How to choose an AI model for Telegram: test it with real prompts

When a Telegram AI bot offers several models, it is tempting to pick the one with the newest name, the largest specification, or the highest public ranking. But “most capable” does not automatically mean “best for this conversation.” Everyday questions may reward speed, file Q&A requires fidelity to the document, image tasks depend on visual understanding, and web-assisted answers must separate current facts from stale ones.

A more reliable approach is to run a small evaluation with questions you genuinely expect to ask. You do not need a laboratory or a mysterious composite score. The goal is simply to make the choice repeatable and explainable.

Define a good answer before choosing a model

OpenAI's official evaluation guide recommends defining the behavior you want and testing it on representative inputs. Anthropic similarly recommends success criteria that are specific, measurable, and tied to the actual task. In practical terms, replace “Which model is best?” with “What must a good answer do here?”

Start with five dimensions:

• Task correctness: Is the conclusion right, and are important steps present?
• Instruction following: Does the answer obey the requested language, format, length, and tone?
• Use of context: Does it use the conversation, image, web page, or file correctly instead of inventing missing details?
• Response experience: Is the wait appropriate for the task, and is the result ready to use?
• Failure boundaries: When information is missing or the task cannot be completed, does the model explain the limit and suggest a sensible next step?

For web-assisted questions, add evidence quality. Check whether cited sources actually support the claim and whether their dates are current enough. A link in an answer is not proof that the answer has been verified.

Build a 20-prompt test set from real work

The set can be small, but it should reflect your actual mix of tasks instead of showcasing only easy demos. NIST's AI measurement and evaluation material stresses that context is essential to interpreting results: the same metric can lead to a different conclusion in a different use case.

One practical 20-prompt set is:

1. Everyday Q&A and writing (4 prompts): summarization, rewriting, action items, and a short response with strict formatting.
2. Reasoning and planning (4 prompts): logic questions with known answers, a constrained plan, and a calculation that must show its checks.
3. Image understanding (3 prompts): a screenshot, chart, or object photo with no sensitive information and a known expected answer.
4. Web-assisted research (3 prompts): time-sensitive questions that require dates and verifiable sources.
5. File Q&A (3 prompts): one answer present in a test document, one absent from it, and one that requires combining separate passages.
6. Multilingual and format constraints (3 prompts): translation, a bilingual summary, and a response that must follow a specified list or JSON structure.

Prompts can come from real work, but attachments should be synthetic, public, or redacted. Never upload passwords, identity documents, unreleased contracts, customer lists, or other confidential data merely to compare models.

Keep the comparison fair

Give every candidate model the same prompt, attachment, and context. Keep controllable settings consistent. Do not give one model an entire document while giving another only a summary.

For each run, record at least:

• the model label shown in the interface and the test date;
• the original prompt, attachment, and required context;
• the complete response;
• approximate response time and any obvious error or refusal;
• a human score with a one-sentence reason.

You can score correctness, instruction following, context use, and immediate usefulness from 0 to 2. Define the levels before scoring. For file Q&A, for example, 2 might mean “the answer is correct and identifies the supporting location,” while 0 means “the response invents information that is not in the document.”

Avoid turning every result into one overall champion. Everyday chat, visual understanding, and complex reasoning are different tasks. A single average can hide the differences that matter most.

Include cases that expose failure boundaries

A set of only straightforward success cases will not reveal much about reliability. Anthropic recommends evaluations that resemble real tasks while including edge cases. NIST's draft TEVV-Athlon framework, released for comment in August 2026, likewise emphasizes adapting testing methods to the application being assessed.

Useful edge cases include:

• a question missing one critical condition, to see whether the model asks or guesses;
• a file that does not contain the requested answer;
• web sources that disagree, to test whether uncertainty is preserved;
• a long instruction with several formatting constraints;
• a blurry image or very small text that should trigger caution rather than confident invention.

High-impact outputs still require human review. A small evaluation can compare day-to-day usability, but it cannot establish that a model is reliable for medical, legal, financial, or safety-critical decisions.

Choose by task instead of forcing one model to do everything

The most useful outcome is often a task map, not a universal winner:

• use a faster, format-stable model for frequent low-risk questions;
• use the model that performed better on your reasoning set for difficult planning;
• select separately for image, web, and file tasks based on those test groups;
• keep a human verification step whenever an answer affects customers, money, or an important decision.

BearChat lets people switch among models in Telegram private chats and groups, with capabilities that include image understanding, web search, reasoning, and personal files. This test method can help you select a model manually for the task at hand; it does not imply that the product automatically routes each request to a model.

Rerun the test instead of treating the result as permanent

Models, versions, and service policies change. Keep the prompts, answers, dates, and scoring rubric. Rerun the same set after a meaningful model update, a change in your workflow, or a noticeable shift in answer quality. That makes it easier to distinguish a model change from a test-data or usage change.

A small evaluation grounded in real prompts is often more relevant than a public leaderboard to the questions you ask in Telegram every day. It will not make the decision for you, but it turns “this model feels better” into evidence you can revisit.

Sources

• OpenAI, Working with evals, accessed October 1, 2026.
• Anthropic, Define success criteria and build evaluations, accessed October 1, 2026.
• NIST, AI Measurement and Evaluation, accessed October 1, 2026.
• NIST, The TEVV-Athlon Framework for Evaluating AI Systems, draft comment period opened August 7, 2026; accessed October 1, 2026.