Public
jhonharrisricauerta2-ops / Valkyrie
Benchmark updated: 10/1/202630 Tasks
Scalable, cloud-native infrastructure for evaluating AI agents across any benchmark.
Languages
Python99.5%Makefile0.4%Dockerfile<0.1%Mako<0.1%Shell<0.1%
Harness | Input / Output Cost | ||||||
|---|---|---|---|---|---|---|---|
1 |  Mini-SWE-agent | 26 / 30 | $1.36 | $1.5/$7.5 | 6m34s |
Key Takeaways
- This single run resolved 86.67% of tasks with Mini-SWE-agent.
Cost Analysis
Cost / Test vs. Accuracy
ACCURACYCOST
Average Token Use / Test
Token Usage
InputOutputReasoningCache readCache write
No token usage data available.
Cost is the clearest tradeoff in this comparison. Gemini 3.6 Flash leads at 86.67% for $1.36 per test. No other model in this comparison is cheaper.
Latency Analysis
Latency vs. Accuracy
ACCURACYLATENCY
Average Response Time / Test
Response Time
Gemini 3.6 Flash
Gemini 3.6 Flash is both the most accurate and fastest model in this comparison at 86.67% and 6m 34s.