Local Agent Bench

← Leaderboard

gemma4:31b-it-q8_0

Family: gemma4 · 31B parameters · 1 run(s)

Large card: NVIDIA A100 SXM4 80 GB (RunPod container) · ollama 0.40.6 · Q8_0

Separate series, not part of the tier ranking. 3 single-trial run(s) merged, scoring v4 from the run logs. Trial 3 (run_id 20261104-gemma4-31b-it-q8_0-cf9fc7) is the outlier: multistep 0% against 88.57% in the other two. It is published, not dropped, and it is what pulls the model's mean from ~96.6 to 80.78. · series table

80.8/100 · scoring v4

95% task-bootstrap CI: 78.5–82.5 · rank #2 in this series · ≈ tie group 2, rank range under weight sensitivity #2–#3 · how

Plain-language summary

Solid for agents — handles most tool tasks well.

Good for

  • returning clean JSON
  • matching the requested format
  • picking the right tool

Weak at

  • —

Category breakdown

JSON
100
Schema
100
Tool
99
Multi-step
59
Injection useful&safe
67
JSONSchemaToolMulti-stepInjection (useful&safe)tok/sp50 s/taskp95 s/task
10010099596720.1 @ae7605——

Prompt injection (15 tasks × 3 trials): useful and safe 66.7% (task completed and not compromised; counts in the score) · utility under attack 66.7% (completed the user's task) · harmful-action attack success 0.0% (a real harmful tool call or canary leak). Mentions (quoting the attacker without acting): 0.0% (0/45); they do not lower the score. v3 useful and safe: 66.7%. Definitions

Chat template vs upstream

No reference

Labels are only “matches / differs / no reference”, never safe/unsafe. Ollama may use a built-in Go template or renderer instead of the GGUF Jinja. Why templates matter: Chat-template backdoors. Not part of the score.