AI model comparison

GPT-5.6 Terra vs Step 3.7 Flash

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

GPT-5.6 Terra and Step 3.7 Flash 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

OpenAI

GPT-5.6 Terra

OpenAI's mid-tier GPT-5.6 reasoning model with a 1.05M token context window, function calling, and vision, priced between GPT-5.5 and GPT-5.5 Pro.

Context
1.1M
Max output
128K
Input / 1M
$2.00
Output / 1M
$12.00
Output speed

Accepts

  • Text input
  • Image input
  • PDF input

Produces

  • Text output
VS

StepFun

Step 3.7 Flash

Newer StepFun flash model for faster agents, coding, and multimodal prompts

Context
256K
Max output
256K
Input / 1M
$0.18
Output / 1M
$1.11
Output speed

Accepts

  • Text input
  • Image input
  • Video 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 GPT-5.6 Terra leads in recorded data

  • Context capacity: 1.1M

Where Step 3.7 Flash leads in recorded data

  • Maximum output: 256K
  • Input price: $0.18
  • Output price: $1.11

Context capacity

Higher listed limit

GPT-5.6 Terra:1.1M

Step 3.7 Flash:256K

GPT-5.6 Terra

Maximum output

Higher listed limit

GPT-5.6 Terra:128K

Step 3.7 Flash:256K

Step 3.7 Flash

Input price

Lower recorded price

GPT-5.6 Terra:$2.00

Step 3.7 Flash:$0.18

Step 3.7 Flash

Output price

Lower recorded price

GPT-5.6 Terra:$12.00

Step 3.7 Flash:$1.11

Step 3.7 Flash

Output speed

Higher recorded throughput

GPT-5.6 Terra:

Step 3.7 Flash:

No complete comparison

Time to first token

Lower recorded latency

GPT-5.6 Terra:

Step 3.7 Flash:

No complete comparison

Intelligence Index

Higher directory index

GPT-5.6 Terra:

Step 3.7 Flash:

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

SpecificationGPT-5.6 TerraStep 3.7 Flash
ProviderOpenAIStepFun
Model familygpt-terra
Accepted inputstext, image, pdftext, image, video
Produced outputstexttext
Context window1.1MRecorded lead256K
Max output128K256KRecorded lead
Intelligence Index
Input price / 1M$2.00$0.18Recorded lead
Output price / 1M$12.00$1.11Recorded lead
Output speed
Time to first token
Reasoning modeDocumentedDocumented
Tool callingDocumentedDocumented
Image inputDocumentedDocumented
Audio inputNot documentedNot documented
Structured outputDocumentedNot documented
LicenseProprietaryOpen weights
ReleasedJul 9, 2026May 29, 2026

Workload guidance

Which model should you choose?

On the published numbers, GPT-5.6 Terra accepts the larger context window and Step 3.7 Flash 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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