Claude vs GPT vs Gemini vs Open-Source: Choosing an LLM for Your Product

"Which model should we use?" is often the first technical question in an AI project, and the one with the shortest shelf life. Model families release new versions every few months. Leaderboards reshuffle. Prices drop. A comparison article written six months ago is usually out of date.

So instead of a ranking that will age badly, this guide gives you a durable way to choose — the criteria that matter, how the main model families tend to differ, and the selection process we use with clients.

What are the main LLM options in 2026?

Each family offers models at several tiers — a frontier model for hard reasoning, a mid-tier workhorse and a small, fast, cheap model. The tier often matters more than the family. A small model from any provider may be the right choice for classification; a frontier model may be necessary for complex analysis.

Strengths shift with every release, so treat the table as a guide to where to start testing, not a verdict.

Which criteria should decide your LLM choice?

1. Quality on your tasks

The only benchmark that truly matters is your own. Public leaderboards measure general skills; your product needs specific ones — extracting fields from your invoices, answering from your documents, following your format. Build an evaluation set of real examples and score candidates on it. See how to evaluate LLM applications.

2. Cost per successful task

Price per million tokens is misleading on its own. A cheaper model that needs longer prompts, more retries or more agent steps can cost more per completed task. Measure total cost per successful outcome on your evaluation set. Caching and batch discounts also differ by provider — see how to cut LLM API costs.

3. Latency

For chat and voice, time to first token and total response time matter as much as quality. Voice agents need very fast responses; overnight batch jobs don't care. Test latency from the region where your users are.

4. Context length and long-document behaviour

If you process long contracts, codebases or transcripts, check both the maximum context size and how reliably the model uses information deep inside it. Larger windows aren't automatically better; test with your documents. Compare with RAG and other approaches.

5. Tool use and structured output

Agents depend on a model choosing the right tool with valid arguments, and on reliably producing JSON that matches a schema. Models vary noticeably here. Include tool-calling scenarios in your evaluation.

6. Multimodal needs

Does your product need images, scanned documents, audio, video or realtime speech? Support and quality differ by family and tier.

7. Data privacy, residency and compliance

This can rule models in or out before quality is even tested:

• Data retention and training terms for API data
• Regional processing — for example, EU or US data residency
• Cloud availability — Claude through AWS Bedrock or Google Vertex AI, GPT through Azure OpenAI, Gemini through Vertex AI — which may fit existing agreements and compliance approvals
• Contracts such as data processing agreements and, for US healthcare, business associate agreements
• Self-hosting with open-weight models when data can't leave your environment

8. Reliability and rate limits

Check uptime history, rate limits at your expected volume, and whether you can get higher limits or provisioned capacity. Plan for a fallback provider either way.

9. Ecosystem and lock-in

Proprietary features (specific agent frameworks, hosted vector stores, assistants APIs) can speed up development and make switching harder. Use them deliberately.

When do open-weight models make sense?

Consider open-weight models when:

• Data must stay in your environment (on-premises, a specific cloud region, air-gapped)
• You need extensive fine-tuning or full control over model behaviour
• Volume is high and steady, so dedicated GPUs stay busy
• The task is narrow enough for a smaller model to match frontier quality

Be realistic about the costs: GPU infrastructure, scaling, monitoring and upgrades require engineering effort. Managed hosting providers for open models are a useful middle ground.

How do you run an LLM selection process?

1. Define the tasks. List each distinct AI step in your product (classify, retrieve and answer, extract, plan, write).
2. Apply hard constraints. Remove models that fail on data residency, compliance, deployment or modality requirements.
3. Shortlist two or three models per task across families and tiers.
4. Build an evaluation set of 50–200 real examples per task.
5. Run and score each candidate on quality, cost per successful task and latency.
6. Test failure modes: rate limits, malformed output, adversarial inputs.
7. Choose per task, not per product. Use the cheapest model that meets the quality bar for each step.
8. Pick a fallback model from a different provider for critical paths.
9. Re-evaluate with each major model release.

How do you avoid LLM vendor lock-in?

• Abstract model calls behind an internal interface so swapping a model is a configuration change
• Keep prompts, evaluation sets and traces in your own systems
• Prefer open standards such as MCP for tool integrations and OpenTelemetry for tracing
• Use multi-provider routing with failover for resilience
• Avoid building core logic on one provider's proprietary hosted features unless the benefit is clear

What does a typical multi-model setup look like?

A common production pattern:

This typically costs much less than using a frontier model everywhere, with no loss in quality where it matters.

How we choose models at Keyved

We're model-agnostic by design. Our platform foundation routes requests across providers including Anthropic, OpenAI and Google, with automatic failover when one is throttled or unavailable. For each project we run candidates against an evaluation set built from your data, and recommend a model per task based on quality, cost and latency — then re-test as new models ship.

If you're choosing a model for a new product, or wondering if you're overpaying for the one you have, talk to our engineers. See also our MVP and prototyping service and projects.