Speed analysis
Latency measured across all benchmark runs. P50 (median) and P95 (95th percentile) give a realistic picture of response speed under normal and peak load.
Quality scores
How this model compares to the rest of the field on each prompt category, from a pairwise fit over the same prompts. The raw judge score sits underneath each number.
Win rate per category: how often this model beats a field-average model on a prompt from that category. 50% is average, not a failing grade. It is not a percentage of correct answers.
Pricing
What you pay per million tokens when you use this model on Tokonomix, plus an estimate for a typical conversation.
Tokens per second
Throughput in tokens per second, derived from measured P50 latency. Higher is better; fluctuations track provider-side load.
Estimated from P50 latency × 200 output tokens — the absolute number depends on this assumption; the trend is what matters.
Capabilities
Availability
Availability
How often this model answers when we call it — measured across real API requests and live tests over the last 30 days. This is separate from quality: these numbers only tell you whether the model responds, not how good the answer is.
Last 7 days
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Last 30 days
100.0%
n=1
Median response time
1,277ms
n=1
Based on 171 measurements over the last 30 days.
Technical details
Only live API calls and live-test requests count — internal probes and benchmark runs are excluded.
Calls with a custom API key (BYOK) are excluded: those failures are key-specific, not a sign of model downtime.
Failed calls are NOT included in quality scores — quality is measured on successful responses only. Availability and quality are independent signals.
Median response time (p50) across successful calls with a recorded duration. Outliers (very slow or very fast calls) pull the median less than the average.
Total calls (30d)
1
OK responses (30d)
1
Total calls (7d)
0
OK responses (7d)
0
Tokonomix benchmark verdicts
Gemini 3.6 Flash adds comprehensive multimodal tools and reasoning features
Gemini 3.6 Flash has significantly expanded its capabilities since the previous benchmark window, introducing a suite of new features that transform it into a comprehensive multimodal AI model. The model now supports tool calling with parallel execution, vision processing, audio input, PDF document handling, and structured output through both JSON mode and JSON schema validation. A notable addition is reasoning capability, suggesting enhanced problem-solving potential. The model also now offers prompt caching, which should improve efficiency for repetitive tasks. These additions position Gemini 3.6 Flash as a versatile option for developers building applications that require multimodal understanding and structured interactions. The extensive feature set indicates Google's commitment to making this Flash variant competitive across diverse use cases, from document processing to audio analysis. Users should be aware that while these capabilities are now present, actual performance benchmarks for quality, latency, and reliability across these new features remain to be evaluated in production scenarios. The rapid expansion of features suggests an aggressive development cycle, though real-world testing will be essential to validate the practical utility of these additions.
Quality
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Latency p50
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Test runs
0