Context Window

Compare

Gpt 5 4 Nano 2026 03 17 vs GPT-5.6 Sol

This page is context-first: how much text each model can take in one request. Full specs adds capabilities and limits; the pricing matrix below is only about $/million tokens from hosts that list both models.

Openai

Model

Gpt 5 4 Nano 2026 03 17

Image inputTool calling

Context window

1.1M

1,050,000 tokens · ~788K words

Model page
Openai

Model

GPT-5.6 Sol

Image inputTool calling

Context window

1.1M

1,050,000 tokens · ~788K words

Model page

Context window · side by side

Bar length is relative to the larger of the two windows (100% = max of this pair). This is not pricing.

Gpt 5 4 Nano 2026 03 171.1M
GPT-5.6 Sol1.1M

Same context window size for both models.

Token calculator →Memory savings →

Gpt 5 4 Nano 2026 03 17 and GPT-5.6 Sol have identical context windows (1050K tokens). Gpt 5 4 Nano 2026 03 17 is 96% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • RAG / high-volume retrieval

    Use Gpt 5 4 Nano 2026 03 17. Input tokens are 96% cheaper — critical when sending large retrieved contexts.

Full specs

Context, output, capabilities, and dates. Green highlights the favorable value where we compute a winner.

SpecGpt 5 4 Nano 2026 03 17GPT-5.6 Sol
Context window1,050,000 tokens (1050K)1,050,000 tokens (1050K)
Max output tokens128,000 tokens (128K)128,000 tokens (128K)
Speed tierFastBalanced
VisionYesYes
Function callingYesYes
Extended thinkingYesYes
Prompt cachingYesYes
Batch APINoNo
Release dateN/AJul 2026

Pricing matrix

Dollar rates only: hosts that list both models, per 1M tokens. For how much text fits, use the context section above — not this table.

ProviderGpt 5 4 Nano 2026 03 17 inGpt 5 4 Nano 2026 03 17 outGPT-5.6 Sol inGPT-5.6 Sol out
Azure$0.200/M$1.25/M——
Openai$0.200/M$1.25/M$5.00/M$30.00/M

Frequently asked questions

GPT-5.6 Sol has a larger context window: 1050K tokens vs 1050K. For long documents, large codebases, or extended agent sessions, the larger context window reduces the need to chunk inputs or summarize history.

Powered by Mem0

Use a smaller model.
Get better results.

Mem0 gives your AI long-term memory so you stop re-sending context on every call. That means you can use a smaller, faster, cheaper model — and still get better answers.

Calculate your savingsLearn about Mem0

Example: a multi-turn chat session

Without Mem0~128K tokens sent
Full history
Repeated info
Old context
With Mem0~20K tokens sent
Key memories
Current turn

80% less to send — works with any model