Opus 5.5 vs Sol 6.1: Coding Benchmarks & API Pricing
Compare Opus 5.5 vs Sol 6.1 using published coding benchmarks, API pricing and long-context cost examples. See which model fits your workload and budget.
Opus 5.5 vs Sol 6.1 is a tradeoff between published coding quality and cost. Opus scores higher on Cognition’s FrontierCode 1.1 Main comparison at medium effort; Sol costs less per evaluated run and has lower standard short-context API prices. Cognition leaderboard, OpenAI pricing, Anthropic pricing.
Start with Sol if shorter requests and budget dominate your workload. Test Opus alongside it when code quality or repeated corrections matter more. For very long inputs, compare the actual bill: the price gap can shrink substantially.
This guide compares Anthropic’s Claude Opus 5.5 with OpenAI’s GPT-6.1 Sol, including coding benchmarks, task duration, API pricing, and two worked budgets.
Updated October 10, 2026. Benchmark results below belong to Cognition. Kavello has not run a private head-to-head test; cost examples are calculations, not measured API runs.
Opus 5.5 vs Sol 6.1 at a glance
| Detail | Claude Opus 5.5 | GPT-6.1 Sol |
|---|---|---|
| API model ID | claude-opus-5-5 |
gpt-6.1-sol |
| Context window | 1 million tokens | 1.05 million tokens |
| Standard maximum output | 128K tokens | 128K tokens |
| Input and output | Text and images → text | Text and images → text |
| Standard input price | $4 per million tokens | $2 per million tokens |
| Standard output price | $20 per million tokens | $10 per million tokens |
Sol’s rates above apply to prompts up to 272K input tokens. Larger prompts use different rates, explained below. Opus also has a separate output-limit beta for batch processing; the table compares ordinary output limits. Sources: Claude Opus 5.5 specifications, GPT-6.1 Sol specifications.
Opus 5.5 vs Sol 6.1 coding benchmarks
Cognition’s live FrontierCode 1.1 Main board provides a direct comparison of the requested models. At medium effort, it reports:
| Published metric | Opus 5.5 | Sol 6.1 |
|---|---|---|
| Weighted score | 54.6% | 50.2% |
| Mean cost per rollout | $0.80 | $0.36 |
| Agent environment | Claude Code | Codex |
Snapshot checked October 10, 2026. Values are rounded from Cognition’s original leaderboard data, revision v1_1, subset main, effort medium.
The weighted score measures rubric performance; it is not the percentage of tasks completed or a probability that your next task succeeds. Cognition evaluates correctness, test quality, scope discipline, and repository conventions. Its separate pass-rate metric should not be confused with this score. Benchmark methodology.
Opus leads the score in this comparison; Sol uses less money per rollout. Because the agents differ, these results compare published model-and-tool configurations. Matching the effort label does not isolate model capability or equalize compute.
For a small development team, start with the work you actually need done:
| Your task | What should decide the comparison? |
|---|---|
| Fix a reproducible bug | Does the fix pass a regression test without breaking adjacent behavior? |
| Add a feature across several files | Does it follow existing architecture and cover all entry points? |
| Build a UI from a reference | Does the result match at desktop and mobile sizes? |
| Review a pull request | Are findings reproducible, correctly prioritized, and free of false alarms? |
| Investigate an unfamiliar repository | Does it find the relevant code and explain it with accurate file references? |
My suggested starting point is Sol for routine work where API budget matters, then test Opus on the tasks that need repeated corrections. That is a purchasing and evaluation strategy, not a claim that one model always reasons better.
If you already have a reliable Claude workflow, include the cost of changing tools and prompts. A cheaper token rate may not justify disrupting a process that consistently produces reviewable changes.
Which is faster for coding tasks?
Cognition’s same medium-effort Main records list task durations of 6.34 minutes for Opus and 14.42 minutes for Sol. These are the dataset’s duration_min values, not time to first token or output tokens per second. Source: Cognition’s original data.
Use that result as a reason to test elapsed task time in your own environment, not as a universal speed guarantee. Repository setup, tool execution, effort, and retries affect what a developer experiences. An interactive chat latency test answers a different question from a completed coding task.
Opus 5.5 vs Sol 6.1 API pricing
For standard processing, the published token rates are:
| Token category | Opus 5.5 | Sol 6.1, input ≤272K | Sol 6.1, input >272K |
|---|---|---|---|
| Uncached input | $4 | $2 | $4 |
| Cached input / cache read | $0.20 | $0.10 | $0.20 |
| Output | $20 | $10 | $15 |
USD per million tokens. Cache creation is billed separately; the cache-read row is not a cache-write price. Sources: Anthropic pricing, OpenAI pricing.
For Sol, crossing 272K input tokens increases input and cache rates by 2× and output rates by 1.5× for the whole request. Opus 5.5 keeps its standard per-token rates across its 1M context window. Sol’s long-context rule, Claude long-context pricing.
Short and long-context cost examples
These examples assume uncached input, standard processing, and the stated number of billable output tokens, not merely the words visible in the final answer. They exclude tools, retries, regional premiums, taxes, and discounts. Equal token counts isolate pricing; they do not predict how many tokens each model will use for the same task.
A shorter request: $0.06 vs $0.12
Assume 20,000 input tokens and 2,000 output tokens:
- Sol: 0.02 × $2 + 0.002 × $10 = $0.06.
- Opus: 0.02 × $4 + 0.002 × $20 = $0.12.
For 1,000 identical requests, the calculated totals are $60 and $120. Here, Sol is 50% cheaper at equal usage. This is not an estimate for 1,000 completed coding tasks: one task may require many requests.
A long request: $1.35 vs $1.40
Now assume 300,000 input tokens and 10,000 output tokens:
- Sol: 0.3 × $4 + 0.01 × $15 = $1.35.
- Opus: 0.3 × $4 + 0.01 × $20 = $1.40.
Sol’s saving is approximately 3.6%, rather than 50%. At this prompt size, a small difference in retries or output length could reverse the total cost ordering. These calculations use the published OpenAI rates and published Anthropic rates.
The practical lesson: inspect your prompt sizes before forecasting savings from a model switch.
How to run a useful comparison on your codebase
Choose 10–20 representative tasks and keep a clean copy of the repository for each run. Include easy maintenance work and difficult bugs; a set containing only your hardest problems will not represent everyday spend.
- Give both models the same task description, repository revision, documentation, and acceptance criteria.
- Record the model version, agent software, available tools, permissions, effort setting, time limit, and retry budget. Identically named effort settings are not proof of equal compute.
- Evaluate the final changes using tests and human review. Check UI work in a browser. Do not let a model’s “done” message count as success.
- Record total API spend across all attempts, elapsed time, manual corrections, and whether the task was accepted.
- Repeat enough tasks to see whether the result survives a different mix of work.
Calculate cost per accepted task = total spend on all attempts ÷ accepted tasks. Include failed attempts in the numerator. Track review time separately so you can see when saving API spend creates more work for a person.
If you compare Claude Code with Codex, label the result as a comparison of complete workflows. Different tools, context management, and prompts are part of that result. For a model-focused experiment, keep the surrounding agent setup as similar as practical.
Opus or Sol: which should you choose?
- Start by testing Sol when you have many shorter requests and cost is the main constraint.
- Give Opus a direct trial when your prompts are very large or repeated corrections dominate the work. Its higher short-context price matters less if it produces more accepted results per attempt. Measure that before assuming it.
- Keep both available only if your results show a useful split. For example, routine tasks could use one model while difficult failures are escalated to the other. Log both stages when calculating cost.
A model switch should earn its place with accepted work, predictable spend, and manageable review time.
For a separate high-volume classification or routing workload, see our Haiku 5.5 pricing guide. It covers a different cost profile from the coding comparison here.
Frequently asked questions
Is GPT-6.1 Sol cheaper than Claude Opus 5.5?
At equal token counts, its standard short-context input and output rates are 50% lower. Sol’s long-context tier changes the comparison, and different token usage or retries change the cost of finishing a task.
Does a ChatGPT or Claude subscription use these prices?
The figures in this article are API token rates. Do not use them to calculate the value of a subscription without checking that plan’s included usage, limits, and billing rules.
Does a bigger context window mean better coding?
Capacity alone does not demonstrate better fixes. Test whether the model finds the right files, preserves constraints, and completes the work correctly with the context you supply.
Did Kavello benchmark these models?
No. Coding scores and task durations are attributed to Cognition. The API examples are our calculations from official rates. Neither should be read as a private Kavello benchmark.