MQ
MODELQUEST
choose the right AI for the quest
Gemini 3.1 Pro · API · High thinking
Evidenceacceptedthird_party_leaderboard

SWE-bench Verified

SWE-bench+ / openlm.ai — Gemini 3.1 Pro

ev_2026_07_gem31pro_swe_openlm

Result
79.8%
Confidence B
Tested
2026-02-19
Runs
1
Configuration

How it was measured

Exact model
gemini-3.1-pro-preview
Reasoning
high
Agent harness
mini-swe-agent / board default
Tool access
agent harness (board standard)
Prompting
board default agent protocol

PRIMARY STR — third-party SWE-bench Verified board (openlm). Vals difficulty table places Gemini 3.1 Pro Preview mid-pack (~79% weighted from bucket rates).

Caveats

Known limitations

  • Prefer Vals overall float when published as single accuracy with stderr.
  • Do not equate with vendor SWE Verified 80.6% (different harness).
  • Preview model id; re-pin if GA string replaces preview.
Weighting

Evidence weight factors

The engine weights each evidence row by these factors (0–1) when fusing scores — higher is more trusted.

relevance0.95
quality0.80
recency0.85
health0.75
comparability0.80
reliability0.75
Benchmark

SWE-bench Verified

Human-filtered 500-instance subset of SWE-bench evaluating whether models can resolve real GitHub issues in existing repositories under a controlled agent harness.

Benchmark source →