Independent comparison of 54 LLMs across 8 management task categories
Models from the same quality cluster as global leaders are available in Russia without restrictions. Kimi K2.5 (4.74), GPT-5.4 (4.80), and ~13 other models are statistically indistinguishable on our task set (gap < 0.30 is within noise at n=4 scenarios per category).
Chinese models are in the same statistical cluster as Western leaders and more accessible. Kimi K2.5, MiniMax M2.7, and Qwen3.5 Plus are in the global top 15 and work without VPN. Our benchmark cannot rank within this cluster – differences are within measurement noise.
Russian models lag behind: YandexGPT Pro 5.1 scored 3.13, GigaChat-Ultra – 3.26. The gap with leaders exceeds 1.5 points – this is statistically significant (above MDD of 1.25). Suitable for routine tasks, not for analytics.
Top models by category (note: within each category, leaders differ by < 0.10 and are effectively tied): information search – GPT-5.2 Pro, communication – GPT-5 Mini, analysis & planning – Claude Sonnet 4.5/4.6, learning & team management – Claude Sonnet 4.5/4.6, regional context – GPT-5.4.
Kimi K2.5
4.74
MiniMax M2.7
4.69
MiMo V2 Omni
4.62
Qwen3.5 Plus
4.56
Qwen3.5 397B
4.55
GPT-5.4
4.80
Claude Sonnet 4.5
4.78
GPT-5.2 Pro
4.78
Claude Opus 4.5
4.78
Claude Sonnet 4.6
4.77You can see the differences between models. In the free course module, you'll learn which model fits each task – and why the top-ranked one isn't always the best choice.
All models were tested with prompts written by a real manager – no prompt engineering. This shows how each tool works out of the box.
All 54 models solved identical 32 scenarios in Russian – tasks typical for a middle manager (team of 5–30 people). Prompts were written as a real manager writes – no optimization, no special techniques. This shows how a tool performs in everyday use.
Each response was evaluated by two independent LLM judges: Claude Opus 4.5 (weight 70%) and Gemini 3 Pro (weight 30%). Systematic bias correction applied: Claude tends to overrate (+0.39), Gemini – underrate (-0.53). Final score is a weighted consensus of both judges after correction.
Scale: 1.0–5.0
Statistical limitation: with 4 scenarios per category, the minimum detectable difference is ~1.25 points. The benchmark reliably separates tiers (e.g., GigaChat vs Kimi) but cannot rank models within the top ~15. Scores within 0.30 should be treated as tied.
| # | Model | Score | |
|---|---|---|---|
| 1 | 4.80 | ||
| 2 | 4.78 | ||
| 3 | 4.78 | ||
| 4 | 4.78 | ||
| 5 | 4.77 | ||
| 6 | 4.74 | ||
| 7 | 4.69 | ||
| 8 | 4.69 | ||
| 9 | 4.69 | ||
| 10 | 4.63 | ||
| 11 | 4.62 | ||
| 12 | 4.57 | ||
| 13 | 4.56 | ||
| 14 | 4.55 | ||
| 15 | 4.50 | ||
| 16 | 4.48 | ||
| 17 | 4.46 | ||
| 18 | 4.42 | ||
| 19 | 4.42 | ||
| 20 | 4.41 | ||
| 21 | 4.39 | ||
| 22 | 4.33 | ||
| 23 | 4.32 | ||
| 24 | 4.29 | ||
| 25 | 4.29 | ||
| 26 | 4.28 | ||
| 27 | 4.25 | ||
| 28 | 4.24 | ||
| 29 | 4.22 | ||
| 30 | 4.14 | ||
| 31 | 4.14 | ||
| 32 | 4.13 | ||
| 33 | 4.11 | ||
| 34 | 4.05 | ||
| 35 | 4.03 | ||
| 36 | 4.00 | ||
| 37 | 3.97 | ||
| 38 | 3.86 | ||
| 39 | 3.75 | ||
| 40 | 3.67 | ||
| 41 | 3.58 | ||
| 42 | 3.27 | ||
| 43 | 3.26 | ||
| 44 | 3.15 | ||
| 45 | 3.13 | ||
| 46 | 3.08 | ||
| 47 | 3.08 | ||
| 48 | 3.05 | ||
| 49 | 2.95 | ||
| 50 | 2.90 | ||
| 51 | 2.85 | ||
| 52 | 2.82 | ||
| 53 | 2.61 | ||
| 54 | 2.27 |
The benchmark gives you numbers, the course gives you the skill to choose. Open the free module and learn to match models to tasks – not just rankings.
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