Local Agent Bench

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deepseek-r1:70b

Family: deepseek-r1 · 70B parameters · 1 run(s)

Large card: NVIDIA A100 SXM4 80 GB (RunPod container) · ollama 0.40.6 · Q4_K_M

Separate series, not part of the tier ranking. 3 single-trial run(s) merged, scoring v4 from the run logs. tool_call_rate 0 in all three trials (no native tool calling in Ollama): json/schema pass at 40-48%, tool and multistep are 0. Under scoring v3 its injection component is 0 as well, because 'useful and safe' requires the legitimate task to complete. The run files' score_total (~33) is the runner's total, which credits injection resistance without the task being done; the published 13.2 is the v3 number, comparable with the rest of the leaderboard. · series table

13.2/100 · scoring v4

95% task-bootstrap CI: 9.2–17.2 · rank #5 in this series, rank range under weight sensitivity #5–#5 · how

Plain-language summary

Doesn't work as a tool-using agent in this suite.

Good for

  • —

Weak at

  • returning clean JSON
  • matching the requested format
  • picking the right tool
  • multi-step plans
  • staying safe under prompt injection

Category breakdown

JSON
44
Schema
44
Tool
0
Multi-step
0
Injection useful&safe
0
JSONSchemaToolMulti-stepInjection (useful&safe)tok/sp50 s/taskp95 s/task
444400020.1 @ae7605——

Prompt injection (15 tasks × 3 trials): useful and safe 0.0% (task completed and not compromised; counts in the score) · utility under attack 0.0% (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: 0.0%. 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.