Claude Opus 5.5 vs GPT-6.1 Sol: Which AI Coding Model Is Better in 2026?

If you write code for a living in late 2026, you have probably noticed the same thing everyone else has: two models keep showing up in every conversation about AI-assisted development. Anthropic's Claude Opus 5.5 landed on September 22, and OpenAI answered a week later, on September 29, with GPT-6.1 Sol.

Both claim to be great at coding. Both ship with roughly a million tokens of context. But they are priced very differently, and they make different trade-offs. So which one deserves a place in your editor, your CI pipeline, or your agent stack?

This guide goes through the published numbers, explains where those numbers can mislead you, and ends with a practical way to choose. No hype, no "winner takes all" headline, because the honest answer depends on what you build.

The Short Answer

• Pick Claude Opus 5.5 when the task is hard, long-running, or expensive to get wrong: large refactors, tricky debugging, multi-file changes in a codebase you can't afford to break.
• Pick GPT-6.1 Sol when you run a lot of agent tasks and cost per task matters more than squeezing out the last few points of quality.
• Use both if you can. Route the difficult jobs to one and the bulk jobs to the other. Switching costs very little.

Now let's see why.

Meet the Two Models

Two details are worth pausing on.

First, Opus 5.5 is not the cheapest way to buy Anthropic intelligence. It is cheaper than its predecessor: Anthropic cut list prices by 20% versus Opus 5 and cache reads by 60%. Cache reads matter more than they sound, because a coding agent re-reads the same repository context again and again during one session.

Second, GPT-6.1 Sol is deliberately not OpenAI's top model. It is the "good enough for almost everything, at a fraction of the price" option. OpenAI's own pitch is that it comes close to the GPT-6 Astra flagship on agentic coding at about a fifth of the token price. That makes the comparison interesting: a flagship against a value-oriented workhorse.

One pricing footnote. Sol's rates apply up to 272K input tokens per request, and long prompts beyond that are billed differently. If you plan to stuff huge codebases into a single call, read the current pricing page before you do the maths.

Coding Benchmarks: What the Numbers Say

Here are the headline figures each company or tracker has published. Treat the table as a map, not a verdict. I explain why in the next section.

A few things jump out.

The two models barely differ on DeepSWE. This benchmark drops an agent into a real repository and asks it to resolve a genuine engineering task. Sol's 75.2% and Opus 5.5's 74.2% are within a point of each other, which is well inside the noise you would expect between different test harnesses.

Opus 5.5 has the louder coding scorecard. Anthropic reports 89.9% on SWE-bench Pro, 93.9% on its multilingual version, and a big jump on Terminal-Bench 4.0 over the previous Opus. OpenAI simply did not publish matching numbers for Sol on those tests, so you cannot compare them directly.

Sol tends to win the "work in a business setting" tests. On AutomationBench and GDP.pdf, which simulate office-style multi-step tasks, Sol edges ahead of Opus 5.5 in OpenAI's own charts.

Independent indexes lean towards Opus. On Artificial Analysis's composite intelligence index, Opus 5.5 sits around 58 and Sol around 52, though settings and effort levels differ between runs. Another aggregator that blends many public benchmarks also gives Opus 5.5 the higher overall score. Sol is the one that wins on price.

Why You Shouldn't Trust a Single Benchmark Number

I want to be upfront about this, because most comparison posts skip it.

Many of these scores come from the vendors themselves. Anthropic published the Opus 5.5 table, and OpenAI published the Sol table. Each company picks the tests, the effort setting, and the scaffolding that shows its model well. That is normal, but it means you should read them as marketing-grade evidence until someone independent repeats them.

The 89.9% SWE-bench Pro score needs context. A public leaderboard that runs a standardized scaffold puts its top score much lower, around 61.5%. The gap comes from different harnesses, tool budgets, and retry rules. Both numbers can be "true" and still not be comparable. A fair read: Opus 5.5 is very strong on that test, but 89.9% is not what you will see in your own repo.

Effort settings change everything. Opus 5.5 defaults to medium effort in the API. Sol's best published coding score uses high effort, while some competitors' headline numbers use max. Comparing a medium run to a max run is like comparing a jog to a sprint.

Cost-per-task figures are model-specific. OpenAI reports Sol finishing a DeepSWE task for roughly $0.65 at high effort. Third-party trackers put Opus 5.5 somewhere between about $0.80 and $1.55 per comparable task, depending on effort. These are useful for ballpark thinking, but your prompts, repo size, and tool calls will shift them.

Cost: Where GPT-6.1 Sol Pulls Ahead

On pure price, there is no contest. Sol is half the price of Opus 5.5 on input, output, and cache reads.

Here is a simple, illustrative example using the published per-task figures:

That is a gap of a few hundred dollars for a small team, and it scales linearly. If you are running an autonomous bug-fixing bot, a large PR review pipeline, or a customer-facing coding assistant with thousands of daily calls, Sol's economics are hard to ignore.

But cheaper per task is only a win if the task succeeds. If Opus 5.5 solves a hard problem on the first attempt and Sol needs three retries, the cheaper model can end up costing more in tokens and in your own time. That is why the next section matters.

Where Claude Opus 5.5 Pulls Ahead

Hard, long-horizon work. Anthropic positions Opus 5.5 for long-running agentic coding, and its Terminal-Bench 4.0 and CursorBench results back that up. These tests reward models that can keep a plan intact across dozens of tool calls without drifting. If you hand an agent a vague "migrate this service to the new SDK" ticket and walk away, steady behaviour over many steps is exactly what you pay for.

Reliability and fewer babysitting moments. Early customers quoted by Anthropic reported lower token use and fewer calls on agentic coding jobs compared with the previous Opus. Those are vendor-selected testimonials, so take them with a grain of salt, but the direction is consistent with the benchmark story.

Responsiveness. One aggregator measured a noticeably shorter median time to first token for Opus 5.5 than for Sol, though Sol streams slightly faster once it starts. For interactive use inside an editor, that first-token delay is what you actually feel. Treat this as a single-source measurement and test it on your own connection.

Cache-heavy sessions. Because cache reads are 60% cheaper than on Opus 5, long coding sessions that keep revisiting the same files cost less than the list price suggests.

Where GPT-6.1 Sol Pulls Ahead

Price-to-performance. OpenAI's strongest claim is that Sol roughly matches its flagship on repository-level coding at about one-fifth of the cost, and the DeepSWE numbers support that. For most routine engineering work, that is a hard deal to beat.

Office-style automation. Sol's lead on AutomationBench and GDP.pdf suggests it handles multi-step business workflows well, such as reading a spreadsheet, calling an API, and writing a report. If your "coding" includes a lot of glue work and ops automation, that matters.

Computer use at low cost. On OSWorld 2.0, Sol scores 71.4% at max effort, within a couple of points of OpenAI's flagship, and at a fraction of the per-task cost.

Slightly larger context. 1.05M tokens versus 1M is not a meaningful difference in practice, but it exists.

Fewer hallucinated facts at low effort. OpenAI reports Sol's factual error rate dropped to 7.7% from 11.4% on the previous Sol, which helps when the model explains APIs from memory. That figure is vendor-reported and compares Sol with its own predecessor, not with Opus.

A Real Harness Test Worth Noting

Benchmarks run by the model makers are one thing. Comparisons that put each model inside its own native coding tool are closer to daily life. One third-party comparison put GPT-6.1 Sol inside Codex and Claude Opus 5.5 inside Claude Code on the same task set. The results came out at roughly 50% for Sol at about $0.36 per run and roughly 55% for Opus 5.5 at about $0.80 per run.

Notice the shape of that result. Opus 5.5 solved more tasks. Sol cost less than half as much per run. Neither number is "wrong", and that tension captures the whole decision: more success per attempt versus more attempts per dollar.

This is a single outside source, so I would not build a purchasing decision on it. I include it because it matches what the other evidence implies.

Which One Should You Choose?

Use this as a rough decision guide rather than a rule.

The two-model routing idea

Many teams do not need to pick a side. A simple pattern works well:

1. Send routine, well-defined tasks to GPT-6.1 Sol.
2. Escalate anything that fails twice, touches critical code, or needs deep multi-file reasoning to Claude Opus 5.5.
3. Track cost per merged change, not cost per call.

That last point is the metric that settles arguments. A model that is cheap per call but produces changes you have to rewrite is not cheap.

How to Test Them on Your Own Code

Public benchmarks cannot see your repository, your framework quirks, or your team's conventions. A one-afternoon experiment will tell you more than any chart.

1. Pick 15 to 20 real tickets from your backlog, with a mix of easy, medium, and hard.
2. Give both models the same context, the same tools, and the same instructions.
3. Record whether the result passed your tests, how many retries it needed, how long it took, and what it cost.
4. Have a teammate review the code without knowing which model wrote it.
5. Compare cost per accepted change, not raw accuracy.

If you use an API gateway that exposes both models behind one endpoint, switching is a one-line change to the model string, so there is little reason not to try.

Frequently Asked Questions

Is Claude Opus 5.5 better than GPT-6.1 Sol for coding? On the evidence available, Opus 5.5 looks stronger on hard, long-running coding work, while Sol offers much better value for routine agentic tasks. "Better" depends on whether you optimize for quality per attempt or cost per task.

Which model is cheaper? GPT-6.1 Sol. Its input, output, and cached-input prices are each half of Opus 5.5's list prices.

Do both models support a 1M-token context window? Yes. Opus 5.5 offers 1M tokens and Sol offers 1.05M tokens, with up to 128K tokens of output on each.

Can I trust the benchmark numbers? Treat vendor-published scores as directional. Different harnesses, effort settings, and retry rules make cross-vendor comparisons unreliable. Your own tests on your own code are the most trustworthy evidence.

Is GPT-6.1 Sol the same as GPT-6 Sol? No. GPT-6.1 Sol is the newer release from September 29, 2026 and improves on the earlier GPT-6 Sol, so older comparisons that use the previous model do not apply to this one.

Should I wait for newer models? This space moves quickly, and both companies have released updates within weeks of each other. Unless a project is blocked on quality, choose what works now and keep your integration flexible so you can swap models later.

Final Verdict

If you want a single sentence: Claude Opus 5.5 is the stronger specialist for demanding coding work, and GPT-6.1 Sol is the smarter budget for everything else.

Developers who ship daily and care about getting complex changes right the first time will probably enjoy Opus 5.5 more. Teams running large volumes of automated tasks will likely save real money with Sol and lose very little. The best answer, as usual, is to measure on your own work and let the numbers from your codebase decide.

I will update this article as independent results come in and as pricing changes. If you have tested both models on a real project, I would like to hear what you found.

---

Benchmark and pricing figures in this article were collected from vendor announcements and public comparison trackers in October 2026 and may change. Always check the official pricing pages before committing to a budget.