NVIDIA: esmfoldWork with me

NVIDIA: esmfold

byNVIDIA

esmfold is a language model from NVIDIA released on Mar 15, 2024. It reads text and writes text, with a 128K token context window and up to 8K tokens per reply.

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It costs $0 per million input tokens and $0 per million output tokens.

Its weights are open, so you can host it yourself.

Modalities
In / out price
$0 / $0 per 1M
Context
128K
Max output
8K
Released
Mar 15, 2024
Knowledge cutoff
n/a
  • Tool calling
  • Structured output
  • Reasoning
  • Prompt caching
  • Open weights
  • Free to use

esmfold pricing

USD, as listed on Oct 15, 2026. Most bills are input heavy, so the cached input rate matters as much as the headline price.

Input$0 / 1M tokens
Output$0 / 1M tokens

What esmfold costs per month

Pick a workload close to yours or type your own volumes.

esmfold per month
$0
Without caching
$0
Per request
$0

Supported request parameters

What you can set when you call esmfold through an OpenAI compatible API.

  • Temperaturetemperature

Alternatives to esmfold

Models that take the same inputs, closest in price, cheaper ones first.

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All NVIDIA models

esmfold questions

How much does the esmfold API cost?

$0 per million input tokens and $0 per million output tokens. The estimator on this page turns that into a monthly bill for your own workload.

What is the context window of esmfold?

128,000 tokens of input, and it can write up to 8,192 tokens in one reply. As a rough guide, a million tokens is around 750,000 English words.

Does esmfold support tool calling?

Not through the standard tools parameter. For an agent that calls your APIs, pick a model from the alternatives below that does.

Is esmfold open source?

Its weights are published, so you can run it on your own hardware or a cloud of your choice. Read the license before commercial use.

When was esmfold released?

Mar 15, 2024.

Catalog data from models.dev (MIT licensed), refreshed daily, last on Oct 15, 2026. The summary is written from these facts.

Building on esmfold?

I test your real tasks on it and the two closest alternatives, then ship the agent on whichever wins on cost and accuracy.

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