MQ
MODELQUEST
choose the right AI for the quest
DeepSeek V4 Pro · API · Max effort
Evidenceacceptedthird_party_leaderboard

SWE-bench Verified

SWE-bench+ / openlm.ai — DeepSeek-V4

ev_2026_07_dsv4pro_swe_openlm

Result
76.2%
Confidence B
Tested
2026-04-23
Runs
1
Configuration

How it was measured

Exact model
deepseek-v4-pro
Reasoning
max
Agent harness
mini-swe-agent / board default
Tool access
agent harness (board standard)
Prompting
board default agent protocol

SECONDARY STR — openlm.ai SWE-bench+ mirror. Prefer Vals mini-swe primary (ev_2026_07_dsv4pro_swe_vals 77.4%) for peer comparisons.

Caveats

Known limitations

  • Secondary to Vals mini-swe primary (same harness family, slightly different board).
  • Do not equate with vendor SWE Verified 80.6% (different harness/config).
Weighting

Evidence weight factors

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

relevance0.90
quality0.75
recency0.90
health0.80
comparability0.75
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 →