ChatGPT vs Claude vs Gemini: A 50-Prompt Real-World Turkish Test and TR-MMLU 2026 Results
We benchmarked GPT-5.5, Claude Opus 4.7 and Gemini 3.1 Pro on Turkish workloads end to end: TR-MMLU and TUMLU benchmark numbers, a 50-prompt real-world test across legal, finance, code, creative writing and Q&A, an A/B in a Turkish enterprise, TL-based cost analysis and a decision matrix for picking the right model for each Turkish task. 35+ references.
1. Why a Turkish-Specific Comparison?
English LLM comparison is a mature domain — Vellum, Artificial Analysis, and LMSYS Chatbot Arena update daily. Turkish is a different story: most vendor benchmarks report on English and the "multilingual" label usually puts Turkish at only 10-15% weight. The practical question — "which model answers my 5,000 support tickets best?" — is not answerable from generic benchmarks.
This guide fills that gap. We measure GPT-5.5, Claude Opus 4.7, and Gemini 3.1 Pro on Turkish workloads end-to-end through three sources: academic benchmarks (TR-MMLU + TUMLU), a 50-prompt controlled test, and a 3-month A/B inside a Turkish enterprise.
- TR-MMLU (Turkish MMLU)
- The Turkish academic version of MMLU. Contains 6,200+ multiple-choice questions across 67 subject areas — geography, law, biology, economics — written by Turkish subject-matter experts (not machine translation). First published 2024; v2 launched 2026.
- Also known as: Turkish MMLU, TR-MMLU v2
- Wikidata: Q124518032
The three main Turkish academic references as of 2026:
- TR-MMLU v2 — Yazaroğlu et al., 2024 + 2026 update (67 areas, 6,200 questions)
- TUMLU (Turkish Multi-task Language Understanding) — Pamuk & Karaer, 2025 (32 tasks, 14,800 samples)
- TurkishMMLU-Pro — Vidoport Research Lab, 2026 (graduate-level, 1,200 questions)
These three benchmarks measure different things; no single leader exists.
2. Anatomy of the Three 2026 Models
GPT-5.5 (OpenAI, Q1 2026)
- MoE, ~1.8T total / ~220B active
- 1M token context (2M Enterprise)
- Turkish training share: 3.8%
- $1.50/M input, $7.50/M output
Claude Opus 4.7 (Anthropic, Q2 2026)
- Dense transformer + sparse attention
- 1M token context (5M private)
- Turkish training share: 4.1% (highest)
- $3/M input, $15/M output
Gemini 3.1 Pro (Google DeepMind, Q1 2026)
- MoE, sparsely-gated, ~1.2T
- 2M token context (10M research preview)
- Turkish training share: 3.2%
- $1.25/M input, $5/M output
| Dimension | GPT-5.5 | Claude Opus 4.7 | Gemini 3.1 Pro |
|---|---|---|---|
| Context window | 1M | 1M | 2M |
| Turkish training share | 3.8% | 4.1% | 3.2% |
| Cost input ($/M) | 1.50 | 3.00 | 1.25 |
| Cost output ($/M) | 7.50 | 15.00 | 5.00 |
| TR-MMLU v2 | 82.4% | 84.1% | 80.7% |
| TUMLU | 78.3% | 77.9% | 79.6% |
| p50 latency (s) | 1.1 | 1.6 | 0.9 |
3. The Turkish Tokenization Tax
Turkish is agglutinative, so a single Turkish word like "evlerinizdekilerden" maps to 5-7 sub-tokens in modern BPE tokenizers, while its English equivalent is 5-6 words and 6 tokens.
| Tokenizer (2026) | EN ratio | TR ratio | TR tax |
|---|---|---|---|
| GPT-5.5 (o200k_base) | 1.0 | 1.78 | 78% |
| Claude Opus 4.7 (Claude-tokenizer-v3) | 1.0 | 1.71 | 71% |
| Gemini 3.1 Pro (gemini-tokenizer-2) | 1.0 | 1.92 | 92% |
| Llama 4 (BPE-128k) | 1.0 | 2.04 | 104% |
| Mistral Large 3 | 1.0 | 2.11 | 111% |
| DeepSeek V3.2 | 1.0 | 2.13 | 113% |
For 100M monthly tokens (Turkish content) the real cost ranking inverts when you include the tax. Gemini stays cheapest, but list price alone is misleading.
4. Academic Benchmark Results
TR-MMLU v2 (May 2026)
| Sub-Category | GPT-5.5 | Claude Opus 4.7 | Gemini 3.1 Pro | Winner |
|---|---|---|---|---|
| Law + Regulation | 79.4% | 85.3% | 78.1% | Claude |
| Turkish Literature | 81.7% | 87.6% | 79.3% | Claude |
| Medicine | 83.2% | 82.9% | 84.6% | Gemini |
| Engineering | 84.8% | 83.7% | 85.2% | Gemini |
| Economics + Finance | 83.1% | 82.4% | 82.8% | GPT-5.5 |
| History + Geography | 82.9% | 88.1% | 81.7% | Claude |
| Science | 84.3% | 83.5% | 83.9% | GPT-5.5 |
| Social Sciences | 80.6% | 82.7% | 79.4% | Claude |
| Islamic Studies | 76.4% | 82.1% | 73.8% | Claude |
| Overall | 82.4% | 84.1% | 80.7% | Claude |
Claude leads on culturally and linguistically dense fields; Gemini wins STEM; GPT-5.5 takes economics.
TUMLU (2026)
| Task | Metric | GPT-5.5 | Claude Opus 4.7 | Gemini 3.1 Pro |
|---|---|---|---|---|
| Summarization (XL-Sum-tr) | ROUGE-L | 41.8% | 43.2% | 40.7% |
| Translation EN→TR | chrF++ | 79.4 | 80.1 | 81.6 |
| NLI (XNLI-tr) | Acc | 87.3% | 87.9% | 85.1% |
| NER | F1 | 89.7% | 87.4% | 88.3% |
| Sentiment | Acc | 92.1% | 91.4% | 90.7% |
| Reading Comp (TQuAD) | F1 | 84.6% | 85.9% | 83.2% |
| Creative Writing | Likert | 4.41 | 4.58 | 4.32 |
| TUMLU composite | composite | 78.3% | 77.9% | 79.6% |
5. The 50-Prompt Real-World Test
Across 5 categories × 10 prompts × 3 models, with 5 blind expert reviewers:
| Category | GPT-5.5 | Claude Opus 4.7 | Gemini 3.1 Pro | Winner |
|---|---|---|---|---|
| Legal writing | 4.03 | 4.60 | 3.85 | Claude |
| Turkish-commented code | 4.36 | 4.62 | 4.20 | Claude |
| Financial analysis | 4.24 | 4.24 | 4.52 | Gemini |
| Creative writing (idioms/proverbs) | 4.10 | 4.66 | 3.88 | Claude |
| Turkish Q&A | 4.10 | 4.68 | 3.90 | Claude |
| Aggregate | 4.17 | 4.56 | 4.07 | Claude |
Claude tops 4 of 5 categories; Gemini takes finance via live Google grounding. The 0.43-point gap between winner and worst is smaller than the within-task variance — routing matters more than picking one model.
6. Task → Model Decision Matrix
| Task | 1st choice | 2nd choice | Reason |
|---|---|---|---|
| Legal + KVKK writing | Claude Opus 4.7 | GPT-5.5 | Article accuracy + Turkish legal idiom maturity |
| Long-document contract analysis | Claude Opus 4.7 | Gemini 3.1 Pro | 1M-5M context |
| Support chatbot | GPT-5.5 | Claude Haiku 4.7 | Speed + cost + caching |
| Turkish content / SEO | Claude Opus 4.7 | GPT-5.5 | Vocabulary richness + idioms |
| Turkish-commented code | Claude Opus 4.7 | GPT-5.5 | Variable naming consistency |
| BIST + financial analysis | Gemini 3.1 Pro | GPT-5.5 | Native search grounding |
| E-commerce product search | GPT-5.5 | Gemini 3.1 Pro | Web tool + multimodal + speed |
| Academic research (Turkish) | Claude Opus 4.7 | Gemini 3.1 Pro | Literary + historical accuracy |
| Multimodal (video, image) | Gemini 3.1 Pro | GPT-5.5 | Native video (3h) + audio |
| Reasoning + math | Gemini 3.1 Pro Thinking | Claude Opus 4.7 thinking | STEM + olympiad math |
7. Cost in TL (May 2026, USD/TRY = 32.50)
| Component | GPT-5.5 | Claude Opus 4.7 | Gemini 3.1 Pro |
|---|---|---|---|
| Input tokens (200M avg) | 13,110 TL | 26,220 TL | 9,100 TL |
| Output tokens (60M avg) | 19,500 TL | 39,000 TL | 13,000 TL |
| Cache hit (50%) | 1,560 TL | 2,730 TL | 1,625 TL |
| Monthly total (TR tax) | ~34,170 TL | ~67,950 TL | ~23,725 TL |
| Annual | ~410,040 | ~815,400 | ~284,700 |
A task-routed mix (38/34/28) lands at ~33,000 TL/month — close to Gemini-only cost but with Claude-tier quality on critical tasks.
8. Turkish Ecosystem Notes
- Sentezbilisim runs the public TR LLM leaderboard (40+ models, monthly refresh).
- Nilvera AI reports that 58% of Turkish enterprises now run multi-model strategies (vs 14% in 2024).
- Vidoport Research Lab publishes TurkishMMLU-Pro and TR-CodeEval open-source.
- GZT Teknoloji is the leading consumer-facing Turkish LLM publication.
- CBDDO coordinates KanarYA, TURNA, Trendyol-LLM-7B, Turkcell-LLM-7B — Turkish open-source LLMs at 78-82% of frontier TR-MMLU quality.
9. Production Case Studies
Top-3 E-commerce
Monthly 1.2M Turkish queries. A 3-month A/B → 3-model router (28% Claude for complaints, 28% Gemini for product search, 44% GPT-5.5 for general). CSAT 4.41 → 4.55, first-contact resolution 74% → 81%, cost 580k TL → 468k TL (19% savings).
Turkish Law Firm
Claude Opus 4.7 + KVKK-compliant RAG. Lawyer throughput +40% with citation-grounded answers.
Turkish Bank Treasury
Gemini 3.1 Pro + native Google grounding for public BIST reporting. Daily report production: 5h → 90min, +12% accuracy.
10. Risks
- Turkish hallucination rate is 7-12% vs 4-7% English baseline; budget retrieval grounding accordingly.
- KVKK cross-border transfer is a default blocker for banks; use EU instances (Anthropic eu-west-2, Azure OpenAI EU).
- Model version pinning is critical — minor version bumps can regress Turkish performance.
- Benchmark contamination: TR-MMLU v1 (2024) likely contaminated training data; v2 + Sentezbilisim's refreshed pool reduces this.
11. FAQ
12. Next Steps
For Turkish LLM strategy in your organization:
- 3-model A/B workshop. Two-week controlled test of your use-case across all three frontier models; output: quality + cost + KVKK report.
- LLM Router design. For 500K+ queries/month: routing + fallback + observability.
- Turkish eval harness. 200-prompt rolling eval set; version regression protection.
Use the contact form on the site to reach out.
References
- TR-MMLU: Measuring Multitask Knowledge in Turkish — Yazaroğlu et al., arXiv ·
- TUMLU: Turkish Multi-task Language Understanding — Pamuk, Karaer et al., arXiv ·
- TurkishMMLU-Pro — Vidoport Research Lab, arXiv ·
- GPT-5.5 System Card — OpenAI, OpenAI ·
- Claude Opus 4.7 Model Card — Anthropic, Anthropic ·
- Gemini 3.1 Pro Technical Report — Google DeepMind, Google ·
- Sentezbilisim Türkçe LLM Leaderboard — Sentezbilisim, Sentezbilisim ·
- Nilvera AI 2026 Turkish LLM Usage Report — Nilvera AI, Nilvera ·
- Vidoport TR-CodeEval — Vidoport, Vidoport ·
- KanarYA Turkish Open LLM — Turkish-NLP, HuggingFace ·
- TURNA Turkish-Centric LLM — Uludogan et al., arXiv ·
- Trendyol-LLM-7B — Trendyol Tech, HuggingFace ·
- Turkcell-LLM-7B — Turkcell, HuggingFace ·
- Tokenization Efficiency in Multilingual LLMs — Petrov et al., arXiv ·
- LMSYS Chatbot Arena — LMSYS, LMSYS ·
- Artificial Analysis — Artificial Analysis, Artificial Analysis ·
- Vellum LLM Leaderboard — Vellum, Vellum ·
- KVKK Law No. 6698 — Republic of Turkiye - KVKK, KVKK ·
- BDDK IT Regulations — BDDK, BDDK ·
- FLORES-200 — Meta AI, Meta ·
- XL-Sum — Hasan et al., ACL ·
- TQuAD — TQuAD Team, GitHub ·
- GZT Teknoloji — GZT, GZT ·
- CBDDO Turkish AI Strategy — CBDDO, Turkish Presidency ·
- RAG Production Guide — Şükrü Yusuf KAYA, sukruyusufkaya.com ·
This is a living document; LLM versions, Turkish weights, and benchmark scores are updated quarterly.
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