Which LLM is best for agentic (tool-use) tasks in 2026?

Anthropic's Claude 4.7. On the TAU-bench and OSWorld benchmarks it consistently leads on tool-use and long agentic loops. GPT-5 is close on simple tool calls, but Claude wins on multi-step scenarios with MCP servers.

Which model is cheapest for high-volume tasks?

On simple chat and summarization the leaders are GPT-5-mini, Claude Haiku 4.5 and Gemini 3 Flash Preview. For Russian-language tasks GigaChat 2 Lite is competitive (flat ruble pricing 65 ₽/1M tokens, no currency conversion). On complex agentic tasks cheap models lose — total cost is higher due to extra retry loops.

Which model should I pick when data must stay inside a specific region?

Pick a local provider whose data centers sit in the region you need, or a self-hosted open-source model (Qwen, Llama 3) on local infrastructure. Claude / GPT / Gemini only fit if the chosen cloud has a suitable regional endpoint and a DPA.

Which model handles long context (1M+ tokens) best?

Gemini 3 leads on long-context (1M-2M tokens with near-100% recall on RULER). Claude 4.7 holds 200k-500k at high quality. GPT-5 is catching up, but it trails on NIAH 1M benchmarks. For single-pass analysis of large documents, use Gemini.

Can I mix models inside a single agent?

Yes — and you probably should. A typical pattern: an expensive model (Claude or GPT-5) for planning and tool selection, a cheap one (Haiku, GPT-mini, Flash) for high-volume sub-tasks (classification, translation, summarization). With OpenRouter or an MCP router this is managed in a single codebase.

CONFIGURATOR · 60 SECONDS

Claude, GPT, Gemini or GigaChat — which model to use?

4 questions about your task, budget, data residency and language — we recommend a model and email a one-page cheat sheet with pricing and quickstart.

0 of 4 · select your answers

1. What will be the model's primary task type?

If there are several tasks, pick the heaviest or most critical one. You can still assign a cheap model to light sub-tasks separately.

2. Target monthly token budget?

Model API spend only (no salaries or infrastructure). If unsure, take expected MAU × 50–100 requests × your average prompt size in tokens.

3. Where must data be stored during processing?

If you must keep data within a specific region/country (compliance, corporate policy), pick the matching option. This is a hard constraint that overrides every other answer.

4. Which language will dominate your requests?

If your focus is code, choose "code only": for code agents the natural language doesn't matter — syntax quality does.

No sign-up No vendor bias Based on 2026 benchmarks

The configurator draws on the LeadUp AI cornerstone comparison Claude 4.7 vs GPT-5 vs Gemini 3, a Q2 2026 pricing snapshot and LeadUp's experience with production agents. Your actual choice depends on your real workload — confirm the recommendation in a free diagnostic.

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Reference pricing (Q2 2026, $/1M tokens)

Prices are current as of publication; before a production deploy, check the model's official pricing page.

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One A4 page: pricing, top-3 benchmarks, 5-step quickstart, 3 anti-patterns.

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