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
100.0%
n=4
Last 30 days
100.0%
n=16
Median response time
9,770ms
n=16
Based on 186 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)
16
OK responses (30d)
16
Total calls (7d)
4
OK responses (7d)
4
Tokonomix benchmark verdicts
Major capability expansion with multimodal features and developer tools
This benchmark window introduces significant new functionality to gpt-6-sol, transforming it from a baseline model into a comprehensive multimodal platform. The model now supports vision processing, PDF input handling, and structured output modes including JSON schema validation and standard JSON mode. Tool calling capabilities have been added with support for parallel execution, alongside prompt caching for improved efficiency. A reasoning mode has been introduced, though specific performance metrics are not yet available. These additions represent a substantial evolution in the model's practical utility for developers building applications that require structured data extraction, document processing, or agentic workflows. The core performance baseline established in the previous window remains unchanged, as no comparative metrics are available for this period. Users should note that while the capability surface has expanded considerably, the effectiveness of these new features in production scenarios will require real-world validation. The simultaneous introduction of multiple features suggests a planned major release rather than incremental improvements.
Quality
—
Latency p50
—
Test runs
0