GPT-6.1 Sol vs GPT-5.6 Sol: time to leave GPT-5?

Many teams built their OpenAI workflows on GPT-5.6 Sol, which launched in July 2026. In late September, OpenAI released GPT-6.1 Sol, the latest model in the Sol line of its GPT-6 family.

Upgrades usually force a trade-off between better results and a higher bill. This one doesn't. In our Ready for Enterprise (RFE) LLM Benchmark, GPT-6.1 Sol has a far higher capability score than GPT-5.6 Sol (87.6 vs. 60.3), and its standard API price is half of GPT-5.6 Sol's current rate. The catch is speed: GPT-6.1 Sol generates output more slowly.

Below, we break down where GPT-6.1 Sol pulls ahead, what the slower output costs you, and how to plan the move, even if you’re still on an older GPT-5 model.

Should you upgrade from GPT-5.6 Sol?

For most teams, yes. GPT-6.1 Sol leads on agentic coding, factual accuracy, science, and math, and it costs less per token. Coding agents, automation workflows, research, and other complex work where analysis quality matters should move first.

The exception is speed-sensitive work. GPT-5.6 Sol is about 60% faster in our measurements, so user-facing features with long, streamed responses should be tested on real traffic before you switch. If they fall short on latency, keep them on GPT-5.6 Sol for now and move the rest.

GPT-6.1 Sol vs GPT-5.6 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. We built it for side-by-side model comparisons of the AI models enterprise teams are evaluating. The capability score reflects benchmark performance alone. GPT-5.6 Sol hasn't been tested on Arena WebDev in our benchmark yet, so its scores are based on five of the six components.

Where GPT-6.1 Sol pulls ahead

Head-to-head, GPT-6.1 Sol leads on four of the five scored benchmarks both models were tested on.

Agentic coding

The biggest gap is in agentic work. GPT-6.1 Sol scores 58.2% on Terminal-Bench 4.0, which measures multistep tasks in a command-line environment, compared with 37.3% for GPT-5.6 Sol. That's a lead of more than 20 points, and it's the clearest reason to move coding agents and automated workflows to the newer model.

Factual accuracy, science, and math

GPT-6.1 Sol also leads on SimpleQA Verified (73.9% vs. 69.7%), GPQA Diamond (95.4% vs. 93.5%), and FrontierMath Tiers 1–3 (93.7% vs. 89.1%). These gaps are smaller, but they all point the same way.

Computer use

OpenAI doesn't publish a direct computer-use comparison between the two models, but its numbers point the same way. OpenAI reports that GPT-6.1 Sol comes within 2.1 percentage points of GPT-6 Astra on OSWorld 2.0's offline set at max reasoning. OpenAI's Astra launch page lists GPT-5.6 Sol 6.9 points below Astra on the same test, at 65.7% versus 72.6%.

Where the models are even

On Arena Text, a human-preference rating of written responses, the two models are effectively tied, with GPT-5.6 Sol slightly ahead (1485 vs. 1480). This is where the two models overlap most, and a five-point difference is too small to matter in practice. If your workload is mostly short conversational answers, the capability gain will matter less.

Some things haven’t changed. Neither model has a larger context window: both support 1,050,000 tokens and up to 128,000 output tokens, and both accept text and image input. The main differences are newer training data, with a knowledge cutoff of April 30, 2026, versus February 16, 2026, for GPT-5.6 Sol, and where you can use it. Beyond the OpenAI API, paid plans can access GPT-6.1 Sol in ChatGPT Work and Codex.

GPT-6.1 Sol costs half as much, for now

GPT-6.1 Sol costs $2 per million input tokens and $10 per million output tokens, with cached input at $0.10. As of the time of writing, GPT-5.6 Sol costs $4 and $20, with cached input at $0.40.

That GPT-5.6 Sol rate is promotional. The model launched at $5 per million input tokens and $30 per million output tokens, and OpenAI’s model page lists the promotional price through November 30, 2026. OpenAI hasn't published what GPT-5.6 Sol will cost after that. If it returns to its launch rate, GPT-6.1 Sol would cost about a third as much on a blended basis ($4.00 vs. about $11.25).

A higher reasoning effort, such as max, creates more reasoning tokens, and reasoning tokens are billed as output tokens, so it raises the cost of each task. Set reasoning effort explicitly when you compare the two models.

Long prompts cost more on both models. Requests above 272,000 input tokens are billed at twice the input rate and 1.5 times the output rate for the full request.

Per-token price is only part of the bill. What you pay depends on how many tokens each model uses to finish a task, plus retries and human review, so measure cost per completed task on your own workloads.

What the slower output costs you

GPT-5.6 Sol is the faster model. In our measurements, it generates 62 tokens per second, compared with 39 for GPT-6.1 Sol, and returns its first token sooner (2.91 seconds vs. 3.97). Exact speeds vary by provider and load. That speed advantage is also why the RFE gap between the two models is smaller than the capability gap.

Here's what that means for total response time, using our measured averages:

Estimates use time to first token plus output length divided by measured output speed. Real response times also depend on reasoning effort, tool calls, and load.

For batch jobs and background agents, nobody is waiting, so the extra time rarely matters. For a chat assistant streaming long answers, users will notice. Test GPT-6.1 Sol on your real response lengths before moving those features, and if you're interested in faster responses, check whether GPT-6.1 Sol Ultrafast fits your use case.

What to change before you switch

Moving to GPT-6.1 Sol takes more than changing the model name in your configuration. OpenAI's migration guide lists a few things to update:

• Reasoning can't be turned off. GPT-5.6 Sol supports a "none" reasoning setting. GPT-6.1 Sol supports five: low, medium (the default), high, xhigh, and max. Replace "none" with "low” and retest latency and cost.
• Tool calling requires the Responses API. GPT-6.1 Sol works with Chat Completions, but only for requests without tool calls, so fix any tool-using requests before you switch.
• Some sampling parameters are unsupported. Remove temperature, top_p, and top_logprobs from requests that use reasoning.

Then build a small evaluation set from your own work and run both models on the same prompts, tools, and reasoning settings. Compare completion rate, error rate, latency, and cost per accepted result. Route each task to the model that passed your tests, and keep the model choice in configuration so you can switch back quickly if performance drops.

^{For help bringing new models into your delivery process, see our AI SDLC Transformation services.}

Still on GPT-5.4 or GPT-5.2?

If you skipped GPT-5.6 Sol, the case for moving is even stronger. GPT-5.4 costs $2.50 per million input tokens and $15 per million output tokens, more than GPT-6.1 Sol on both, and its knowledge cutoff is August 31, 2025.

Parts of the older GPT-5 line are already being retired. According to OpenAI's deprecations page, gpt-5.2-codex was shut down on July 23, 2026, and gpt-5.2-chat-latest on August 10, 2026, with GPT-5.6 Sol listed as the replacement for both. Check that page for the specific model versions your applications call, and plan the migration before a shutdown date forces it.

For teams on GPT-5.4 or GPT-5.2, moving straight to GPT-6.1 Sol makes more sense than stopping at GPT-5.6 Sol. The same migration steps apply.

How FullStack can help

FullStack builds OpenAI-powered systems for clients and runs five of its own in production, covering retrieval, prospecting, voice, client-facing search, and talent matching. Our OpenAI implementation work helps enterprises test new model versions on their real workloads, migrate without surprises, and route each task to the model that handles it best.

^{Compare these models in the RFE benchmark.}