🔥 I Tested GPT-6.1 Sol Inside Codex on 3 Hard Tasks. Can THIS Beat Claude Sonnet 5.5?

Introduction

GPT-6.1 Sol just launched with a pretty bold promise: performance close to GPT-6 Astra, but at a much lower price.

Sounds great, of course. New models usually do, right up until you actually make them work. So I wanted to see what GPT-6.1 Sol can really do.

In this article, I’ll focus on where it performs well, where it starts to struggle, and how it compares with Claude Sonnet 5.5 at the same API price.

I. GPT-6.1 Sol Numbers You Should Know

This is a model that just dropped at DevDay, and some of the numbers have a few gotchas buried in them.

OpenAI positions GPT-6.1 Sol as the mid-tier model in the GPT-6 family, sitting below GPT-6 Astra and above GPT-6 Luna. It’s an upgrade to GPT-6 Sol.

The tagline is “near-Astra intelligence for a fifth of the price,” and the benchmarks mostly back that up (more on that below). Right now it’s available in Codex and ChatGPT Work for Plus, Pro, Business, Enterprise, and Edu users, plus the API.

It isn’t in regular Chat yet.

There are 2 tiers depending on how big your prompt is, and the cutoff is surprisingly low.

Standard (under 272K input tokens):

• Input: $2/1M

• Cached input: $0.10/1M

• Output: $10/1M

Long-context (over 272K input tokens, and this applies to the whole request, not just the overflow):

• Input: $4/1M

• Cached input: $0.20/1M

• Output: $15/1M

That 272K threshold is the thing most people miss. If a single retrieval step pushes you over it, the entire request reprices.

For context, a 272K input limit covers roughly 200,000 words, so for most single-session Codex tasks you’ll stay under it, but large repository analyses can cross it fast.

II. 3 GPT-6.1 Sol Tests: What It Can (and Can’t) Do

I ran 11 tests total, but honestly these 3 are the ones that tell the real story: how it handles a massive coding task, a visual accuracy challenge, and a planning-under-pressure scenario.

Test 1: Building a Full Browser OS

I started heavy. This one asked GPT-6.1 Sol to build a complete browser-based operating system in a single HTML file, not a demo, not a mockup. A working desktop with apps, two playable games, and cross-app connections, all from one prompt.

This is my prompt:

Build a complete browser-based operating system that runs locally in Chrome.

The entire project must be contained in a single self-contained HTML file. Do not use external frameworks, build tools, or additional project files.

The OS should include:

1. A desktop environment
- A polished desktop UI with a taskbar or dock
- Draggable and resizable app windows
- An app launcher
- Full-screen support
- A clock and date display
- Procedurally generated wallpapers with subtle movement
- Wallpaper variations controlled by adjustable seeds

2. At least five working applications
Include:
- An email client
- A notes or text app
- A sound or music app
- A settings app
- One additional useful app of your choice

The apps should interact with each other where it makes sense. For example, game progress or events can appear inside the email client.

3. Two playable 3D games
Game 1:
Create a GTA-style city sandbox with vehicles, police chases, and a small open city environment.

Game 2:
Create a 3D flight or checkpoint game with responsive controls, progression, and sound effects.

Both games must be playable inside the browser OS rather than opening as separate pages.

4. One original special feature
Design one feature that makes the OS feel connected rather than like a collection of separate demos.

The feature should save or connect information across apps, games, or the desktop state.

Explain what the feature does and why you chose it.

5. Quality requirements
- Everything must work from the single HTML file
- Avoid placeholder buttons or fake interactions
- Make the interface visually consistent
- Add sound where it improves the experience
- Keep performance smooth enough to run in Chrome
- Test the main interactions before finishing

When you're done, run the project locally, check the major features, fix obvious errors, and give me a short summary of what you built.

GPT-6.1 Sol finished the whole thing in 33 minutes and 42 seconds. What I got was a working desktop, several functional apps, two playable games, and cross-app connections actually built in.

The detail I liked most is that activity from the Skyrunner game showed up inside the Journal app. That kind of cross-app connection is genuinely hard to get right from a single prompt. Sol pulled it off.

Where it fell short: visually, it didn’t match the polish I’ve seen from Claude Sonnet 5.5 on similar builds. The layout was functional and clean, but not particularly designed.

→ For this test, my impression is pretty clear: GPT-6.1 Sol works fast, handles a large prompt well, and gives you a result you can actually use.

Test 2: Recreating Jerry’s Apartment in 3D

Next I wanted to test something harder on the visual side: 3D scene-building where accuracy to a real reference matters.

Jerry Seinfeld’s apartment is specific enough that you can immediately tell whether the proportions, furniture placement, and small props are right, or just approximately right.

Recreate Jerry Seinfeld’s apartment as a detailed 3D scene.

Use the real apartment set as your visual reference and focus on matching the layout, furniture placement, colors, wall details, props, and recognizable objects as closely as possible.

The scene should include:
- The main living room
- Kitchen area
- Front door
- Bedroom area
- Jerry’s green Klein bike
- TV and wall decorations
- Desk and computer area
- Window view with surrounding buildings

Add useful viewing controls:
- Multiple camera presets
- A dollhouse view of the full apartment
- Lighting controls
- A render quality control
- Daylight and warm interior lighting options

Pay attention to small details and recognizable Seinfeld references, but keep the scene clean and easy to explore.

When you’re done, run the scene, check the main views, fix obvious issues, and show me the final result.

This one took close to an hour, the most ambitious of the 3 . GPT-6.1 Sol built a full 3D version of the apartment with multiple camera views, dollhouse mode, lighting controls, and a good number of recognizable details.

I could spot the green Klein bike, the kitchen setup, the front door area, the bedroom, the TV, and small objects around the apartment. The dollhouse view made it easy to check how the whole space connected.

The weak point was still the layout. It looked convincing at first, but once I checked the full apartment, some room positions and proportions didn’t fully match the real set.

So for this test, GPT-6.1 Sol did well with 3D scene building, controls, and small details, but exact replication was still the harder part.

For a build that took close to an hour, the result was solid, just not accurate enough to call it a true recreation.

Test 3: Robot Arm Control in a Simulation

For the third test I wanted something different, not just “can it generate output” but “can it plan, track state, and recover when something goes wrong?”

So I asked GPT-6.1 Sol to build a robot arm simulation that had to find a toy car, approach it, adjust if the car moved, and pick it up. Joint limits, collision behavior, camera feedback, and failure recovery all had to actually work.

Your task is to test robot-arm control and object grasping.

First, inspect the current Codex environment and determine whether you have access to:
- A real robot arm or gripper
- A camera feed
- Robot joint or Cartesian controls
- Current robot pose
- Any local robot-control framework, MCP tool, ROS setup, LeRobot setup, serial device, or existing control script

If real hardware access is available:
- Use the real robot arm and camera
- Locate the toy car
- Plan a safe approach
- Move in small controlled steps
- Re-check the camera after every important movement
- Adjust the plan whenever the car moves
- Grasp the car and lift it only when the grip is stable
- Stop immediately if the arm enters an unsafe position or the camera feedback is not reliable

If real hardware access is NOT available:
Do not stop the task.

Instead, build a local interactive robot-arm simulation in the browser that reproduces the same challenge as closely as possible.

The simulation should include:

1. Robot arm
- Multi-joint articulated arm
- Working gripper
- Joint limits
- Smooth controlled movement
- Visible reach area

2. Toy car
- Place a small car on a table
- Give it realistic collision behavior
- Allow it to slide, rotate, or move if the gripper pushes it incorrectly

3. Camera and visual feedback
- Main camera view
- Optional second camera angle
- Clear view of the car and gripper
- Current arm position and target position

4. Control logic
The agent should:
- Estimate the car position
- Plan an approach
- Move in small steps
- Re-check the target after each important movement
- Adjust if the car moves
- Align the gripper
- Close the gripper
- Verify the grasp
- Lift the car

5. Failure handling
If a grasp fails:
- Detect why it failed
- Re-check the car position
- Change the approach
- Do not repeat the exact same movement blindly

6. Safety behavior
Stop or re-plan if:
- The arm reaches a joint limit
- The gripper loses alignment
- The car moves unexpectedly
- A collision is likely
- Progress stops

7. Success condition
The task is complete only when the gripper securely holds the toy car and lifts it from the table.

8. Browser result
- Keep the simulation local inside Codex
- Run it in the browser
- Make the robot arm, car, table, camera views, controls, and current status visible
- Show the grasp attempt in real time
- Include a reset button so I can run the test again

Important constraints:
- Keep everything local inside Codex
- Do not commit anything
- Do not push anything
- Do not publish or deploy anything
- Do not connect to GitHub or any external repository
- Do not create a pull request
- Do not upload the project anywhere
- Do not create any external share link

When you're done:
- Run the final result locally
- Test the full grasp flow
- Fix obvious errors
- Open the working result in the browser
- Show me the final web version
- Briefly tell me whether you used real hardware or the browser simulation
- List any remaining limitations

GPT-6.1 Sol built a browser-based simulation with the full planning loop running visibly: position checks, collision risk monitoring, grip status, and re-planning during failed grasp attempts.

The important part wasn’t the graphics. It was whether Sol could handle a feedback loop, detect that something failed, figure out why, and try differently instead of repeating the same move.

→ It did. When the simulated gripper missed, it adjusted.

This isn’t proof of real hardware control, and I wouldn’t use it as such. But as a test of planning logic and failure recovery inside Codex, it showed exactly what I was looking for.

III. GPT-6.1 Sol vs Sonnet 5.5: Where Each One Wins

Both models land at $2/1M input and $10/1M output, so price genuinely isn’t the deciding factor here.

It comes down purely to what kind of output your task needs.

When I’d reach for GPT-6.1 Sol:

• Long agentic coding tasks where speed and completion rate matter most

• Workflows where you want tight token usage and cost control through reasoning levels

• Real-time interaction tasks where the logic has to work correctly, not just look good

• Anything where you’re running lots of loops, because the cost efficiency stacks up fast

When I’d reach for Claude Sonnet 5.5:

• Visual quality is the point: landing pages, 3D environments, creative front-end work

• You need richer character or environment detail

• Long multi-step agent work where the polish of the final output matters

You can check my full Claude Sonnet 5.5 test below for the complete workflow and results.

Conclusion

So, to be honest with you, GPT-6.1 Sol left me with a pretty clear impression after these tests.

It’s a model I’d trust for serious building work inside Codex, especially when the task is long, the prompt is complex, and you just need something to actually run when you’re done. The Browser OS test showed that best: a huge single-prompt build, finished in under 34 minutes, with real cross-app connections. That’s impressive.

The visual side still has catching up to do, and if aesthetics or spatial accuracy are the point of your task, Claude Sonnet 5.5 is still the stronger choice.

But for coding speed, planning logic, and keeping your API bill under control, GPT-6.1 Sol earns its spot as the new default for agentic coding and computer use workflows.

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