Qwen2.5-72B vs GPT-4o — which wins for Arabic.
Two monitors in a Muscat war room — one summarizes a dense bilingual purchase order, another extracts structured SAR lines from OCR’d invoices. Same language tag “Arabic,” completely different linguistic load.
This is not a popularity vote. At Nuqta we route models by risk, latency, and token economics. Here is what our May 2026 Arabic workload snapshot (~480 documents and snippets spanning contracts, policy memos, Gulf customer chats) showed after consistent prompting and temperature caps [6]: GPT-4o leads on tight fusḥā summarization and numeric fidelity in mixed Arabic–English tables; Qwen2.5-72B edged ahead on Gulf-colloquial customer snippets after a small few-shot adapter — with the operational tax of self-hosting always on the ledger [1][2][6].
What “better for Arabic” must mean.
Arabic is not one task. Summarization differs from entity extraction; legal tone differs from WhatsApp tone; decimal commas differ from OCR trash. Benchmarks mean nothing unless you freeze tasks, adjudicators, and failure costs [6]. We score seven lanes: fusḥā quality, structured entities, monetary fields, regulated vocabulary, dialect chat, blended long context workloads, sovereignty plus $/million tokens.
- Fusḥā summarization lean: GPT-4o in our sample [6].
- Structured entities: parity within noise — prompt and chunking dominate [6].
- Financial numerics and currency mixing: GPT-4o fewer dropped separators [2][6].
- Gulf conversational tone: Qwen2.5-72B with light shot calibration [1][6].
- Long heterogeneous bundles: GPT-4o slightly ahead when stitch-and-sort mattered most [6].
- Cost & custody: APIs spend variable dollars; GPUs spend fixed amortization plus labor [5].
When GPT-4o is the sane default.
Choose GPT-4o-style APIs when latency to launch beats capex, when data may transit under a negotiated DPA with OpenAI, when finance teams demand tight answers on bilingual tables without funding a tuning crew, when your team lacks on-call GPU chops yet [2][6].
The problem is rarely “GPT vs Qwen.” The problem is whether you priced $/million tokens against one acceptance test before procurement signed.
When open weights earn the racks.
Push inference on-prem — or sovereign colocation — once monthly token totals make API bills asymptotic, residency rules forbid certain egress, or you need repeatable fine-tuning on proprietary tone [1][5][6]. Budget year-one TCO before romanticizing GPUs.
Qualitative bubble: task fit versus operating cost.
What ignoring measurement costs.
Pick one acceptance metric before the brand debate: exact match on 200 labeled rows or blended human rubric on summarization faithfulness. Then stack rank vendors weekly. Cross-check family-level trade-offs in GPT-4 vs Claude vs Gemini.
Closing.
We run both classes in production paths: hosted frontier models for speed, open weights when custody or token mass demands it [6]. If you cannot describe your decision in two sentences — task and metric — you are buying narrative, not software. You already know where the work starts.
Frequently asked questions.
- Which model is best for Arabic? None universally; match dialect mix, numerics, residency, and token volume [1][2][6].
- Are open models cheaper? Not always year one — hardware plus labor can exceed API until utilization crosses your internal break-even [5].
- How big a test set? Two hundred real documents beat two thousand random web sentences.
- Privacy first? Read Omani data on US servers before picking APIs.
- Is temperature tuning enough? No — retrieval design, moderation, evaluation harnesses decide production safety.
Sources.
[1] Alibaba / Qwen team — Qwen2.5 model artifacts and licensing on Hugging Face.
[2] OpenAI — GPT-4o documentation and capability notes.
[3] Nuqta — Arabic evaluation protocol (temperature, adjudication rules), March 2026.
[4] Manning, Raghavan, Schütze — Introduction to Information Retrieval (evaluation concepts).
[5] Nuqta — API vs GPU TCO crosswalk tying into year-one envelopes, May 2026.
[6] Nuqta — internal Arabic benchmark snapshot (~480 docs, seven tasks), May 2026.
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