How to Choose an AI Vendor in 2026: Anthropic vs OpenAI vs Google vs Mistral vs Manus — An Enterprise Scorecard

In the last week of September three frontier models — Claude Sonnet 5.5, GPT-6.1 Sol and Gemini 4 Argon — launched at the same price: $2 per million input tokens and $10 per million output tokens. When price stops being the tiebreaker, choosing an AI vendor becomes a real procurement decision: quality on your own tasks, security, data residency, integration, contract terms and the cost of switching later. This guide gives you a weighted scorecard, the deployment options of each vendor, the contract clauses that matter and a 30-day selection process.

Why vendor choice changed in 2026

Three years ago the choice was simple: OpenAI had the best model and everyone else was catching up. That market no longer exists. Menlo Ventures' enterprise surveys show OpenAI's share of enterprise LLM API spend falling from about 50% in 2023 to 27% at the end of 2025, while Anthropic rose from 12% to 40% and Google from 7% to 21%. In coding — the largest single enterprise use case — Anthropic's share was higher still. Leadership on benchmarks now changes hands every few weeks: Opus 5.5 set a new SWE-bench Pro record on 22 September, and eight days later Gemini 4 Argon claimed the lead on most of Google's own disclosed tests.

Four forces make vendor selection a different exercise from a year ago:

• Price convergence. At the mid-to-frontier tier the list prices are identical, so total cost depends on how many tokens a model needs for your task, caching and batch discounts — not on the price sheet. See our guide to model routing and cost optimisation.
• Gated frontier. "Released" no longer means "available to you". Claude Mythos 5.1, Gemini 4 Argon and OpenAI's most cyber-capable models sit behind trusted-access programmes, so the model you evaluate may not be the model you can buy.
• Behaviour, not only capability. OpenAI's decision to shelve GPT-6.1 Astra after it acted beyond its authorised scope showed that how a model behaves as an agent — staying in scope, reporting truthfully — is now a selection criterion in its own right.
• Interoperability. Anthropic, Google and OpenAI all support the Model Context Protocol. An integration built once for your systems can serve several vendors, which lowers the cost of switching and makes a multi-vendor strategy realistic.

The six criteria — and how to weight them

A good selection scores every vendor on the same criteria with weights agreed before anyone runs a demo. These are the weights we use as a starting point with clients; adjust them to your risk profile.

The vendors at a glance

Anthropic (Claude)

The enterprise API leader. Claude Opus 5.5 and Sonnet 5.5 are the strongest choice for coding, agents and long documents (1M-token context), and Claude runs natively in AWS Bedrock, Google Cloud Vertex AI and Microsoft Azure, inside Excel, Word and PowerPoint, and in Microsoft 365 Copilot. Its safety posture — Enterprise Frontier Safeguards with zero data retention, staged access for Mythos — appeals to regulated industries. Weak spots: the top Opus tier is more expensive than mid-tier rivals, and there are no open weights for self-hosting.

OpenAI (GPT, ChatGPT, Codex)

The broadest ecosystem. ChatGPT reaches roughly 1.2 billion weekly users, so adoption inside your company is often already happening. DevDay 2026 turned ChatGPT into an agent platform with persistent Dots agents, an enterprise app marketplace and Space for shared work. GPT-6.1 Sol brings near-frontier coding at the mid-tier price, Azure OpenAI gives Microsoft shops a familiar procurement path, and the gpt-oss open-weight models cover some self-hosting needs. Watch the agent governance story after Astra.

Google (Gemini)

The strongest choice for Google Workspace companies and multimodal work — video, images and very long outputs (Gemini 4 Argon raised the output limit to 1M tokens). Vertex AI offers EU regions and mature data-governance controls, and Google's balance sheet makes it the safest bet on vendor viability. Gemini is not available natively on AWS or Azure, and its newest flagship is initially limited to selected testers.

Mistral (Le Chat, Mistral models)

The European option. Mistral's models run on its own EU infrastructure, on all three hyperscalers and — thanks to open weights — on your own servers. It is usually the lowest-cost option for high-volume tasks such as classification, extraction and translation, and Le Chat offers 65+ connectors for an EU-hosted assistant. On raw capability it trails the US frontier, though Mistral says its next generation will close much of the gap. For sovereignty-driven buyers it is often the right primary vendor; for others, a strong second one.

Manus

Manus is an autonomous agent product rather than a model platform. It is excellent at turning a goal into a finished research report, spreadsheet or deck, and useful for teams that want results without building anything. Treat it as a tool you buy for a job, not as the foundation of your AI stack: there is no self-hosting, limited data-residency control and you depend on the underlying models it chooses.

The scorecard

Applying the starting weights to our editorial scores (1–5 per criterion, October 2026) puts Anthropic, OpenAI and Google within two points of each other. That is the real lesson: among the big three the decision is made by your weights and your own test results, not by the vendor. Switch to a sovereignty-first weighting (30% data residency, 20% quality) and Mistral draws level with the US leaders.

Where you can run each vendor

Deployment channels decide more selections than benchmarks do. If all your data lives in AWS, a model available in Bedrock inherits your existing security review, billing and network controls; if you must keep data on your own servers, only open-weight models qualify.

The contract clauses that matter

Model quality changes every month; your contract lasts for years. Before signing, make sure the agreement covers:

1. No training on your data — inputs, outputs and files — written into the contract, not only in a policy page.
2. Data processing agreement and subprocessor list, including which cloud regions process your data and who is notified when that changes.
3. Retention: zero data retention for sensitive workloads, or a defined maximum (for example 30 days for abuse monitoring).
4. SLA and rate limits: uptime, guaranteed throughput for your peak load and what happens on overload.
5. Model deprecation notice: how long a model version you have validated stays available — ask for at least six months for production workloads.
6. Price-change protection for committed spend, and the right to move committed spend to newer models.
7. IP indemnity for generated outputs and clear ownership of outputs and fine-tuned artefacts.
8. Security and incident terms: certifications, penetration testing, breach notification within a fixed number of hours.
9. EU AI Act cooperation: the documentation you need as a deployer, and how the provider supports transparency and the 2 December 2026 marking obligation — see our EU AI Act guide.
10. Exit: export of conversations, files and configurations, and certified deletion at the end of the contract.

One vendor or several?

For employee assistants, standardise. Two competing chat tools double training, licence and governance costs and confuse users. For API workloads, the opposite is true: keep at least two vendors qualified. A primary model handles most traffic, a second one is tested on the same evaluation set and ready to take over if prices, quality or availability change — or to handle the tasks where it is simply better.

Three architectural choices keep that flexibility cheap: a thin gateway in front of all model calls (logging, routing, cost control), MCP-based integrations instead of vendor-specific plugins, and a portable evaluation set that you rerun whenever a new model ships. With those in place, switching models is a configuration change, not a project.

A 30-day selection process

The bottom line

In 2026 every serious AI vendor is good enough for most business tasks, and the leading ones cost the same per token. The winners in procurement will not be the companies that pick the "best" model, but the ones that pick deliberately: clear weights, tests on their own data, contracts that protect them when the market moves, and an architecture that lets them switch. For most companies in Croatia and the DACH region that means one primary vendor — very often Claude or GPT, with Gemini for Workspace shops and Mistral where sovereignty dominates — plus a qualified second vendor and specialist tools like Manus where they earn their place.