🚀 GPT-5.6 Sol in Action: How Well Can It Handle Complex Work? Full Results Inside

Introduction

Fable 5 had only just launched when OpenAI introduced GPT-5.6 Sol. A lot of attention was still on Anthropic’s new model, so OpenAI’s timing quickly raised one obvious question:

Can GPT-5.6 Sol actually perform better than Fable 5?

Early demos on X are getting plenty of attention, although the reactions are still mixed because some developers continue to prefer Fable 5.

In this article, I’ll show you what GPT-5.6 Sol can do through popular coding demos on X and direct tests inside ChatGPT Chat.

You’ll see how GPT-5.6 Sol handles real tasks, where it performs best, and whether it’s worth using instead of Fable 5.

I. What Is GPT-5.6 Sol?

GPT-5.6 is OpenAI’s new model family, and it comes in 3 tiers:

Sol is the flagship. It’s built for complex, multi-step work: coding, research, science, computer use, and professional workflows that need to stay on track across many steps.

How Do You Actually Use Sol in ChatGPT?

In standard ChatGPT conversations, Sol powers these reasoning settings (confirmed by OpenAI’s Help Center):

• Medium → Standard reasoning with Sol

• High → Extended reasoning with Sol

• Extra High → Highest reasoning effort in standard chat

• Pro → Sol Pro, for difficult tasks and longer workflows (Pro and Enterprise plans only)

There are also two more advanced modes, Max and Ultra, but these are only available inside ChatGPT Work and Codex, not in regular chat.

Ultra is the most interesting one: it spins up 4 parallel subagents that each tackle part of the task simultaneously. That’s how Sol gets some of its highest benchmark scores but it also multiplies your token cost significantly.

The takeaway: Terra and Luna don’t even show up in standard ChatGPT chat, those are API and Codex-only. Sol is the only GPT-5.6 model most ChatGPT users will actually touch.

II. GPT-5.6 Sol Benchmark Results Are Mixed

I need to tell you that almost all the benchmark numbers you’ll see right now come from OpenAI’s own launch materials. Independent testing was gated during the preview period.

→ That doesn’t make the numbers wrong, but it does mean you should treat them as directional rather than definitive.

1. Where Sol Is Clearly Ahead

OpenAI’s benchmark charts show that GPT-5.6 Sol performs very well in coding agents, terminal work, and web research.

a. Long professional tasks (Agents’ Last Exam)

This is OpenAI’s biggest headline number. Agents’ Last Exam tests long-running professional workflows across 55 fields.

Sol scored 53.6 on Agents’ Last Exam, eclipsing Fable 5 by 13.1 points. Even at medium reasoning, Sol beats Fable 5 by 11.4 points at roughly a quarter of the estimated cost.

One small note: OpenAI’s own eval table lists Sol at 52.7%, while the headline announcement says 53.6%. The 13.1-point gap over Fable 5 at 40.5% checks out either way, multiple independent sources confirm it.

b. Coding agent performance (AA Coding Agent Index v1.1)

On the Artificial Analysis Coding Agent Index, Sol at max reasoning scored 80, 2.8 points above Fable 5, while using less than half the output tokens, taking less than half the time, and costing about one-third less.

c. Terminal work (Terminal-Bench 2.1)

Here’s where it gets a little complicated, so bear with me for a second. Terminal-Bench 2.1 tests command-line workflows that require planning, retries, and tool coordination.

Sol scored 88.8% on Terminal-Bench 2.1 in standard mode, reaching 91.9% in Ultra mode, with Fable 5 at 83.4% to 84.3% depending on the source.

Anthropic’s own launch materials report 86–88%, while OpenAI’s cross-model comparison table lists 83.1%. Independent evaluators land somewhere in between.

→ Sol is clearly ahead on this benchmark, but the exact gap depends on which Fable 5 number you trust.

d. Web research (BrowseComp)

BrowseComp includes difficult questions that require a model to search the web and connect information from several sources. A lot of pieces report Sol’s BrowseComp score as 90.4% and Ultra as 92.2%. That’s actually backwards.

Sol reached 92.2% on BrowseComp, while Ultra reached 91.9% on Terminal-Bench 2.1. Those two numbers got swapped in several summaries that circulated after launch. The confirmed BrowseComp score for Sol is 92.2%.

2. Where Fable 5 Is Still Clearly Ahead

a. SWE-Bench Pro (real repository coding)

This is the one that matters most for day-to-day software engineering, and Fable 5 wins by a wide margin.

On SWE-Bench Pro, Sol scored 64.6% while Fable 5 scored 80%, roughly a 15-point deficit on a widely watched coding benchmark.

It’s worth noting that OpenAI published a separate article suggesting about 30% of SWE-Bench Pro tasks may be methodologically flawed.

Maybe that’s true. But when a lab leads a benchmark, they cite it, and when they lose, they question the methodology. Fable 5 leads SWE-Bench Pro, and that gap is real enough to matter for anyone doing active codebase work.

b. General intelligence (AA Intelligence Index)

Sol with max reasoning scores 59 on the Artificial Analysis Intelligence Index, just one point behind Fable 5 at 60, while completing tasks 61% faster at roughly half the estimated cost.

The gap is tiny. But Sol doesn’t lead this one.

The Quick Summary

→ Overall: Sol wins agentic, terminal, and research tasks. Fable 5 wins repository coding and broad intelligence. Neither model swept the board.

III. What GPT-5.6 Sol Can Actually Build: Real Demos

I want to be clear: these are community examples, not our controlled tests. They’re useful for understanding Sol’s range, but don’t treat any of them as final proof of anything.

Demo 1: A Full Replit-Style App from One Prompt

Riley Brown shared a Codex run where GPT-5.6 Sol created a full Replit-style app from one prompt.

The project included a database and isolated sandboxes. It also had an agent that could build and edit complete web apps.

Twitter tweet

This is more complex than generating a polished landing page. GPT-5.6 Sol had to connect the main product parts and keep the same goal across a long build.

Demo 2: Sol Worked on a Spaceship for 87 Minutes Straight

Another test used the same spaceship task with GPT-5.6 Sol and GPT-5.5. Sol continued working for 87 minutes, while GPT-5.5 stopped after around 35 minutes.

Twitter tweet

The longer run suggests better persistence on large projects. That matters when coding requires several checks and fixes before the result becomes usable.

Runtime alone still can’t tell us whether the final code was cleaner or more reliable.

Demo 3: A Minecraft Clone in 70 Minutes

Kirill shared a head-to-head task that asked both models to build a full Minecraft clone from scratch in one run.

GPT-5.6 Sol finished in 70 minutes through Devin, while Fable 5 took 90 minutes in Claude Code.

Twitter tweet

The result looks strong, although the models ran in different coding environments.

So the time comparison is interesting as a community data point, but it’s not a clean speed test between the models themselves.

Next, I’ll test GPT-5.6 Sol with smaller tasks inside ChatGPT Chat, so you can see how it handles each request and check the results more clearly.

IV. Test 1: Research with Conflicting Evidence

Sol scored 92.2% on BrowseComp, which measures agentic web browsing. So this test checks whether it can find reliable sources and build a careful conclusion when the evidence doesn’t all point the same direction.

The topic is whether AI coding assistants help teams ship software faster without increasing production defects.

A simple web summary won’t be enough here. Sol needs to check the original evidence and explain why different sources reach different conclusions.

You can paste this prompt into ChatGPT:

Research whether AI coding assistants reduce software delivery time
without increasing production defects.

Use evidence published from January 2025 onward.

Requirements:
- Prioritize original studies, engineering reports, and public datasets.
- Support every numerical claim with a direct citation.
- Separate measured findings from company claims and personal opinions.
- Explain where reliable sources disagree.
- Show what the current evidence still can't prove.
- Don't use a source unless you can verify that it supports the claim.

Write the answer in this format:
1. Main conclusion
2. Strongest evidence
3. Conflicting evidence
4. Limits of the research
5. Practical advice for a software team

Keep the final report under 1,200 words.

What You Should Check in the Answer

Open a few citations and verify each source actually supports the claim next to it. Then check whether Sol clearly separates measured results from assumptions, and whether it explains why reliable sources disagree.

The conclusion should stay narrow. Faster coding doesn’t automatically mean faster delivery or fewer production bugs, so Sol should be able to tell you what the evidence proves and what it still can’t.

V. Test 2: Complex File Analysis

After the research task, here’s another one worth trying: upload a report, spreadsheet, or PDF with multiple tables and sections, and see how Sol handles it.

The goal is to find out whether Sol can spot real errors, notice unusual patterns, and recognize when the available data isn’t enough to support a conclusion.

Upload the file you want GPT-5.6 Sol to analyze, then paste the prompt below:

Analyze the file I uploaded and check for any issues that could affect
the accuracy of its conclusions.

Requirements:
- Find data errors, unusual patterns, and sections that conflict with each other.
- Check whether the tables, charts, and written explanations use the same figures.
- Identify missing data that prevents a conclusion from being confirmed.
- Cite the exact page, sheet, table, row, column, or paragraph for each finding.
- Don't guess any missing values.
- Don't treat correlation as causation unless the file provides enough evidence.
- Clearly label anything that is only a possibility rather than a confirmed finding.

Use this structure:
1. Errors that need fixing
2. Unusual patterns
3. Conclusions without enough evidence
4. Missing data
5. Overall assessment of the file's reliability

What You Should Check in the Answer

Open each location cited by GPT-5.6 Sol and confirm that the evidence appears there. For spreadsheets, pay close attention to formulas, totals, and rows that may have been skipped.

The answer should also separate real errors from details that only look unusual. When the file doesn’t contain enough evidence, GPT-5.6 Sol should say that clearly instead of filling the gap with a reasonable-sounding assumption.

VI. Test 3: Rebuild a Chart Into a Cleaner Slide

This example starts with a chart that already has a title, two data groups, and yearly trends. However, the bars don’t show exact values, and the layout still looks more like a draft than a finished presentation slide.

The original chart without value labels

In this example, Sol kept the original structure but added a value above each bar, moved market labels so they don’t cover the data, added branding and a footer, and cleaned up the spacing.

The version rebuilt by GPT-5.6 Sol

The result is easier to read, you can compare each year without estimating bar heights yourself.

One thing to always check: if the original image didn’t include exact values, Sol most likely estimated them from the height of each bar. Verify every number before using the slide in a real presentation.

VII. Is GPT-5.6 Sol Better Than Fable 5?

GPT-5.6 Sol and Fable 5 are strong in different kinds of work.

Here’s the honest comparison. Neither model is better at everything.

Use Sol when:

• You need web research pulling from multiple sources

• The task involves tool use across multiple steps

• You have clear success criteria you can check along the way

• Cost efficiency on agentic tasks matters

Use Fable 5 when:

• You’re doing active codebase work or fixing real repository issues

• The task requires open-ended planning where the model needs to decide the right direction before it starts

• Judgment matters more than execution speed

• You need peak reasoning quality and aren’t sensitive to the 2x price difference

You may see different results depending on the platform, prompt, and type of task. You should choose GPT-5.6 Sol when you can check the work step by step.

Fable 5 may be the better choice when you need the model to decide the right direction before it starts. Run your own workload tests before making any real decisions.

Conclusion

GPT-5.6 Sol performs well in research, file analysis, terminal work, and long tasks with clear goals. Fable 5 may still be a better choice when you need deeper planning or stronger judgment across a complex codebase.

You shouldn’t use one model for every task.

You’ll get better results when you choose GPT-5.6 Sol for work that you can check step by step, while Fable 5 fits tasks where the model needs to decide the right direction before it starts.

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