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 (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 use under 152-FZ or RU-only?

GigaChat (Sber) or YandexGPT — both are hosted in Russia and fall under 152-FZ. An alternative is self-hosted open-source (Qwen or Llama 3 in an RF data center). Claude / GPT / Gemini are not a fit for strict 152-FZ: data leaves to EU/US clouds.

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?

152-FZ or RU-only is a hard constraint that overrides every other answer. If you have no such requirement, choose "any country."

4. Which language will dominate your requests?

If your focus is code, choose "code only": for code agents Russian/English 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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