Is GPT-5.6 Sol available with Zero Data Retention on Elosia?

GPT-5.6 Sol runs on Elosia's standard backend; ZDR is available on many other models in the catalog.

How much does GPT-5.6 Sol cost on Elosia?

GPT-5.6 Sol is billed by usage at $4 per 1M input tokens and $20 per 1M output tokens, within your Elosia plan.

What is GPT-5.6 Sol's context window?

GPT-5.6 Sol supports a context window of up to 1,050,000 tokens.

Is GPT-5.6 Sol hosted in the EU?

Yes, GPT-5.6 Sol is available on Elosia's EU-hosted (EdenAI) backend for enterprise organizations.

When should you choose GPT-5.6 Sol?

GPT-5.6 Sol is a balanced model: a strong quality/cost trade-off for everyday production work.

GPT-5.6 Sol

GPTBalanced

ThinkingTool UseVisionStructured Output

About this model

GPT-5.6 Sol is the flagship model in OpenAI's GPT-5.6 series. It is suited for complex reasoning, coding, and agentic workflows, and is particularly strong at command-line and multi-step coding tasks...

Performance Tier

Balanced

GPT-5.6 Sol is a balanced model from GPT : strong performance at a reasonable price.

Strong cost-performance ratio. Reliable for most professional use cases without premium pricing.

Pricing

This model is included in Elosia plans

High

High pricing. Reserve this level for tasks that demand maximum quality.

Type	per 1M tokens
Input (prompt)	$4.00
Output (completion)	$20.00
Cache read	$0.400
Cache write	$5.00

Capabilities

Context Length1.1M

Max Output Tokens128K

TokenizerGPT

Inputfile, image, text

Outputtext

Release DateJuly 9, 2026

Benchmarks

General Intelligence

MMLU

Not reported

Mathematics

MATH-500

Not reported

Programming

HumanEval

Not reported

Reasoning

ARC-AGI-2

85.4%

Agentic

SWE-bench Pro

64.6%

Terminal-Bench 2.1

88.8%

Where does GPT-5.6 Sol stand?

Compare its performance index against every other model.

View the performance leaderboard

Recommended Use Cases

CodingAnalysisResearchMathematicsData Extraction

Strengths

Top tier of the GPT-5.6 family, particularly strong at command-line and multi-step coding tasks
Abstract reasoning that scales markedly with reasoning effort, from low up to max
Optional ultra mode coordinates several agents in parallel for harder tasks
1M-token context window with programmatic tool calling and multimodal (text, image, file) input

Limitations

Resolution of issues inside existing codebases (SWE-bench Pro) is weaker than its command-line results
OpenAI published no MMLU, GPQA or math benchmarks for GPT-5.6, so direct comparison with older models is harder
High and maximum reasoning efforts, and ultra mode, consume many more tokens per query and add latency