OPENAI MODEL GUIDE
GPT-5.6 Sol API
OpenAI's flagship GPT-5.6 model for complex professional work, advanced coding, reasoning, and long-running agents.
High-value coding, repository-scale changes, deep research, and agents where failure is expensive.
Classification, extraction, bulk rewriting, or tasks Terra and Luna already pass reliably.
Choosing and integrating
GPT-5.6 Sol API pricing breakdown
GPT-5.6 Sol (gpt-5.6-sol) is billed by token usage. Prices below are USD per 1M tokens. LLMFly AI rates already include the 0.3x group multiplier; do not apply it again. Provider reference prices are separate. These rates come from the site's Model Plaza snapshot; confirm live catalog and account rates before calling.
Example: a request with 10,000 uncached input tokens and 1,000 output tokens costs approximately $0.024 in LLMFly AI token charges. This excludes cache writes, tools, retries, and other additional fees.
- Platform and provider prices use their own input-token tiers. Do not apply provider long-context multipliers to platform prices.
- Not listed does not mean free. Account for cache reads, cache writes, and uncached input in their respective billing categories.
- Token cost = tokens in each billing category × its price per million ÷ 1,000,000. Add tools and other charges separately.
| Price source | Input tokens per request | Input | Output | Cache write | Cache read | 1h cache write |
|---|---|---|---|---|---|---|
| LLMFly AI | 0 < tokens ≤ 272,000 | $1.5 | $9 | $1.875 | $0.15 | $1.875 |
| LLMFly AI | 272,000 < tokens | $3 | $13.5 | $3.75 | $0.3 | $3.75 |
| Provider reference | 0 < tokens ≤ 272,000 | $5 | $30 | $6.25 | $0.5 | $6.25 |
| Provider reference | 272,000 < tokens | $10 | $45 | $12.5 | $1 | $12.5 |
Why choose GPT-5.6 Sol
Sol earns its place when a harder professional task needs a higher capability ceiling. Start with Terra as the production baseline, then escalate only failed or high-risk work to Sol.
- Repository-scale coding and cross-file refactors.
- Long-running agents that coordinate several tools.
- Research across many sources where a wrong conclusion is expensive.
- Professional workflows where first-pass quality matters more than the lowest token price.
GPT-5.6 Sol vs Terra vs Luna
Do not choose from the tier name alone. Run the same tasks, tools, and acceptance criteria; use Sol only when the quality gain pays for the premium.
| Decision | Sol | Terra | Luna |
|---|---|---|---|
| Default role | Quality ceiling | Production baseline | Cost-first tier |
| Start here for | Hard coding, complex agents | General coding and workflows | Extraction, classification, volume |
| Escalate when | — | Terra misses the quality bar | Retries erase Luna savings |
| Vendor input / output | $4 / $20 | $2 / $12 | $0.20 / $1.20 |
When the 1.05M context window is worth it
- Deduplicate retrieved passages, tool logs, and repeated files.
- Separate stable prefixes from changing content and measure real cache hits.
- Compact or summarize long agent history instead of resending it forever.
- Evaluate accepted-result cost and quality, not only whether one huge request completes.
Configure reasoning and tool loops
Sol exposes reasoning effort from none through max. Establish a medium baseline, then raise effort only on hard samples; record reasoning level, tool turns, and output length together.
- Streaming, function calling, and structured outputs.
- Responses API tools including web search, file search, code interpreter, hosted shell, computer use, MCP, and tool search.
- A 1.05M-token context window for large repositories and document collections.
- Six reasoning-effort levels, with medium as the documented default.
Send the first Sol request through LLMFly AI
The example uses the provider's model ID. Before launch, copy the model ID available to your API key in Model Plaza and validate a non-streaming request, error handling, and the usage record.
curl https://app.llmfly.ai/v1/chat/completions \
-H "Authorization: Bearer $LLMFLY_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "gpt-5.6-sol",
"messages": [{"role": "user", "content": "Summarize the tradeoffs in three bullets."}],
"stream": false
}'Evaluate Sol with an escalation gate
- Run Terra on 20–50 representative tasks and classify the failures.
- Send only valuable failed samples to Sol and measure incremental passes.
- Track cost per accepted result, time to first token, total latency, and tool retries.
- Define explicit automatic-escalation rules instead of sending all traffic to Sol.
Where Sol is the wrong fit
- It is usually excessive for simple classification, extraction, or bulk rewriting.
- Requests above 272K input tokens use higher long-context pricing for the full request.
- A large context limit does not remove the need to select and deduplicate context.
- Exact endpoints and tools still need verification on the selected LLMFly AI route.
GPT-5.6 Sol specification sources and usage notes
Specifications and provider list prices were checked against OpenAI GPT-5.6 Sol documentation: https://developers.openai.com/api/docs/models/gpt-5.6-sol. Confirm the LLMFly AI model ID, API key access, availability, and price in Model Plaza.
Frequently asked questions
What is the practical difference between GPT-5.6 Sol and Terra?
Sol is the quality ceiling for harder tasks; Terra is the stronger default production baseline. Test whether Sol adds enough accepted results to justify its premium.
Should I fill the 1.05M context window?
No. Curate, deduplicate, and compact context first, and account for the higher long-context tier above 272K input tokens.
Which reasoning effort should Sol use?
Start at medium, then test high, xhigh, or max on difficult samples while tracking latency, output tokens, and tool loops.
Which requests should not use Sol?
Stable classification, extraction, rewriting, and work already handled reliably by Terra or Luna usually do not need the flagship tier.
Is the LLMFly AI model ID always gpt-5.6-sol?
Not necessarily. The example uses the provider's ID; copy the model ID currently available to your API key in Model Plaza.
Test GPT-5.6 Sol with your own task
Create an API key, confirm the model ID, and start with a small representative request.