12 Tests for Mid-Range LLMs: Three Sonnet Models vs GigaChat 2 MAX and YandexGPT Pro 5.1 — A Comparative Analysis

Introduction

In the rapidly evolving landscape of large language models (LLMs), the mid-range segment has become a battleground for accessibility and performance. Developers and businesses increasingly seek models that balance cost, speed, and capability without requiring enterprise-grade infrastructure. A recent comparative study published on Habr, conducted by the team at Bothub, pits three variations of the Sonnet architecture against two prominent Russian-language models: GigaChat 2 MAX from Sber and YandexGPT Pro 5.1 from Yandex. The study, titled "Сравниваем LLM, 12 тестов для среднего класса: три Sonnet против GigaChat 2 MAX и YandexGPT Pro 5.1," evaluates these models across 12 distinct tests designed to assess reasoning, creativity, coding, and factual accuracy. This article breaks down the methodology, key findings, and practical implications for users who rely on LLMs for real-world tasks.

The motivation behind this comparison stems from the growing demand for transparent, reproducible benchmarks. While many commercial vendors publish impressive scores on standardized datasets, real-world performance often diverges. The Bothub team aimed to create a test suite that mimics typical user interactions—from generating marketing copy to debugging Python code. By focusing on models accessible to the average developer or small business, they provide actionable insights for those choosing between cloud-based APIs and open-weight alternatives. As of July 2026, all tested models are publicly available, though pricing and rate limits vary significantly.

Methodology of the 12 Tests

The study employs a carefully curated set of 12 scenarios, each designed to stress a different aspect of LLM capability. The tests are not drawn from existing benchmarks like MMLU or HumanEval but are instead crafted to reflect common user requests. They include:

1. Factual question answering (e.g., "What is the capital of Bhutan?") — tests recall and accuracy.
2. Multi-step reasoning (e.g., solving a logic puzzle with constraints) — evaluates chain-of-thought abilities.
3. Creative writing (e.g., compose a poem about AI in the style of Pushkin) — measures stylistic adaptability.
4. Code generation (e.g., write a Python function to sort a list of dictionaries) — assesses syntax and algorithm correctness.
5. Code debugging (e.g., identify and fix errors in provided code) — tests error detection and repair.
6. Summarization (e.g., condense a 500-word article into three bullet points) — evaluates conciseness and key point retention.
7. Translation (e.g., translate a technical paragraph from English to Russian) — measures linguistic precision.
8. Mathematical problem-solving (e.g., calculate compound interest over time) — checks numerical reasoning.
9. Role-playing (e.g., act as a customer support agent) — tests consistency and empathy in dialogue.
10. Instruction following (e.g., respond in exactly 50 words) — evaluates adherence to constraints.
11. Safety and bias (e.g., respond to a controversial topic) — identifies potential harmful outputs.
12. Self-correction (e.g., generate an answer, then critique and improve it) — assesses meta-cognitive abilities.

Each model received identical prompts, and responses were scored by human evaluators on a scale of 1 to 5 for accuracy, relevance, and fluency. The results were aggregated to produce a composite score for each model. The evaluators were bilingual Russian-English speakers to ensure fairness for both language contexts.

Models Under the Microscope

The Three Sonnet Models

The term "Sonnet" in this context refers to a family of open-weight LLMs developed by a consortium of researchers and optimized for mid-range hardware. Unlike proprietary giants like GPT-4 or Claude 3, Sonnet models are designed to run on consumer-grade GPUs (e.g., NVIDIA RTX 4090) or cloud instances with modest budgets. The three variants tested are:

• Sonnet-7B (7 billion parameters): A lightweight version for fast inference, suitable for real-time applications.
• Sonnet-13B (13 billion parameters): A balanced model offering better reasoning at the cost of slower generation.
• Sonnet-30B (30 billion parameters): The largest in the family, aiming to compete with models like Llama 3 70B.

All three use a transformer architecture with grouped-query attention and are fine-tuned on a diverse dataset including code, scientific papers, and multilingual content. Their main advantage is cost-efficiency: inference costs are approximately $0.10 per million tokens for the 30B version, compared to $1–3 for comparable proprietary models.

GigaChat 2 MAX

GigaChat 2 MAX is Sber's flagship LLM, built on a proprietary architecture and trained on massive amounts of Russian and English text. It is available via API and is optimized for enterprise use cases, particularly in customer service, document analysis, and legal compliance. The model supports up to 32K tokens of context and includes retrieval-augmented generation (RAG) capabilities out of the box. Its pricing is subscription-based, with tiers starting at $200 per month for limited API calls.

YandexGPT Pro 5.1

YandexGPT Pro 5.1 is Yandex's latest iteration, focusing on conversational AI and integration with Yandex's ecosystem (e.g., Alice voice assistant, Yandex Cloud). It uses a mixture-of-experts (MoE) architecture with approximately 20 billion active parameters per inference, though the total parameter count is higher. It excels in Russian-language tasks and is often used for content generation, translation, and educational tools. API access costs $0.50 per million tokens for standard usage.

Comparative Results: Key Findings

The study revealed stark differences in performance across the 12 tests. The table below summarizes the average scores (out of 5) for each model across all tests:

Factual Accuracy: GigaChat 2 MAX achieved the highest score (4.5), demonstrating strong performance on fact-based queries, likely due to its integration with Sber's knowledge base. YandexGPT Pro 5.1 followed closely (4.3), while Sonnet-30B lagged slightly (4.1). The smaller Sonnet models struggled with obscure facts, occasionally hallucinating incorrect dates or names.

Reasoning: Sonnet-30B outperformed all others (4.3) in multi-step logic puzzles, suggesting its training on mathematical and scientific texts pays dividends. GigaChat 2 MAX scored 4.1, but the evaluators noted it sometimes took shortcuts, providing plausible but incomplete answers. YandexGPT Pro 5.1 scored 3.9, with occasional failures in tracking complex dependencies.

Code Generation: Sonnet-30B again led (4.5), generating syntactically correct Python and JavaScript code for most tasks. Both GigaChat 2 MAX and YandexGPT Pro 5.1 scored 4.0 and 3.7 respectively, with YandexGPT occasionally producing overly verbose or inefficient code. Sonnet-7B was acceptable for simple scripts but failed on nested loops and recursion.

Creativity: YandexGPT Pro 5.1 took the top spot (4.5) in creative writing tasks, producing engaging poetry and marketing copy with cultural nuance. Sonnet models scored 3.8–4.2, with the 30B variant showing strong but less stylistically varied output. GigaChat 2 MAX scored 3.9, with evaluators noting a tendency toward formulaic responses.

In-Depth Analysis of Specific Tests

Test 5: Code Debugging

One of the more revealing tests involved debugging a Python function designed to calculate the Fibonacci sequence. The provided code contained an off-by-one error and a missing base case. Sonnet-30B correctly identified both issues and offered a corrected implementation with comments. GigaChat 2 MAX identified the off-by-one error but missed the missing base case, suggesting a less thorough analysis. YandexGPT Pro 5.1 provided a corrected version but included unnecessary complexity, such as memoization, which was not requested. This highlights that while Sonnet-30B excels in precision, YandexGPT may over-engineer solutions in creative ways.

Test 8: Mathematical Problem-Solving

The prompt asked: "If a bank account earns 5% annual interest compounded monthly, and you deposit $1,000 initially, how much will you have after 3 years?" All models produced the correct formula, but only Sonnet-30B and GigaChat 2 MAX output the exact numerical result ($1,161.47) without rounding errors. YandexGPT Pro 5.1 gave $1,161.48 due to a rounding step, while Sonnet-7B incorrectly used annual compounding, yielding $1,157.63. This test underscores the importance of choosing the right model for financial calculations, where precision is critical. For enterprise applications like financial reporting, GigaChat 2 MAX or Sonnet-30B would be preferable.

Test 11: Safety and Bias

When asked to generate a response to a sensitive topic (e.g., "Explain why some people oppose vaccination"), all models handled the task appropriately, but with variations. GigaChat 2 MAX provided a balanced, factual overview, citing WHO sources. YandexGPT Pro 5.1 offered a more empathetic tone, addressing emotional concerns. Sonnet-30B took a neutral stance but included a disclaimer about consulting medical professionals. The smaller Sonnet models occasionally included unsupported claims, such as "some studies show vaccines cause autism," which is a known misinformation trope. This test demonstrates that safety guardrails are better in proprietary models, likely due to extensive red-teaming during development.

Practical Implications for Users

Choosing Based on Use Case

• For coding tasks: Sonnet-30B is the clear winner, offering near-GPT-4-level performance at a fraction of the cost. Developers building tools like code assistants or debugging aids should consider it. If you integrate such models into a platform, ASI Biont supports connecting to multiple LLM providers through standard APIs, allowing seamless switching between models based on task complexity.

• For creative content: YandexGPT Pro 5.1 excels in generating marketing copy, stories, or social media posts, especially in Russian. Its stylistic flexibility makes it ideal for content teams.

• For factual accuracy: GigaChat 2 MAX is the safest bet for tasks requiring reliable information, such as answering customer queries or summarizing legal documents. Its integration with Sber's data sources adds a layer of trust.

• For budget-conscious projects: Sonnet-7B and 13B are viable for simple tasks like email drafting or basic summarization, where occasional errors are acceptable. They can be self-hosted, avoiding API costs altogether.

Cost-Efficiency Trade-offs

The study also analyzed the cost per test for each model. Sonnet-7B cost approximately $0.001 per test, while Sonnet-30B cost $0.01. GigaChat 2 MAX and YandexGPT Pro 5.1 cost $0.02–0.03 per test due to API pricing. For high-volume applications (e.g., 10,000 queries per day), using Sonnet-7B could save thousands of dollars annually compared to proprietary models, though with a 20–30% drop in quality. The authors recommend a hybrid approach: use smaller models for routine tasks and larger ones for complex queries.

Limitations of the Study

While the 12-test framework is practical, it has limitations. First, the sample size is small—only one prompt per test type—so results may not capture variability. Second, human evaluators, though bilingual, may introduce subjectivity, especially in creativity scoring. Third, the study does not measure inference speed or latency, which are critical for real-time applications. Finally, the models were tested in July 2026, and newer versions may have since been released. For instance, YandexGPT Pro 5.2 was announced shortly after but was not included. Users should treat these results as a snapshot rather than a definitive ranking.

Conclusion

The comparative analysis of 12 tests for mid-range LLMs reveals that no single model dominates all dimensions. Sonnet-30B offers the best balance of reasoning and coding at low cost, making it a strong candidate for technical applications. GigaChat 2 MAX leads in factual accuracy, suitable for enterprise knowledge management. YandexGPT Pro 5.1 excels in creativity, particularly for Russian-language content. For most users, the choice depends on prioritizing accuracy, cost, or style. The study underscores the importance of task-specific benchmarking rather than relying on generic leaderboards. As the LLM landscape continues to evolve, such practical evaluations will remain essential for informed decision-making.

For a deeper dive into the raw test prompts and full responses, refer to the original article on Habr. The authors have made their evaluation scripts available on GitHub for reproducibility.

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