Updated on June 25, 2025
At Kommunicate, we often play around with the latest releases of AI models to understand how we need to update our customer service platform. 2025 has seen big releases from every big frontier lab, and the products have become more complex.
With more agentic tools and abilities, the current group of State-of-the-Art (SOTA) models, OpenAI O3 Pro, Claude Opus 4.0, and Gemini 2.5 Pro, have become more capable models for customer service.
So, which one should you choose to build your AI agent for customer service? Let’s review the individual USPs of these models and then compare them head-to-head for a customer service-focused overview. We’re going to cover:
1. What’s New in OpenAI O3 Pro?
2. What’s New in Claude Opus 4.0?
3. What’s New in Gemini 2.5 Pro?
4. Which Model Should You Choose: Open AI O3 v/s Claude Opus 4.0 vs/Gemini 2.5 Pro?
5. How Will These Recent Models Affect Customer Service?
6. Conclusion
What’s New in OpenAI O3 Pro?
One consistent theme across all these models is the inclusion of tool use and deep research capabilities. These function-calling capabilities allow these models to solve complex customer questions in a short amount of time.
However, since these are reasoning models, they take more time to answer questions. This can become a limitation for specific use-cases (customers won’t wait long for a reply during a voice call). However, it can be beneficial to include these capabilities and enable them to solve some real-life problems during customer service so that customers get real-time solutions. Some new features that will help you solve customer problems faster are:
1. Reasoning Core – To improve hallucination rates and solve complex problems, OpenAI O3 has been trained to “think before answering.” This feature is similar to O1, but O3 Pro can reason for longer and provide more accurate answers.
2. Large Context and Output – O3 Pro has a large context window of 200,000 and can provide answers of up to 100,000 tokens in length. This allows the AI agents powered by O3 to read more of your documents and provide more detailed explanations.
3. Large integrated OpenAI stack – OpenAi has been experimenting with different tool calls and adding new capabilities to its models. Currently it can
| Built‑in tool | Customer Service Use‑Case |
| Web browser | Pull live shipping estimates or outage pages |
| Python runner | Recalculate refunds, generate graphs for KPI tickets |
| File/image analysis | Extract text from a user‑uploaded receipt, highlight a faulty device photo |
| Image generator (via call) | Produce annotated “how‑to” screenshots (Through an external API) |
| Automations / memory | Schedule follow‑ups, remember VIP preferences across chats |
- Safety and Compliance – Enterprise accounts of OpenAI O3 Pro can turn off specific tools. The model also provides standard AI safety protections around jailbreaking and refusal (the AI agents refuse to answer questions that might affect your company).
- Pricing – As of June 2025, OpenAI O3 is priced at $2/M input tokens and $8/m output tokens. The company has also promised more discounts on pricing as soon as they make the model more efficient to run.
Customer Service Bottom-Line
- Seamless “agent + tool” hand‑offs—O3 can confirm a warranty date via API, compose a replacement‑approval email, and drop a calendar reminder for the human agent in one flow.
- Context fidelity—A 200 K window lets a multilingual bot keep the entire complaint history and shipping logs, and support knowledge‑base snippets in memory, reducing repetitive “please recap your issue” moments.
- Lower operational risk—Published safety metrics, enterprise compliance hooks, and deterministic tool permissions give CX leaders more transparent governance than most rival models.
- Balanced cost curve—While cheaper than Claude Opus 4 and similar‑class Gemini quotas, O3 still packs the reasoning power needed for tier‑2/3 resolution without blowing the budget.
OpenAI O3 is a capable SOTA model that can provide technical support and help your customer support workflows. It also has a long context window to support your docs and reason over them to answer complex customer questions. We’ve tried it on complex workflows that we see from our enterprise customers, and it can solve the problems in 1 to 3 minutes, a reply timeline that works for channels like email and chat.
This model is on par with Claude Opus 4.0, which we will explore in the next section.
What’s New in Claude Opus 4.0?
The new Claude 3.5 Sonnet became a favorite of technical support teams everywhere for its ability to code quickly. Most coding AI agents like Cursor and Lovable used the Sonnet model as the baseline.
Claude Opus 4.0, released recently, aims to improve this capability of Sonnet. It also introduces reasoning and deep research capabilities. The model is competent but focused on solving coding problems and technical support use-cases rather than general customer support.
The features you should look out for are:
- Agentic Problem Solving – Claude Opus 4.0 has reasoning capabilities and can solve complex L2 and L3 technical problems that need multiple API calls and KB lookups.
- Context and Output – Claude Opus 4.0 supports a long context window (200,000 tokens), but its output (32,000 tokens) is much shorter than OpenAI O3.
- Agentic Search – The agentic search from Anthropic lets AI agents call different functions, make API calls, and search external sources to find a solution to a customer problem. This enables it to solve complex technical bugs right out of the box.
- Safety and Alignment – Claude shows the best safety and alignment among all frontier AI models, with a robust constitutional AI framework where the AI model reviews answers before pushing them out to the customer.
- Pricing – Claude Opus 4.0 is available across AWS. GCP and Anthropic’s API. It currently costs $15/M input tokens and $75/M output tokens. This is significantly more expensive than the current O3 models.
Customer Service Bottom Line
- Context depth means no “please repeat your order number” loops—agents can see the entire conversation and metadata in one shot.
- Autonomous research lets the bot answer long‑tail, policy‑heavy questions without escalating to humans.
- Published safety level and Constitutional AI give your legal teams clearer guardrails. However, the premium token price makes Opus 4 best for high‑value, lower‑volume interactions or situations where context size is a hard requirement.
Opus 4.0 is a great model for powering AI agents for coding-based support tools. However, it is too expensive for repetitive L1 and L2 queries. It’s better to use a cheaper model for less complex use cases. It’s possible to create an AI agent that switches to Claude Opus 4.0 when the problem is too technical and complex.
However, the last time we reviewed the SOTA landscape, we crowned Gemini the best AI customer service model. Let’s see if it retains that status with their new Gemini 2.5 version.
What’s New in Gemini 2.5 Pro?
When the AI wars started with the release of ChatGPT 3.5, Google was criticized for inaction. However, with a flurry of releases in the Gemini model series, they’ve begun scoring highly in AI benchmarks. Add in the fact that Google’s homegrown TPUs are cheaper for inference, and you get reasoning models that are cheaper and more efficient.
Gemini 2.5 Pro also topped the benchmark charts and had the following features:
1. Longest Context Window – The Gemini 2.5 Pro has a 1 M long context window, eclipsing all other SOTA models. The AI also supports parallel “thinking” chains, providing more accurate and faster answers.
2. Native Multimodality and Video understanding – Gemini can understand videos and images natively. It can also answer customer questions in 24 languages.
3. Tool Calling – Like the other SOTA models, Gemini 2.5 Pro can also call different functions and gather data from various sources while answering customer questions.
4. Safety & Governance – While Gemini hasn’t posted its security updates, Vertex AI comes preloaded with GDPR compliance and other data protections.
5. Pricing – The current pricing for the Gemini 2.5 Pro model is $1.25/M tokens and $10/M tokens.
Customer Service Bottom Line
1. Omnichannel unification: one model can “watch” a customer’s unboxing video, “listen” to their complaint, read their live‑chat text, and respond coherently, reducing hand‑offs between specialised subsystems.
2. Voice‑first advantage: for brands building next‑gen IVR or multilingual concierge bots, Gemini’s native audio streaming beats rivals that need a separate TTS layer.
3. Future‑proof context scale: the forthcoming 2 M‑token window means entire product histories, warranty PDFs, and compliance manuals can live in‑prompt—no retrieval layer required for many use cases.
Our experience with Gemini has been very positive for general L1, L2, and L3 questions. It can solve more complex reasoning problems at a relatively low cost. Also, the model supports the setting of “thinking budgets,” which lets you keep costs low and efficient for enterprise use-cases with high customer question volumes.
Next, compare these features head-to-head and determine what they mean for customer service.
Which Model Should You Choose: Open AI O3 v/s Claude Opus 4.0 v/s Gemini 2.5 Pro?
We put these technical features of these models together to see which performs best in a customer service context.
| Scenario | Claude Opus 4 | Gemini 2.5 Pro | OpenAI O3 |
| Very long, multi‑step chats | ✅ 200 K context keeps whole ticket threads | ✅ 1 M context (→ 2 M) swallows years of transcripts | ✅ 200 K context—ample, but < Gemini |
| Autonomous research / agentic workflows | ✅ “Extended‑thinking” mode + Agentic Search | ✅ Parallel Deep Think chains + tool calls | ⚠ Needs explicit function/tool orchestration (well‑documented) |
| Voice or rich multimodal inputs | 🚫 Text + image only | ✅ Native voice & full multimodal | ⚠ Text + image native; voice via separate Realtime API, not in‑model |
| Strict safety / regulated industries | Solid policies, but fewer third‑party attestations | Safety card not yet public | ✅ Full safety card, jailbreak & hallucination data published |
| Cost‑sensitive high‑volume L1 chat | Highest raw price ( $15 / $75 ) | Mid‑high ( $1.25 / $10 ) — still TBD for ≥200 K context | ✅ Moderate ( $2 / $8 ) plus caching & assistants tooling |
Which Model Should You Use?
1. Claude Opus 4 dominates context‑heavy or self‑directed agent use cases (budget permitting).
2. Gemini 2.5 Pro is the clear pick for multimodal + real‑time voice experiences, but it faces some problems in terms of deployment.
3. OpenAI O3 is sophisticated and well-balanced for solving more complex L3 or L4 problems. However, the costs are much higher than those of the Gemini model.
Our experience says the Gemini 2.5 pro is a reliable model that can be used for enterprise use-cases. The costs make it a standout, and it also features sophisticated reasoning and tool calls that can help customer service teams solve more complex problems.
We also noticed some trends in developing these recent models, which we will address next.
How Will These Recent Models Affect Customer Service?
We have seen that most recent models are tackling more technical problems at scale. This is because of the undeniable popularity of AI agents like Cursor and Lovable, which have been very profitable.
For customer service, it’s important to choose models that can solve problems but are also cost-effective. Some things to note are:
1. Without “reasoning budgets,” enterprises can’t consistently predict customer service costs with these models. All three models have some form of budgeting that you can set up in the API calls when you build your AI agents.
2. While these models have better technical capabilities, they’re not helpful for everyday customer service in solving repetitive L1 and L2 questions. We recommend using a model like Gemini 2 or GPT 4-o-mini for that.
3. Technical support is now a real use case for AI agents. However, the context and code must be readily available for the AI agent to implement.
4. For primarily technical and complex questions with essential safety implications, it’s better to choose Claude Opus 4.0, which has the best alignment and safety features. However, given the costs, choosing a model that supports vendor switching is essential.
Depending on the results we listed above and these trends and implications, you can make a more judicious choice about the AI model that will power your customer service AI agents.
Conclusion
The 2025 AI landscape offers compelling options for customer service teams, each with distinct strengths. Gemini 2.5 Pro is the most versatile choice, combining cost-effectiveness with robust reasoning capabilities and native multimodal support, making it ideal for enterprises handling diverse customer interactions at scale. Claude Opus 4.0 excels in safety-critical scenarios and complex technical support but comes with premium pricing that limits its use to high-value interactions. OpenAI O3 Pro strikes a middle ground with strong reasoning capabilities and comprehensive tooling, though it requires more careful orchestration for agentic workflows.
The Key Takeaway
There’s no one-size-fits-all solution. Innovative customer service teams will implement a tiered approach—using cost-effective models like Gemini 2.5 Pro for routine L1/L2 queries while escalating complex technical issues to Claude Opus 4.0 or O3 Pro when the situation demands it. As these models evolve, the winners will be organizations that build flexible AI agent architectures capable of seamlessly switching between models based on query complexity, safety requirements, and cost considerations.
The future of AI-powered customer service isn’t about choosing the “best” model—it’s about choosing the right model for each specific customer need.
Want to use the latest AI Models for your Customer Service? Talk to Kommunicate!
A Content Marketing Manager at Kommunicate, Uttiya brings in 11+ years of experience across journalism, D2C and B2B tech. He’s excited by the evolution of AI technologies and is interested in how it influences the future of existing industries.
