GPT-6 vs Claude Opus 5.5 vs Gemini 4: Which AI Should Finance Teams Use?

GPT-6 vs Claude 5.5 vs Gemini 4: What Actually Launched

OpenAI: GPT-6 Astra and GPT-6.1 Sol. GPT-6 Astra arrived in early September as OpenAI's most capable model. At DevDay on 29 September, OpenAI followed it with GPT-6.1 Sol, which it says nearly matches Astra on agentic coding, computer use and professional work at one-fifth of Astra's API price. Sol is rolling out to ChatGPT Plus, Pro, Business, Enterprise and Edu users in ChatGPT Work, and a cheaper GPT-6 Luna model covers the free tiers.

Anthropic: Claude Opus 5.5 and Sonnet 5.5. Claude Opus 5.5 was released on 22 September for long-running agentic and knowledge work, with a one million token context window by default. Sonnet 5.5 followed on 28 September at the same price as its predecessor and noticeably faster. Anthropic's top model, Claude Fable, remains above both for the hardest work.

Google: Gemini 4 Argon. Announced on 30 September, Gemini 4 Argon is aimed at long-horizon work, and Google specifically names legal and financial knowledge work among its target uses. The catch is access: Google is releasing it first to cyber-defence partners, then to paid API customers and Google AI Ultra subscribers. Most Google Workspace users cannot use it yet.

For a finance team, that last point matters more than any benchmark. A model you cannot use inside your organisation's approved tools is not an option, however well it scores.

What Each Model Is Good At for Finance Work

Launch materials focus on coding and science benchmarks. Translated into finance work, the differences look like this.

GPT-6.1 Sol is the practical default for teams already on ChatGPT Business or Enterprise. OpenAI highlights improvements in understanding documents and executing multistep workflows, which maps well to reading contracts, summarising board papers and working through a reconciliation checklist. Astra is the step up for the hardest analysis, at a much higher cost.

Claude Opus 5.5 is strong on long documents and careful reasoning, and the one million token context means a full year of management accounts, a policy manual and a contract bundle can sit in one conversation. In our experience Claude models tend to be good at following a house style precisely, which suits variance commentary and board narrative. Sonnet 5.5 is the faster, cheaper choice for everyday drafting.

Gemini 4 Argon is the one to watch for Google Workspace finance teams, given Google's explicit focus on financial knowledge work and its very long outputs. Until it reaches Workspace, Gemini users will be working with the current 3.x models.

One caution applies to all three. Every vendor's benchmark claims come from the vendor, and none of these models should be trusted with an unchecked number. The capability gap between them is now small enough that your prompts, your context and your checking process will move results more than the choice of model.

What GPT-6, Claude 5.5 and Gemini 4 Cost

Most finance teams pay per seat through ChatGPT, Claude, Gemini or Microsoft 365 Copilot plans, so the API prices below mainly matter if you build your own automations. They are still a useful signal of relative cost. Published API prices per million input and output tokens, as reported at launch (see this comparison):

• GPT-6 Astra: $10 input, $50 output.
• GPT-6.1 Sol: $2 input, $10 output.
• Claude Opus 5.5: $4 input, $20 output.
• Claude Sonnet 5.5: $2 input, $10 output.
• Gemini 4 Argon: introductory pricing of $2 input, $10 output.

Two changes matter more than the headline numbers. First, frontier models are moving to usage-based billing inside business suites. Microsoft now bills its newest frontier models, OpenAI's Astra and Anthropic's Claude Fable, by usage on top of the Copilot licence. If your AI budget sits in the forecast as a tidy per-seat line, that assumption has a shelf life.

Second, the right comparison is not price per token but cost per completed task. A cheaper model that needs three attempts and a long human review can cost more than an expensive one that gets it right first time. For routine extraction and formatting, the cheapest capable model wins. For a board paper or a contract review, quality per attempt matters more. Track what a reliable output actually costs you, and you will make better model decisions than any leaderboard allows.

Choosing by Where Your Finance Team Already Works

For most finance teams the decision is made by their existing technology, and that is no bad thing. Data protection, single sign-on, retention policies and audit trails are already in place for the tools your organisation has approved.

Microsoft 365 organisations will get these models through Copilot. Microsoft's new Copilot app brings frontier models, including OpenAI's Astra and Anthropic's Claude Fable, alongside Word, Excel and PowerPoint, with the frontier models billed by usage. Check with IT which models are enabled in your tenant. Our guide to setting up Copilot for finance covers the configuration.

ChatGPT Business or Enterprise users get GPT-6.1 Sol as it rolls out, without changing anything. Confirm your workspace data settings before pasting financial data.

Claude Team or Enterprise users have Opus 5.5 and Sonnet 5.5 available. Projects let you store your chart of accounts, reporting templates and style guide once, which is where Claude tends to earn its place in finance.

Google Workspace users should plan for Gemini 4 Argon but not wait for it. The current Gemini models are capable enough for most finance drafting and summarising work today.

If you are choosing from scratch, pick the platform your IT and data protection teams can approve fastest, then invest in the skills. Switching models later is easy. Rebuilding habits is not.

How to Decide: A Ten-Minute Test on Your Own Work

Rather than relying on benchmarks, run the same real task through the models you have access to and compare the output against a version you already trust. A month you have already closed is ideal, because you know the right answer. We explain the approach in our guide to testing AI safely on a closed month.

Use the same structured prompt each time, so the model is the only variable:

Score each answer on three things: accuracy against what you filed, how much editing it needed, and how long it took. That is your benchmark, and it is the one that ends up in the business case. The prompt structure above is our RACEF framework, which works the same way in every model, so the skill transfers whichever one you choose.

Frequently Asked Questions