The 12 best LLMs in 2026: comparing Claude, ChatGPT, Gemini, DeepSeek, and Grok

The world of language models in 2026 resembles a hypermarket with a massive dairy section: forty-seven types of yogurt, all looking similar, and you've been standing in front of the shelf for six minutes. The difference is that what's at stake isn't breakfast, but code quality, analytics speed, and team working time. An author from Habr took 12 current models and compared them honestly — with benchmarks and real-world scenarios, without marketing promises.

The review covered three categories. The first — proprietary flagships: ChatGPT 5.4 and ChatGPT 5.

4 Pro from OpenAI, Claude Opus 4.7 and Claude Sonnet 4.6 from Anthropic, Gemini 3.

1 Pro from Google, and Grok 4.20 from xAI. The second — specialized tools: the BotHub aggregator and the Perplexity Sonar search model.

The third — open or conditionally-open solutions: DeepSeek v3.2, Gemma 4 26B A4B, and GPT-OSS-120B. ChatGPT 5.

4 Pro and Claude Opus 4.7 expectedly proved strongest in deep analysis and complex code tasks. The difference in approaches: GPT-5.

4 Pro wins in structured scenarios — function calling, agent chains, tool usage. Claude Opus 4.7 excels elsewhere — narrative in long texts becomes more cohesive, and context of 100k+ tokens holds up without quality degradation closer to the end.

Gemini 3.1 Pro stands out with native multimodality: documents, images, and code are processed in one window, without unnecessary API jumps between services. In the mid-price category, Claude Sonnet 4.

6 remains the workhorse for most tasks — speed is higher, price is lower than the flagship, code quality is sufficient for 80% of production scenarios. Grok 4.20 is interesting for data freshness (xAI has minimal lag from real time) and the absence of restrictions where other models start getting nervous about content filters.

A real surprise among budget options was DeepSeek v3.2. At a price significantly lower than American flagships, it shows results comparable to Sonnet 4.

6 on coding and analysis tasks — especially in the Chinese-language domain. Gemma 4 26B A4B from Google suits local deployment: the mixture-of-experts architecture allows fitting into reasonable hardware without cloud expenses. GPT-OSS-120B — the largest open model in the review — is still most interesting as a benchmark for those building vertical products who want to precisely understand the ceiling of open source.

Perplexity Sonar occupies a separate niche: it's not a pure chatbot, but a search model with live internet inside. Where others answer from training weights, Sonar actually searches and cites sources. BotHub, conversely, plays the role of aggregator — a single interface for accessing a dozen models with Russian payment, which in current conditions is itself a key function.

The article's main conclusion isn't about which model is best — the right answer always depends on the task. For daily code work, Sonnet 4.6 or Gemini 3 Flash provide the best balance of speed and cost.

For deep research and agent systems — Opus 4.7 or ChatGPT 5.4 Pro.

For budget savings without catastrophic quality loss — DeepSeek v3.2. The LLM market in 2026 has finally matured to the point where model selection is not a lottery, but an engineering decision with clear trade-offs.