SSyncDev

AI model comparison

Muse Spark 1.1 vs Llama Nemotron Embed VL 1B v2

Compare recorded price, context, output limits, speed, latency, capabilities, and input/output support. Every missing value stays visible, and unsourced benchmarks are excluded.

This Muse Spark 1.1 vs Llama Nemotron Embed VL 1B v2 comparison is designed for teams choosing between two production model records, not for declaring a universal winner. Muse Spark 1.1 by Meta currently records 1M context, 32K maximum output, $1.25 input per 1M tokens, $4.25 output per 1M tokens. Llama Nemotron Embed VL 1B v2 by NVIDIA records 32.8K context, 2.0K maximum output, $0.00 input per 1M tokens, $0.00 output per 1M tokens. The tables below separate documented capabilities, commercial limits, directory measurements, and source-linked benchmark evidence so you can see where the data is complete and where independent testing is still required.

Side-by-sideSpecs and pricing, compared

Meta

Muse Spark 1.1

Muse Spark is a natively multimodal reasoning model with support for tool-use, visual chain of thought, and multi-agent orchestration.

Context
1M
Max output
32K
Input / 1M
$1.25
Output / 1M
$4.25
Output speed
—

Accepts

  • Text input
  • Image input
  • PDF input
  • Video input

Produces

  • Text output
Full model profileVendor docs ↗
VS

NVIDIA

Llama Nemotron Embed VL 1B v2

Embedding model for semantic search, retrieval, clustering, and ranking pipelines

Context
32.8K
Max output
2.0K
Input / 1M
$0.00
Output / 1M
$0.00
Output speed
—

Accepts

  • Text input
  • Image input

Produces

  • Text output
Full model profileVendor docs ↗

At a glance

Decision snapshot

This is a directional summary of the values recorded in this directory—not a universal quality verdict. Test the finalists on your own prompts before committing.

Where Muse Spark 1.1 leads in recorded data

  • Context capacity: 1M
  • Maximum output: 32K

Where Llama Nemotron Embed VL 1B v2 leads in recorded data

  • Input price: $0.00
  • Output price: $0.00

Context capacity

Higher listed limit

Muse Spark 1.1:1M

Llama Nemotron Embed VL 1B v2:32.8K

Muse Spark 1.1

Maximum output

Higher listed limit

Muse Spark 1.1:32K

Llama Nemotron Embed VL 1B v2:2.0K

Muse Spark 1.1

Input price

Lower recorded price

Muse Spark 1.1:$1.25

Llama Nemotron Embed VL 1B v2:$0.00

Llama Nemotron Embed VL 1B v2

Output price

Lower recorded price

Muse Spark 1.1:$4.25

Llama Nemotron Embed VL 1B v2:$0.00

Llama Nemotron Embed VL 1B v2

Output speed

Higher recorded throughput

Muse Spark 1.1:—

Llama Nemotron Embed VL 1B v2:—

No complete comparison

Time to first token

Lower recorded latency

Muse Spark 1.1:—

Llama Nemotron Embed VL 1B v2:—

No complete comparison

Intelligence Index

Higher directory index

Muse Spark 1.1:—

Llama Nemotron Embed VL 1B v2:—

No complete comparison

Evidence

Verified shared benchmarks

A score appears only when both records include a verified source. It is not treated as a universal ranking.

These two models haven't been measured on the same benchmark yet, so there's nothing meaningful to chart. Comparing scores from different tests would tell you more about the tests than the models — the specs and pricing above are the better guide here.

Specifications

Pricing, limits and capabilities

SpecificationMuse Spark 1.1Llama Nemotron Embed VL 1B v2
ProviderMetaNVIDIA
Model familymusenemotron
Accepted inputstext, image, pdf, videotext, image
Produced outputstexttext
Context window1MRecorded lead32.8K
Max output32KRecorded lead2.0K
Intelligence Index——
Input price / 1M$1.25$0.00Recorded lead
Output price / 1M$4.25$0.00Recorded lead
Output speed——
Time to first token——
Reasoning modeDocumentedNot documented
Tool callingDocumentedNot documented
Image inputDocumentedDocumented
Audio inputNot documentedNot documented
Structured outputDocumentedNot documented
LicenseProprietaryOpen weights
ReleasedApr 8, 2026Feb 10, 2026

Workload guidance

Which model should you choose?

Muse Spark 1.1 currently leads on larger context, larger maximum output. Llama Nemotron Embed VL 1B v2 currently leads on lower recorded input price, lower recorded output price. Choose according to the constraints that matter to your workload, then run the same representative prompts against both models before making a production commitment. Neither the larger number nor the lower price is automatically the better choice: prompt quality, tool reliability, modality support, rate limits, data policy, and provider implementation can change the result. Use this page to build a shortlist, confirm current terms in the linked sources, and validate quality, latency, and cost with your own traffic.

Provenance

Sources and data limits

Prices and hosted performance can change. SyncDev records source references and clearly separates documented specifications from measured values; confirm commercial terms with the provider before deployment.

Questions people ask

It compares recorded context and output limits, token prices, output speed, time to first token, Intelligence Index, modality support, tool and structured-output capabilities, release information, and only source-linked benchmarks verified on both records. Missing information is labeled rather than inferred.

Compare a different pair

Choose any two approved model records and review them with the same evidence rules.

Open model comparison