SSyncDev

AI model comparison

Qwen2.5-VL 72B Instruct 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.

This Qwen2.5-VL 72B Instruct vs GPT-5.6 Luna comparison is designed for teams choosing between two production model records, not for declaring a universal winner. Qwen2.5-VL 72B Instruct by Alibaba currently records 131.1K context, 8.2K maximum output, $2.80 input per 1M tokens, $8.40 output per 1M tokens. GPT-5.6 Luna by OpenAI records 1.1M context, 128K maximum output, $0.20 input per 1M tokens, $1.20 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

Alibaba

Qwen2.5-VL 72B Instruct

Qwen vision-language model for visual reasoning, documents, and agent tasks

Context
131.1K
Max output
8.2K
Input / 1M
$2.80
Output / 1M
$8.40
Output speed
—

Accepts

  • Text input
  • Image input

Produces

  • Text output
Full model profileVendor docs ↗
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
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 Qwen2.5-VL 72B Instruct 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

Qwen2.5-VL 72B Instruct:131.1K

GPT-5.6 Luna:1.1M

GPT-5.6 Luna

Maximum output

Higher listed limit

Qwen2.5-VL 72B Instruct:8.2K

GPT-5.6 Luna:128K

GPT-5.6 Luna

Input price

Lower recorded price

Qwen2.5-VL 72B Instruct:$2.80

GPT-5.6 Luna:$0.20

GPT-5.6 Luna

Output price

Lower recorded price

Qwen2.5-VL 72B Instruct:$8.40

GPT-5.6 Luna:$1.20

GPT-5.6 Luna

Output speed

Higher recorded throughput

Qwen2.5-VL 72B Instruct:—

GPT-5.6 Luna:—

No complete comparison

Time to first token

Lower recorded latency

Qwen2.5-VL 72B Instruct:—

GPT-5.6 Luna:—

No complete comparison

Intelligence Index

Higher directory index

Qwen2.5-VL 72B Instruct:—

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

SpecificationQwen2.5-VL 72B InstructGPT-5.6 Luna
ProviderAlibabaOpenAI
Model familyqwengpt-luna
Accepted inputstext, imagetext, image, pdf
Produced outputstexttext
Context window131.1K1.1MRecorded lead
Max output8.2K128KRecorded lead
Intelligence Index——
Input price / 1M$2.80$0.20Recorded lead
Output price / 1M$8.40$1.20Recorded lead
Output speed——
Time to first token——
Reasoning modeNot documentedDocumented
Tool callingDocumentedDocumented
Image inputDocumentedDocumented
Audio inputNot documentedNot documented
Structured outputNot documentedDocumented
LicenseOpen weightsProprietary
ReleasedSep 1, 2024Jul 9, 2026

Workload guidance

Which model should you choose?

Qwen2.5-VL 72B Instruct has no complete recorded factor that establishes a lead. GPT-5.6 Luna currently leads on larger context, larger maximum output, 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