens and 500 output tokens). Mistral Nemo costs $0.03 per 1M input tokens and $0.03 per 1M output tokens, while Llama 3.1 8B Instruct costs $0.05 per 1M input tokens and $0.08 per 1M output tokens.

Which has a bigger context window, Llama 3.1 8B Instruct or Mistral Nemo?

Llama 3.1 8B Instruct supports up to 131K tokens of context, compared with 131K for Mistral Nemo.

Can I use Llama 3.1 8B Instruct and Mistral Nemo at the same time?

Yes. Shortcut Chat sends one prompt to multiple models at once, so you can see Llama 3.1 8B Instruct and Mistral Nemo answer side by side and keep the better response.

meta-llama logomistralai logo

Why choose? Ask both.

Send one prompt to Llama 3.1 8B Instruct and Mistral Nemo at the same time. Compare the answers side by side and keep the best one.