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Comparing AI models — who builds what and how to choose

Redaktion ·

Why choosing the right model matters

In 2026, building an AI feature is no longer about whether to use AI — it is about which model from which provider. The field has grown wide: seven to ten relevant model families, each with three to six tier levels, plus open-weights models from China, Europe and the US. Anyone who just says “GPT” or “Claude” typically leaves 30–70 % of performance on the table — either in cost, quality, or latency.

This article sorts the field. You get an overview of the major model families, their distinct traits, typical use cases — and at the end a decision logic that helps you pick a model for concrete tasks. If you want to go deeper on pricing, see the sister article AI Pricing Explained.

The market’s basic structure

Before looking at individual providers, it helps to understand the three axes that structure the field.

Closed vs. open-weights

Providers like Anthropic, OpenAI and Google release their models only via API — the weights (the actual parameters of the network) remain proprietary. You pay per token and get updates, SLAs, infrastructure.

Others — Meta with Llama, Mistral, Alibaba with Qwen, DeepSeek, Google with the Gemma family — publish weights under more or less permissive licenses. You can download them, self-host, fine-tune, embed. In return you carry the operational burden, hardware, and updates.

The choice is rarely purely technical: cost structure, data protection, vendor lock-in, and your own DevOps maturity decide. More on this in the lexikon article running local LLMs.

Tier levels — Frontier, Mid, Low

Practically every provider maintains three tiers following the same logic: frontier models (Opus, GPT-Top, Pro) for the hardest tasks, mid-tier (Sonnet, Mini, Flash) as the workhorse for most workloads, low-tier (Haiku, Nano, Flash-Lite) for classification, routing, and quick standard answers. The price gap between top and bottom is typically a factor of 30–60 — and on many tasks the quality difference does not matter at all.

Reasoning models as their own category

Since 2024 a second axis has emerged: models that internally “think” before producing the actual answer, generating visible or invisible thinking tokens. OpenAI calls this the o-series, Anthropic “Extended Thinking”, Google “Thinking Mode”, DeepSeek ships an R-variant. These models cost more and are slower, but they solve logic, math, and code problems more reliably.

The model families at a glance

Claude — Anthropic

In 2026 Anthropic releases the 4.X series in three tiers: Opus 4.7 as the frontier model, Sonnet 4.6 as the standard workhorse, Haiku 4.5 as low-tier. The context window is consistently 200k tokens, with a 1-million variant for Sonnet. Strengths: long coherent text, honest self-assessment, very strong tool use and coding. Weaknesses: no native image generation (vision input only), no built-in web browser.

Claude is often perceived as “the balanced all-rounder” and is particularly common in coding agents and writing workflows. Anthropic positions itself clearly around safety and alignment; in enterprise contexts, that is frequently a deciding factor.

GPT — OpenAI

OpenAI’s 2026 lineup is the GPT-5 series as the main line (5.3, 5.4, 5.5) plus the o reasoning models, with mini and nano variants for the low end. Strengths: deep multimodality (text, image, audio, video — generative as well), huge ecosystem, very mature function calling, embedded tool set (web browsing, code interpreter, image generation). Weaknesses: high volatility in pricing and model availability, less transparent on version transitions.

GPT is the natural choice when your use case requires multimodality or when you are building on top of the ChatGPT ecosystem (GPTs, Custom Actions).

Gemini — Google

Google consolidated the lineup with Gemini 3: Pro, Flash, Flash-Lite. Strengths: extreme context windows (1M+ tokens standard), seamless integration with Google services (Workspace, Search, Vertex), strong native video and audio understanding. Weaknesses: API ergonomics less polished than the US competitors, tool use historically not quite at Claude or GPT levels — improving with every release.

Gemini makes sense when your workflow processes long documents (PDF stacks, legal files, code bases), when you need native multimodality, or when you are already on Google Cloud.

Llama & Gemma — the open Western models

Meta releases the Llama series (currently 4.x with Scout and Maverick as the main variants) under a license that allows most production use cases. Google’s Gemma 4 is a smaller, fully open sister line. Both are the default choice when you want to self-host or fine-tune without binding yourself to a cloud vendor.

Strengths: full control, fine-tuning without restrictions, no token price, data-protection advantages. Weaknesses: you need GPUs, DevOps know-how, and top-end quality lags the closed frontier models by 6–12 months.

Mistral — the European answer

Paris-based Mistral AI runs two tracks: open models (Small, open-Mixtral variants) and proprietary top models (Mistral Large 3). Strengths: strong performance per parameter, European data residency, MoE (Mixture of Experts) architectures make the open models efficient. Weaknesses: smaller ecosystem than Llama, less community tooling.

Mistral is attractive for European companies that have to document GDPR compliance and EU data residency, or that politically prefer not to depend on US cloud providers.

DeepSeek, Qwen, Kimi, GLM — the Chinese wave

Since 2024 the second major source of open-weights models has been China. Four families are relevant in 2026: DeepSeek V4 (reasoning-strong, MoE), Qwen 3.5 from Alibaba (very strong in non-English languages), Kimi K2 from Moonshot AI (extreme context lengths), and GLM 5.1 from Zhipu AI (multimodal, very cheap).

Strengths: top performance at a fraction of Western prices, open weights under liberal licenses, fast innovation cycles. Weaknesses: geopolitical sensitivities, compliance questions depending on industry, sometimes built-in content filters for China-sensitive topics.

For applications without politically sensitive inputs and without compliance hurdles, these models cannot be ignored in 2026 — especially for cost-sensitive workloads.

Grok — xAI

xAI’s Grok-4 has reached the frontier league in 2026, but its tight integration with X (Twitter) gives it a distinct niche: real-time information from the X feed where other models are blind. Outside that use case, Grok offers little that Claude or GPT do not also cover.

Which model for which task?

Instead of a single “best” recommendation — which simply does not exist — it pays to decide per workload type. The table below shows typical recommendations for 2026, without claiming completeness.

| Task | First choice | Reasonable alternative | Why | |---|---|---|---| | General chatbot, medium complexity | Claude Sonnet 4.6 | GPT-5.5-mini | Best balance of quality and price | | Coding agent | Claude Opus 4.7 or Sonnet 4.6 | GPT-5.3-Codex | Strongest tool use, best code quality | | Classification at scale | Claude Haiku 4.5 or GPT-Nano | DeepSeek V4 | Lowest price, accurate enough | | Long-document analysis | Gemini 3 Pro | Claude Sonnet (1M) | Largest context windows | | Multimodal apps (image + text) | GPT-5.5 | Gemini 3 Pro | Best image generation and understanding | | Reasoning, math, logic | OpenAI o-series | Claude Opus 4.7 (Extended Thinking) | Top reasoning benchmarks | | Self-hosted, on-premise | Llama 4 Maverick | Mistral Large 3 | Best performance with full control | | Local on consumer hardware | Qwen 3.5 (smaller variants) | Gemma 4 | Optimized for RAM/VRAM | | Cost-sensitive volume workloads | DeepSeek V4 | Qwen 3.5 | Best price-performance ratio | | GDPR-strict, EU data residency | Mistral Large 3 | Aleph Alpha / local Llama | European location, documented |

Three typical selection scenarios

Scenario 1: B2B SaaS rolling out its first AI feature

A mid-sized B2B SaaS vendor wants to add a summarization feature — 5,000 summaries per month, 2–4 pages of input, output around 200 words.

Recommendation: Claude Sonnet 4.6 as the main model. Reason: high quality on longer texts, strong language understanding across European languages, mature prompt caching, and Anthropic offers EU hosting via AWS Bedrock. Plan B: Mistral Large 3 if GDPR compliance has to be documented especially strictly.

Scenario 2: E-commerce shop generating product descriptions

40,000 products are to be automatically populated, one-off, with SEO-optimized descriptions of 150–250 words each.

Recommendation: DeepSeek V4 via Batch API. Reason: throughput matters more than the last few percentage points of quality, the price advantage is substantial (factor of 5–10 over Sonnet), and Batch API halves it again. Plan B: Claude Haiku 4.5 if Western hosting is a compliance requirement.

Scenario 3: Pharma company building an internal RAG system for studies

20,000 study PDFs are to be made searchable for internal staff, with precise source citation, 100 queries per day, data protection absolutely central.

Recommendation: Self-hosted Llama 4 Maverick on dedicated GPU infrastructure. Reason: studies must not leave the infrastructure, the query volume justifies dedicated hardware, and Llama 4 is now competitive on retrieval-augmented tasks. Plan B: Mistral Large 3 on a dedicated EU cloud, if no in-house data center is available.

Three common mistakes in model selection

Mistake 1: Frontier model for everything. Using Opus 4.7 for every classification burns money for no reason. Rule of thumb: measure first, then pick the cheapest model that clears the quality bar.

Mistake 2: Skipping your own benchmark setup. Twitter rankings are no substitute for tests on your own data. One hour spent on a mini-benchmark with 30 real queries often saves months of bad decisions. More on this in the lexikon article measuring LLM quality.

Mistake 3: Underestimating lock-in. Building a feature that only works with OpenAI-specific quirks leaves you stuck when prices rise or quality drops. Abstract the model layer — modern SDKs and frameworks (LiteLLM, LangChain, OpenRouter) make this easy.

Conclusion

The AI model landscape in 2026 is mature enough that the choice deserves deliberate thought — and dynamic enough that the choice should be re-evaluated every 3–6 months. Pragmatic teams combine several providers and tiers in a single application: a small model up front as router or classifier, a mid-tier as the workhorse, a frontier model as fallback for the hard 5 % of requests.

The most important recommendation: start with the mid-tier of an established provider (Claude Sonnet, GPT-5.5-mini, Gemini 3 Flash), measure actual quality on your data, then optimize for cost and latency. Anyone jumping straight to the frontier league or to self-hosting usually optimizes for the wrong target.

For the next step, look at AI Pricing (what the options actually cost) and at the AI coding tools comparison if your use case heads in the direction of developer productivity.

Themenuebersicht

KI-ModelleClaudeGPTGeminiLlamaMistralDeepSeekQwen