Scope

• Model tested: google/gemini-3.5-flash
• Total tests run: 459
• Categories included: All available categories were tested (coding, cost, debugging, hallucination, reasoning, refactoring, security, speed, ui)
• Run mode: Full benchmark (no --limit or --category filter applied)
• Fail-fast behavior: Disabled (fail_fast=false)
• Results completeness: Partial — results_truncated=true, meaning not all test outcomes are included in this payload.

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Performance Summary

• Overall pass rate: 108/459 (23.5%)
• Total score: 1308.69 out of 3155 (41.5%)
• Total cost: $5.43
• Average latency: 6.66s
• Median latency: 4.94s
• Average time to first token (TTFT): 2.25s
• Average output speed: 338.25 tok/s

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Category-Level Performance

✅ Strong Performance

• Coding:
  • Pass rate: 51/60 (85%)
  • Score: 225/261 (86.2%)
  • Latency: 2.28s TTFT, 323.1 tok/s output speed
  • Subcategory highlights:
    • coding::easy: Perfect pass rate (20/20)
    • coding::medium: 85% pass rate
    • coding::hard: 70% pass rate

⚠️ Moderate Performance

• Security:
  • Pass rate: 13/60 (21.7%)
  • Score: 236/512 (46.1%)
  • Strongest in security::medium (40% pass rate)
• Reasoning:
  • Pass rate: 8/60 (13.3%)
  • Score: 273/541 (50.5%) — high score despite low pass rate, suggesting partial credit on complex tasks
• Speed:
  • Pass rate: 14/60 (23.3%)
  • Score: 83/180 (46.1%) — better on harder tasks (35% pass rate on speed::hard)

❌ Weak Performance

• Debugging:
  • Pass rate: 0/60 (0%)
  • Score: 155/601 (25.8%) — all failures, but some scoring suggests partial credit
• Hallucination:
  • Pass rate: 5/60 (8.3%)
  • Score: 115/360 (31.9%) — frequent false claims about APIs and behaviors
• Refactoring:
  • Pass rate: 2/60 (3.3%)
  • Score: 135/541 (24.9%) — very low success rate across all difficulty levels
• Cost:
  • Pass rate: 13/30 (43.3%) — mixed results, with only 20% pass rate on cost::hard

⚠️ Limited Data

• UI: Only 9 tests; pass rate 2/9 (22.2%), all failures on easy and hard subcategories.

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Notable Observations

• High hallucination rate: Model frequently invents non-existent API behaviors (e.g., halluc-api-*, halluc-doc-*), especially in hard and medium levels.
• Strong on coding, weak on reasoning about systems: Excels at code generation but struggles with debugging, system constraints, and cost implications.
• Cost inefficiency in refactoring: Despite high token usage (121k completion tokens), pass rate is near zero in refactoring.
• Latency variability: Average 6.66s latency with 4.94s median — long-tail delays likely due to complex or failing tasks.
• TTFT consistency: Time to first token is stable across categories (~2.2–2.4s), suggesting prompt processing is predictable.

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Summary

The google/gemini-3.5-flash model demonstrates strong coding ability but significant weaknesses in debugging, refactoring, and hallucination avoidance. It scores moderately on reasoning and security but fails most advanced system-design tasks. The high cost ($5.43) and partial results suggest this was a large, expensive run with incomplete outcome data. Model is suitable for straightforward code generation but unreliable for system reasoning or safety-critical tasks.