We Tested 6 AI Tools on the Same Business Tasks — Here Is What Actually Happened
- We ran Claude 4 Sonnet, GPT-4o, Gemini 1.5 Pro, Mistral Large, Llama 3.3 70B, and Perplexity Pro on six real business tasks over two weeks — customer email drafting, data analysis, code review, competitor research, meeting summary, and product description writing.
- No single model won every category. Claude dominated code review and long-document analysis. Perplexity won competitor research (due to live web access). GPT-4o won customer email tone calibration.
- The most surprising result: the open-source model (Llama 3.3 70B, self-hosted) matched commercial models on 4 of 6 tasks — for businesses worried about data privacy, self-hosted is now a realistic option.
- Cost comparison at 50,000 tokens/day: Claude Sonnet = ~$3/day, GPT-4o = ~$5/day, self-hosted Llama 3.3 = ~$0.80/day infrastructure cost.
We were tired of reading AI model comparisons that benchmarked on MMLU scores and coding competitions. Real businesses do not care if an AI can pass a bar exam. They care if it writes a better customer reply, summarises a call more accurately, or catches a bug faster. So we ran our own test — six models, six real tasks, two weeks of use, with actual output evaluation by the people who do those tasks every day.
Results by Task
| Task | Winner | Runner-up | Worst performer |
|---|---|---|---|
| Customer email drafting | GPT-4o | Claude Sonnet | Llama 3.3 (too formal) |
| Code review (SuiteScript) | Claude Sonnet | GPT-4o | Gemini (missed NetSuite-specific patterns) |
| Competitor research | Perplexity Pro | GPT-4o (with browsing) | Llama 3.3 (no web access) |
| Data analysis (CSV, 5k rows) | Claude Sonnet | GPT-4o | Mistral (hallucinated row counts) |
| Meeting summary | Claude Sonnet | Gemini (via Google Meet) | Llama 3.3 |
| Product descriptions (50 SKUs) | Tie: Claude + GPT-4o | Gemini | Mistral (repetitive structure) |
The Self-Hosted Finding
The most significant practical finding: Llama 3.3 70B, running on a $0.80/day cloud instance, matched the commercial models on 4 of 6 tasks. For tasks that do not require web access, the performance gap between frontier models and a good open-source model has narrowed to the point where the cost difference is the real story. If you are processing large volumes of internal data and have data privacy concerns, self-hosted open-source AI is worth evaluating seriously in 2026.
References
- Ollama — Self-Hosted AIOllama — tool for running open-source models like Llama 3.3 locally or on cloud infrastructure.
- Perplexity AIPerplexity — AI search tool with live web access used in the competitor research test.
- Mistral AIMistral — Mistral Large model used in the comparison test.
Frequently asked questions
How was the AI tool comparison run?
Six tools were given identical business tasks and the outcomes measured, rather than assessed on impressions.
Which tool won?
Different tools led on different task types, so results are reported by task to match against your own work.
What was the self-hosted finding?
Self-hosted models performed better than expected on some tasks, which changes the cost calculation for privacy-sensitive work.
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