Claude, GPT, or Gemini, and open models: how to choose for a specific task
Three well-known families of closed models and the open models that cover the same tasks in Yenisei. A practical scenario-by-scenario breakdown, and how not to overpay.
Yenisei team
Claude, GPT, or Gemini, and open models: how to choose for a specific task
Anthropic's Claude, OpenAI's GPT, and Google's Gemini are the three best-known families of closed models. They are not in the Yenisei catalog: we work with open-weight models and our own Yenisei lineup. But the tasks people choose Claude, GPT, or Gemini for are covered by open models; let's go through them scenario by scenario.
A short map
- Claude is valued for careful step-by-step reasoning, good Russian, code, and agents.
- GPT for a broad lineup across budgets, reliable tool calling, and a separate reasoning branch.
- Gemini for working with images, audio, and video, and for the million-token context.
By scenario
A long document that needs careful analysis
DeepSeek V4 Pro (deepseek-pro) or GLM 5.3 (glm-5.3): a context of about a million tokens and reasoning. If the document has diagrams and pictures, Kimi K3 (kimi-k3) or Yenisei Max (yenisei-max).
Video or audio as input
MiMo v2.5 (mimo-v2.5) understands both. For video and images, Qwen3.8 Flash (qwen-flash) also works; for audio alone, Voxtral Small 24B (voxtral-small).
Hard logic, math, planning
Kimi K2 Thinking (kimi-k2-thinking) or GPT-OSS 120B (gpt-oss-120b), OpenAI's open model with reasoning. Start with GPT-OSS 120B and move to Kimi K2 Thinking if the task is harder.
Coding assistant, long chains of tool calls
Kimi K2.7 Code (kimi-k2.7-code) or Qwen3 Coder Next (qwen3-coder-next); from our own lineup, Yenisei Code (yenisei-code).
A high-volume stream of cheap requests
Mistral Nemo (mistral-nemo), GPT-OSS 20B (gpt-oss-20b), or Gemma 4 26B (gemma-4-26b). All are fast and inexpensive; choose by what works best on your data.
Russian-language text for people
Try DeepSeek V4 Pro, Qwen3.8 27B (qwen3.8-27b), and Yenisei Max on your own style and pick the closest one.
A practical method
- Don't pick the flagship by default. Start with a mid-size model.
- Measure on your own data. General leaderboards won't tell you how a model handles your specific task.
- Move up only on failure, where the mid-size model actually makes mistakes.
- Move down where you can. If the task is simple and there is a lot of it, move it to a light model and save.
All models through one endpoint
curl https://api.yenisei.ru/v1/chat/completions \ -H "Authorization: Bearer $YENISEI_KEY" \ -H "Content-Type: application/json" \ -d '{"model": "deepseek-pro", "messages": [{"role": "user", "content": "Hello"}]}'Changing model is all it takes to switch between models. You can compare them directly on your own task, without different SDKs and different bills. Payment is in rubles, 1 credit = 1 ₽, so the budget is counted directly, with no exchange-rate conversion.
There is no best model for every case. Take the one that solves your task at an acceptable level for the least money, and keep a larger one on hand for when the task turns out to be hard.