Everyone quotes the same two numbers: GPT-6 Sol is $2/$10 per million tokens, Claude Opus 5.5 is $4/$20. Sol looks half the price. Case closed, right?
Not quite. An AI coding agent reads a file, calls a tool, edits code, and calls another tool sometimes 20 or 50 times for one task. The sticker price barely matters once you factor in cache hits, retries, and how many tokens each model burns to finish. That's the number this post works out.
Quick Answer
GPT-6 Sol is cheaper per token, but usually not per finished task. At list price, Sol is about $2/$10 per million input/output tokens versus Opus 5.5's $4/$20, making it roughly half the price.
However, in a simulated 20-turn coding agent loop, Sol's cost savings drop from 1.87x to about 1.4-1.5x when accounting for the extra retries a cheaper model needs on hard tasks.
For short, high-volume agent loops, Sol wins easily. But for longer, more difficult tasks where a single attempt matters, the actual price difference is much smaller than headline rates suggest.
Price on paper: GPT-6 Sol vs Claude Opus 5.5
Here's the sticker price for both, side by side:
| Cost per 1 million tokens | GPT-6 Sol | Claude Opus 5.5 |
|---|---|---|
| Input | $2.00 | $4.00 |
| Cached input (repeat context) | $0.20 | $0.20 |
| Output | $10.00 | $20.00 |
| Context window | ~1.05M tokens | 1M tokens |
| Max output | 128K tokens | 128K tokens |
Two key points explain this difference. First, cached input costs the exact same rate ($0.20 per million tokens) across both models. Cached input refers to the part of your prompt the model has already processed, such as your codebase or chat history, which is billed at a discount. In long agent sessions, most tokens are cached repeats, making the initial input price gap far less significant.
Second, past roughly 272K input tokens, Sol's rates increase for both input and output. When coding agents work on real software projects, they quickly cross this limit, steadily shrinking Sol's cost advantage over time.
The real question: cost per finished task
Token prices show what one request costs, but they do not tell you how many tokens a model needs to finish a job, how many retries it takes, or how much context gets cached.
A cheaper model per token can end up costing more per finished task if it needs multiple attempts or writes longer responses. This cost per completed task is what actually determines your final bill.
Worked example: a 20-turn coding agent loop
Let's say a coding agent needs 20 turns to complete a task, similar to the kind of multi-step automation covered in our piece on AI agents replacing SaaS seats.
During those turns, it might:
- Read files
- Write or update code
- Run tests
- Check errors
- Fix problems
- Repeat until the task is complete
On average, each turn uses:
- 20,000 cached input tokens: existing conversation and project context
- 3,000 fresh input tokens: new tool results, errors, or instructions
- 2,000 output tokens: the model's response and generated code
Cost per Turn
| Token Type | GPT-6 Sol | Claude Opus 5.5 |
|---|---|---|
| Cached input (20K) | $0.004 | $0.004 |
| Fresh input (3K) | $0.006 | $0.012 |
| Output (2K) | $0.020 | $0.040 |
| Total per turn | $0.030 | $0.056 |
If the agent needs 20 turns to finish the task:
| Model | Cost for 20 Turns |
|---|---|
| GPT-6 Sol | $0.60 |
| Claude Opus 5.5 | $1.12 |
So, in this example, Sol costs about 1.87x less than Opus 5.5.
But There Is Another Cost: Retries
A cheaper model is not always cheaper if it needs more attempts to complete the task successfully.
For example, suppose:
- Sol needs 1.4 attempts on average
- Opus 5.5 needs 1.1 attempts on average
Then the estimated cost per successfully completed task becomes:
- Sol: $0.60 x 1.4 = $0.84
- Opus 5.5: $1.12 x 1.1 = $1.23
That changes the difference:
- Before retries: 1.87x cheaper
- After retries: about 1.47x cheaper
The Main Lesson
The cheapest model on paper may not always be the cheapest model in real-world use.
When comparing coding agents, track these three numbers:
- Tokens used per turn
- Number of turns per task
- How often the agent needs to retry
Then use those numbers to calculate your real cost per completed task.
Note: The retry rates above are only examples, not published benchmark results. Your actual results may be different depending on the type and difficulty of your coding tasks.
Hidden costs and mistakes to avoid
Hidden Costs That Can Increase Your AI Bill
The price shown in a model's pricing table is only part of the story. A few other factors can increase your actual costs:
1. Runaway Reasoning Costs
Some models can spend a large amount of tokens on reasoning before producing an answer.
For example, testing has shown that Opus 5.5 at its highest "max" reasoning setting can use its full 128K output limit without producing a usable answer. You can still be charged for that failed request.
This becomes more expensive when an agent automatically retries the task. Setting a reasonable limit on reasoning effort can help control this cost.
2. Long-Context Costs on Sol
Sol becomes more expensive when the input context gets very large.
Once the input goes beyond roughly 272K tokens, both input and output pricing increases.
This matters for coding agents working with large codebases because they can reach this limit faster than expected.
3. Fast and Batch Modes Have Trade-Offs
Faster processing usually costs more.
- Sol Fast: roughly 2x the normal rate
- Opus 5.5 Fast: $8/$40, with speeds of up to around 2.5x
- Sol Batch: roughly 50% cheaper, but it is designed for background processing rather than real-time tasks
Fast mode makes sense when speed is essential, whereas Batch mode is better suited for background jobs that do not need immediate answers, an architecture decision that often overlaps with how teams handle their cloud and DevOps setup.
4. Don't Compare Models by Token Price Alone
One of the biggest mistakes is looking only at the price per token and using that to estimate your monthly bill.
Your real cost also depends on:
- How many turns an agent needs
- How much output it generates
- How often it retries
- How much context it sends
- Whether you use Fast or Batch processing
The Bottom Line
If you're building AI agents or automation for a product, calculate the expected cost using your actual workloads before choosing a model at scale.
Looking at token prices alone can give you a very different number from your real monthly bill.
This is also an area where our AI automation work can help teams model and control their AI costs.
Which Model Should You Pick?
There isn't one model that is best for every coding task. The right choice depends on the type of work, how often you run it, and how expensive retries are.
| Your situation | What to consider first | Why |
|---|---|---|
| High-volume, repetitive coding tasks | GPT-6 Sol | Lower token costs can make it more economical for frequent tasks |
| Difficult tasks where retries are expensive | Claude Opus 5.5 | A higher success rate can sometimes justify the higher cost |
| Long agent sessions with lots of context | Calculate the cost per session | Sol's higher pricing after 272K input tokens can change the cost comparison |
| Simple classification or routing | Use a smaller model | Sol and Opus 5.5 may be more capable than the task requires |
A Practical Approach
Instead of using the same model for every task, you can use a tiered approach:
- Start with GPT-6 Sol for regular coding tasks.
- Monitor failures, retries, and token usage.
- Send difficult or high-risk tasks to Claude Opus 5.5 when the extra reliability is worth the additional cost.
- Use smaller, cheaper models for simple tasks such as classification, routing, or basic extraction.
This approach lets you balance cost, speed, and reliability instead of optimizing for token price alone.
If you're designing an AI agent pipeline, our AI agents content and case studies go deeper into the architecture and implementation side.
Conclusion
GPT-6 Sol appears much cheaper than Claude Opus 5.5 when comparing only headline token prices. However, real-world agent costs depend on much more than list rates. Retries, context size, cache usage, reasoning effort, and the number of turns all impact the final bill.
Sol is a practical choice for high-volume, repetitive coding tasks, while Opus 5.5 works well for complex tasks where failed attempts are expensive. The key is to compare total cost per completed task rather than price per token. Test both models on your specific workload, track retries and token usage, and use those real metrics to guide your choice.
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