What is GPT 5.6 Sol?
GPT 5.6 Sol is OpenAI’s proprietary model for high-end reasoning, tool-using agents, coding and very large document or repository workloads. On The AI Leaderboard it currently ranks number 2 with an Overall score of 81.0. That score is a comparative benchmark result, not a percentage chance of correctness and not a promise that the model is best for every workload.
QUICK VERDICT: Choose GPT 5.6 Sol when you need high-end reasoning, tool-using agents, coding and very large document or repository workloads. The main trade-off is that Requests above 272,000 input tokens receive higher token multipliers, so the headline price can understate long-context cost.
Field | Current guide value |
|---|---|
Provider | OpenAI |
Leaderboard rank | #2 |
Overall score | 81.0 |
License | Proprietary |
Best suited to | high-end reasoning, tool-using agents, coding and very large document or repository workloads |
Access | OpenAI API through the gpt-5.6 alias and versioned snapshots |
Context or scale | 1,050,000-token context window and up to 128,000 output tokens |
Published pricing note | $4 per million input tokens and $20 per million output tokens during the documented promotional period |
GPT 5.6 Sol capabilities and architecture
OpenAI positions Sol as the frontier tier in the GPT-5.6 family. The unsuffixed gpt-5.6 alias routes to it.
Reasoning effort supports none, low, medium, high, xhigh and max, making evaluation settings essential when comparing results.
It accepts text and image input, produces text, supports structured outputs and exposes tools through the Responses API.
The practical lesson is to evaluate the complete system: model, reasoning level, tool permissions, context strategy, agent harness and verification loop. A strong base model can underperform with weak tools or poor task decomposition. Conversely, an effective harness can make a slightly lower-ranked model the better product choice.
How GPT 5.6 Sol performs on the leaderboard
The 81.0 Overall score comes from the current LiveBench release used by The AI Leaderboard. LiveBench first averages subtasks inside each of seven categories, then gives those category averages equal weight. This prevents one unusually strong category from dominating the final number.
The public row uses the canonical family name GPT 5.6 Sol. The evaluated LiveBench configuration may include a reasoning or effort qualifier. Those qualifiers are preserved in our source validation even though they are removed from the public model label. Anyone attempting to reproduce the score must match the exact dated release, variant, effort setting and harness.
Interpretation | What it means |
|---|---|
Overall score | A one-decimal composite across LiveBench categories, currently 81.0 |
Rank | Position 2 among the eight models in the current site table |
Not measured directly | Your latency, regional availability, private data, integration quality or total production cost |
Reproduction requirement | Match the dated model variant, reasoning effort, harness and benchmark release |
Best use cases for GPT 5.6 Sol
- Primary fit: high-end reasoning, tool-using agents, coding and very large document or repository workloads.
- Use it for bounded workflows with explicit success criteria, tool permissions and a review step.
- Run a small representative evaluation before migrating a production workload or committing to a large inference budget.
- Keep the exact model snapshot fixed during testing so silent alias changes do not invalidate the comparison.
Access, pricing and deployment
OpenAI API through the gpt-5.6 alias and versioned snapshots. The current pricing reference is: $4 per million input tokens and $20 per million output tokens during the documented promotional period. Pricing changes quickly and often depends on caching, batch processing, long-context multipliers, regions and tool calls. Verify the official page before budgeting.
1,050,000-token context window and up to 128,000 output tokens. Context-window size is a capacity ceiling, not evidence that every token will receive equal attention. For long documents or repositories, measure retrieval accuracy, instruction retention, latency and cost at the actual lengths you expect to use.
Limitations and safety
Requests above 272,000 input tokens receive higher token multipliers, so the headline price can understate long-context cost.
Do not rely on a leaderboard score for high-stakes medical, legal, financial, cybersecurity or safety decisions. Require domain review, log model and prompt versions, test failure cases, minimize tool permissions and keep a rollback path. Open weights provide deployment control, but they also move security, patching and abuse prevention responsibilities to the operator.
How to evaluate GPT 5.6 Sol yourself
Test | What to record |
|---|---|
Task completion | Binary success plus a written quality rubric |
Reliability | Pass rate over repeated runs, not one best attempt |
Tool use | Wrong calls, retries, permission requests and recovery |
Quality | Factuality, instruction adherence, citations and deliverable usefulness |
Efficiency | Wall time, input, output, cached tokens and tool fees |
Safety | Unsafe actions, sensitive-data handling and approval-boundary failures |
Use 20 to 50 tasks drawn from your real workload, freeze them before testing, and run every candidate with equivalent tools and budgets. Keep a hidden holdout set so prompts are not gradually optimized for the public examples. Report medians and failure rates, not only impressive demonstrations.
Frequently asked questions
Is GPT 5.6 Sol the best AI model?
GPT 5.6 Sol is currently number 2 on this site’s LiveBench-based table. That makes it a strong current candidate, but “best” depends on the task, cost, latency, modalities, deployment constraints and the agent harness.
Is GPT 5.6 Sol open source?
No. GPT 5.6 Sol is proprietary and accessed through OpenAI or approved partners.
What is GPT 5.6 Sol best used for?
Its strongest fit is high-end reasoning, tool-using agents, coding and very large document or repository workloads. Start with a supervised pilot and compare it with at least one neighboring model using the same task set.
Can I compare the score directly with vendor benchmarks?
Not safely. Vendor benchmarks may use different prompts, reasoning budgets, tools, sampling, dates and scoring rules. Compare only results produced under the same evaluation protocol.
Official source and update policy
The primary product reference for this guide is OpenAI’s official GPT 5.6 Sol documentation.
We update this guide when the official model specification, access, pricing, benchmark release or canonical leaderboard position changes. Checked August 26, 2026. No vendor payment or affiliate relationship determined the ranking.