AI model comparison

Command A Plus vs GPT-5.6 Luna

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

Command A Plus and GPT-5.6 Luna both come up when teams are choosing a model for production work, and the honest answer usually depends on your workload rather than a leaderboard. This page puts their published specifications side by side — context window, token pricing, supported inputs and outputs — so you can see where they actually differ.

Side-by-sideReviewed by our team

Cohere

Command A Plus

Cohere's stronger command model for multilingual agents and enterprise workflows

Context
128K
Max output
64K
Input / 1M
$2.50
Output / 1M
$10.00
Output speed

Accepts

  • Text input
  • Image input

Produces

  • Text output
VS

OpenAI

GPT-5.6 Luna

Cost-efficient GPT-5.6 model for fast, high-volume workloads

Context
1.1M
Max output
128K
Input / 1M
$0.20
Output / 1M
$1.20
Output speed

Accepts

  • Text input
  • Image input
  • PDF input

Produces

  • Text output

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 Command A Plus leads in recorded data

No complete recorded factor currently favors this model. That does not establish lower real-world quality.

Where GPT-5.6 Luna leads in recorded data

  • Context capacity: 1.1M
  • Maximum output: 128K
  • Input price: $0.20
  • Output price: $1.20

Context capacity

Higher listed limit

Command A Plus:128K

GPT-5.6 Luna:1.1M

GPT-5.6 Luna

Maximum output

Higher listed limit

Command A Plus:64K

GPT-5.6 Luna:128K

GPT-5.6 Luna

Input price

Lower recorded price

Command A Plus:$2.50

GPT-5.6 Luna:$0.20

GPT-5.6 Luna

Output price

Lower recorded price

Command A Plus:$10.00

GPT-5.6 Luna:$1.20

GPT-5.6 Luna

Output speed

Higher recorded throughput

Command A Plus:

GPT-5.6 Luna:

No complete comparison

Time to first token

Lower recorded latency

Command A Plus:

GPT-5.6 Luna:

No complete comparison

Intelligence Index

Higher directory index

Command A Plus:

GPT-5.6 Luna:

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

SpecificationCommand A PlusGPT-5.6 Luna
ProviderCohereOpenAI
Model familycommand-agpt-luna
Accepted inputstext, imagetext, image, pdf
Produced outputstexttext
Context window128K1.1MRecorded lead
Max output64K128KRecorded lead
Intelligence Index
Input price / 1M$2.50$0.20Recorded lead
Output price / 1M$10.00$1.20Recorded lead
Output speed
Time to first token
Reasoning modeDocumentedDocumented
Tool callingDocumentedDocumented
Image inputDocumentedDocumented
Audio inputNot documentedNot documented
Structured outputDocumentedDocumented
LicenseOpen weightsProprietary
ReleasedMay 20, 2026Jul 9, 2026

Workload guidance

Which model should you choose?

On the published numbers, GPT-5.6 Luna accepts the larger context window and GPT-5.6 Luna is cheaper per input token. Which matters more depends on whether your bottleneck is document size or spend — the table above has the exact figures.

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.

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