🏆 Overall Leaderboard
Rank
Model
Score
Cases
Latency
Status
1
Qwen 2.5 7B
9.53
18 cases
16.4s avg
✓ Production
2
Qwen 7B (alt)
9.44
18 cases
3.9s avg
✓ Production
3
DeepSeek R1 7B
5.83
13 cases
32.8s avg
⚠ Judge errors
4
TinyLlama 1.1B
3.43
13 cases
20.9s avg
✗ Not ready
⚡ Key Finding
Qwen 2.5 7B is production-ready at 9.53/10 — achieving near-perfect scores on structured tasks, multilingual comprehension, and code generation, all at zero cost.
📊 Test Suite Results
⚡ Speed Test
Winner
Qwen 2.5 7B
Score
9.2/10
Cases
5
Avg Latency
1.2s
Perfect Scores
4/5
🌍 Multilingual Test
Winner
Qwen 2.5 7B
Score
10.0/10 ⭐
Languages
4
Perfect Scores
5/5
Status
FLAWLESS
📝 Context Stress
Winner
Qwen 2.5 7B
Score
9.73/10
Cases
3
Avg Latency
85.8s
vs DeepSeek
2× faster
💻 Coding Benchmark
Winner
Qwen 2.5 7B
Score
9.38/10
Cases
8
Perfect Scores
4/8
Avg Latency
4.2s
💡 Key Findings
- Qwen 2.5 7B scored PERFECT 10/10 on all 5 multilingual cases (Spanish, French, German, Japanese)
- Production-ready for code generation — 9.38/10 average, perfect scores on TypeScript, Python, refactoring
- 2× faster than DeepSeek with higher quality (9.53 vs 5.83, 16.4s vs 32.8s)
- TinyLlama is unusable — 3.43/10, fails multilingual, context, and reasoning tasks
- Cost savings: $1,800/year vs cloud for continuous evaluation workloads
- Local models excel at structured tasks — JSON, code, schemas, fact extraction
- Verdict enables data-driven model selection at zero cost with unlimited testing
🎯 Recommendations
✅ Deploy: Qwen 2.5 7B
Use for: Code generation, structured output (JSON/schemas), multilingual content, documentation, fact extraction, math calculations
Avoid for: Novel creative writing, ambiguous edge cases, very long context (>4K tokens)
Config: ollama run qwen2.5:7b • 4.7 GB VRAM • 4096 token context
⚠️ Investigate: DeepSeek R1 7B
Shows promise but needs debugging of judge errors. Potentially strong alternative if issues resolved.
❌ Avoid: TinyLlama 1.1B
Not production-ready. Only suitable for trivial tasks or experimentation.