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
1,118ms
n=1
Based on 137 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
gpt-6-luna debuts with strong coding performance and multimodal capabilities
OpenAI's gpt-6-luna enters the benchmarking arena with a comprehensive feature set and competitive performance across multiple domains. The model demonstrates particular strength in coding tasks, achieving 74.8% on HumanEval and 80.2% on MBPP, positioning it among the stronger code-generation models currently available. Mathematical reasoning shows solid results with 88.3% on GSM8K, though MATH performance at 71.2% suggests room for improvement on more challenging problems. Multimodal capabilities are present with vision support, alongside modern tooling features including function calling, JSON mode with schema support, PDF input processing, and parallel tool execution. The model also incorporates reasoning capabilities and prompt caching for efficiency. General language understanding appears competent with 85.1% on MMLU, indicating broad knowledge coverage. As a baseline entry, gpt-6-luna presents as a well-rounded model suitable for developers requiring strong coding assistance combined with multimodal input handling. Future benchmark windows will reveal performance stability and how it evolves relative to competing models in this rapidly advancing space.
Quality
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Latency p50
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Test runs
0