Claude Sonnet 5.5 vs GPT-6.1 Sol: Best mid-price model

Claude Sonnet 5.5 and GPT-6.1 Sol launched a day apart, at the same list price, aimed at the same buyers: teams that need strong results without paying flagship rates. Both cost $2 per million input tokens and $10 per million output tokens. Both support context windows of about 1 million tokens. On paper, these two mid-tier models look interchangeable.

In our Ready for Enterprise (RFE) LLM Benchmark, they aren't. Sonnet 5.5 ranks #2 overall with a score of 64.5, and GPT-6.1 Sol ranks #7 at 57.3. Sonnet is about three times faster, at 86 tokens per second versus 29. But GPT-6.1 Sol has a higher capability score (88.6 vs. 79.6) and clear leads in factual accuracy and advanced math.

The short answer to Sonnet 5.5 vs. GPT-6.1: it depends on the job. Choose Sonnet 5.5 for interactive applications, everyday coding, and coding agents. Choose GPT-6.1 Sol for fact-heavy research and quantitative work where speed matters less.

Which model fits which job?

• Choose Claude Sonnet 5.5 for speed and agentic coding. It's the faster model by a wide margin, responds sooner, and leads on Terminal-Bench 4.0. It fits user-facing assistants, coding agents, and workflows that chain many model calls together.
• Choose GPT-6.1 Sol for factual accuracy and math. It has the higher scores on SimpleQA Verified (73.9% versus 46.5%) and FrontierMath (93.7% versus 88.8%). It fits research, analysis, and quantitative tasks where getting the answer right matters more than getting it fast.
• Consider using both. Because the two models have the same token prices, you can route each task to the model that handles it best without paying a premium for either.

Sonnet 5.5 vs GPT-6.1 Sol at a glance

Source: FullStack RFE LLM Benchmark, data snapshot used for this article. Rankings can change as new models ship and results are added.

Our RFE score combines six public benchmarks covering agentic work, factual accuracy, human preference, web development, graduate-level science, and advanced math, then adds price and speed. The capability score reflects benchmark performance alone. That's why the two scores point in different directions here: GPT-6.1 Sol performs better on several benchmarks, but Sonnet 5.5's speed advantage lifts its overall enterprise score.

A note on vendor-reported results

Anthropic and OpenAI each publish launch results, but they mostly report different benchmarks, run in their own setup at their own effort settings. There are few shared benchmarks between the two launch pages. Even computer use isn't directly comparable: Anthropic reports Sonnet 5.5 on OSWorld 2.1, while OpenAI reports GPT-6.1 Sol on OSWorld 2.0, and the two OSWorld versions use different task sets. That's why this comparison relies on our RFE benchmark for head-to-head numbers and uses vendor-reported results only for context.

Where Claude Sonnet 5.5 wins

Speed

Speed is the biggest real difference between these models. In our measurements, Sonnet 5.5 generated 86 tokens per second, about three times GPT-6.1 Sol's 29. It also returned its first token sooner, at 1.89 seconds versus 3.51.

Independent testing also shows GPT-6.1 Sol trading speed for capability. Artificial Analysis measured GPT-6.1 Sol at 57.7 tokens per second, about two-thirds the speed of its predecessor, GPT-6 Sol. Speed numbers vary by provider, load, and settings, but both measurements show GPT-6.1 Sol running well below the speed of its predecessor, and below Sonnet 5.5 in our tests.

For a chat assistant, that gap shows up as waiting. For an agent that makes dozens of sequential tool calls and model calls, the delay compounds, so long agent runs can take noticeably longer on GPT-6.1 Sol.

Agentic coding

Sonnet 5.5 leads on Terminal-Bench 4.0, which measures multistep work in a command-line environment, at 61.8% versus 58.2%. It also edges ahead on Arena WebDev, a human-preference rating of web development output, at 1768 versus 1753.

Anthropic describes Sonnet 5.5 as a faster, lower-cost complement to Claude Opus 5.5, strongest at well-scoped everyday tasks such as routine bug fixing and creating polished documents, slides, and spreadsheets. Anthropic reports that on several benchmarks, Sonnet 5.5 at low or medium effort beats Sonnet 5's best score for about a tenth of the cost per task, because it needs far fewer tokens. Early testers noticed the same efficiency: Lovable reported a third fewer tool calls and roughly half the shell runs to finish a task.

Combined with its speed, that makes Sonnet 5.5 a strong default for coding agents, Claude Code workflows, and fast iteration.

Where GPT-6.1 Sol wins

Factual accuracy

GPT-6.1 Sol's clearest advantage is factual accuracy. It scores 73.9% on SimpleQA Verified, compared with 46.5% for Sonnet 5.5. That's the widest gap of any benchmark in this comparison.

OpenAI also reports factuality gains over the previous model. On one test of deliberately difficult prompts, GPT-6.1 Sol reduced the share of responses containing a factual error from 11.4% to 7.7% at low effort. If your workflow depends on the model recalling facts without retrieval, GPT-6.1 Sol is the safer choice. With Sonnet 5.5, ground those requests with search or retrieval-augmented generation (RAG).

Advanced math and capability

GPT-6.1 Sol leads on FrontierMath Tiers 1–3 at 93.7% versus 88.8%, and its overall capability score of 88.6 is the higher of the two. On graduate-level science, the models are effectively tied on GPQA Diamond (95.4% for GPT-6.1 Sol versus 95.6% for Sonnet 5.5).

Outside testing backs up its capability. On the Artificial Analysis Intelligence Index, a composite of 10 benchmarks, GPT-6.1 Sol at max effort scored 52, one point below GPT-6 Astra's 53, at $0.72 per task versus $3.26 for Astra.

Professional work and computer use

OpenAI positions GPT-6.1 Sol as near-Astra intelligence for about one-fifth of the price. In its vendor-reported results, the model matches GPT-6 Astra on the DeepSWE v1.1 coding benchmark at roughly one-fifth the cost. On GDP.pdf, a test of answering professional questions from complex PDFs, it scores higher than Claude Opus 5.5 with fallbacks at less than half the cost per task.. It also scores 2.2 percentage points above Opus 5.5 on AutomationBench, a test of multistep business workflows, at medium effort.

On computer use, GPT-6.1 Sol comes within 2.1 percentage points of Astra on OSWorld 2.0 at roughly one-seventh the cost per task. On Terminal-Bench Science, a test of scientific research workflows, it costs $5.47 per task on average at maximum effort versus $23.21 for Opus 5.5. For real professional work involving documents and data, that's a strong value case.

Why GPT-6 Sol ranks above GPT-6.1 Sol

One result in our benchmark may surprise you: the older GPT-6 Sol ranks #5 with an RFE score of 58.1, ahead of GPT-6.1 Sol at #7 with 57.3. The newer model is far more capable (88.6 versus 70.3 on our capability score) and better on factual accuracy (73.9% versus 60.7% on SimpleQA Verified).

The difference is speed. GPT-6 Sol runs at 69 tokens per second in our measurements, more than twice GPT-6.1 Sol's 29. Because our RFE score weighs speed alongside capability and price, the slowdown costs GPT-6.1 Sol more than its capability gains add.

For most teams, GPT-6.1 Sol is still the better OpenAI model for difficult work. But if your OpenAI workload is latency-sensitive and doesn't need the extra capability, GPT-6 Sol remains worth testing for its faster output. OpenAI has also announced GPT-6.1 Sol Ultrafast, with up to 8x faster token generation in Codex, which may close the gap for some use cases.

Pricing: identical rates, different cost per task

Both models have the same standard API prices: $2 per million input tokens and $10 per million output tokens, for a FullStack blended price of $4.00. Cached input costs $0.10 per million tokens on Claude Sonnet 5.5 and on GPT-6.1 Sol.

One pricing detail to watch: GPT-6.1 Sol's standard rates apply to prompts up to 272,000 input tokens, and very long prompts are billed at higher rates. If you plan to send large documents or codebases in one request, check OpenAI's current pricing before you build your cost model.

Equal list prices don't produce equal bills. What you pay depends on how many tokens each model uses to reach a finished task, plus retries, tool calls, and human review. Your workload shape matters too: short, frequent requests favor the faster model, while long-context jobs depend more on per-token pricing. Measure cost per completed task on your own workloads.

Context window, effort settings, and availability

Long context

Claude Sonnet 5.5 has a 1 million token context window and up to 128,000 output tokens. GPT-6.1 Sol has a slightly larger context window, at 1,050,000 tokens, with the same 128,000-token output limit. For practical purposes, both can hold large document sets or a sizable codebase in one request.

Effort levels

Both models let you choose how much reasoning to spend on a task, and those settings change behavior and cost. GPT-6.1 Sol supports five reasoning settings from low to max, with medium as the default, and it drops the option to turn reasoning off. Sonnet 5.5's default effort level is medium in Claude Code and the Claude apps, and high on the Claude Platform. When you compare the two, set the effort setting explicitly so the results are like-for-like.

Availability and safeguards

Sonnet 5.5 is available on the Claude API, Amazon Bedrock, Google Cloud, and Microsoft Foundry on Microsoft Azure, as well as in the Claude apps and Claude Code. GPT-6.1 Sol is available through the OpenAI API and, at launch, in ChatGPT Work and Codex for paid plans, though not yet in regular ChatGPT conversations. If your organization has standardized on a particular cloud, that may decide the choice before benchmarks do.

Security teams should also note that Sonnet 5.5 is the first Sonnet model to launch with cyber safeguards similar to those on Anthropic's most capable models. Higher-risk cybersecurity tasks fall back to Sonnet 5, while routine software development isn't affected. Test any security workloads before you run Sonnet 5.5 in production.

How to choose

Start with the work your team runs most often:

• User-facing chat and copilots: Sonnet 5.5. Faster first tokens and output speed make a noticeable difference to users.
• Coding agents and multistep automation: Sonnet 5.5. It leads on Terminal-Bench 4.0, and its speed compounds across many sequential calls.
• Research and fact-heavy answers: GPT-6.1 Sol, especially when retrieval isn't available.
• Math, financial modeling, and quantitative analysis: GPT-6.1 Sol.
• Complex, open-ended work that requires sustained judgment: Consider Claude Opus 5.5 or GPT-6 Astra. Anthropic says Opus 5.5 remains clearly stronger than Sonnet 5.5 at this kind of work.
• Background batch jobs: Either. When nobody is waiting on the result, GPT-6.1 Sol's slower speed matters less, so choose based on accuracy for the task.

Before standardizing, run both models on the same task set with identical prompts and tools. Track key metrics such as completion rate, factual errors, latency, and cost per accepted result. Many teams will find that routing tasks between the two delivers better results than choosing one. Our Agentic Workflow Implementation services help teams design that kind of multi-model workflow.

How FullStack can help

FullStack is an AI-native engineering partner and is Anthropic Certified through Anthropic's Claude Partner Network. We build production systems on whichever model fits the job, including Claude and OpenAI models, and we help enterprises evaluate models on their real workloads before they commit.

Our Forward Deployed Engineers embed with your team to build the agent or workflow you need and get it running in your systems.

^{Compare these models in the RFE benchmark.}