Eleven of fourteen models named TimescaleDB first on the direct prompt; zero named VictoriaMetrics. Both were named by all fourteen models and TimescaleDB carries 69 labels and VictoriaMetrics 57, so the shares are not directly comparable.
Named in two categories this edition.
Named in four categories this edition.
Share is the count of first choices across the direct, paraphrase, budget and scale prompts over all fourteen models, for a mid-market B2B company; rank is within the category; every quote names the model and the prompt it came from. Both figures come from the time series databases page.
Across every category in the November 2026 Edition, TimescaleDB and VictoriaMetrics were named in the same answer 138 times, of the 219 answers naming TimescaleDB and the 165 naming VictoriaMetrics. In those answers VictoriaMetrics took the first choice sixteen times and TimescaleDB seventy-eight.
| Model | Direct | Paraphrase | Comparative | Budget-constrained | Scale-constrained | Negative |
|---|---|---|---|---|---|---|
| Claude Haiku 4.5 | ||||||
| GPT-5.4 mini | ||||||
| Gemini 3.5 Flash | ||||||
| Perplexity Sonar | ||||||
| Grok 4.20 | ||||||
| Mistral Small | ||||||
| DeepSeek V4 Flash | ||||||
| Llama 4 Maverick | ||||||
| Qwen 3.7 Flash | ||||||
| Kimi K2 | ||||||
| GLM 4.7 FlashX | ||||||
| MiniMax M2.5 | ||||||
| GPT-6 Luna | ||||||
| Muse Glimmer 30B |
Bold names in an answer are the products the judge labeled a first choice; a model naming several gives each of them that label. The full answer text for every row is in the record.
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Six of eight in this category shown.
“avoid both InfluxDB and TimescaleDB if your time-series data has a natural transactional relationship with other data” Claude Haiku 4.5 · negative prompt · hard negative
“Caution: TimescaleDB is an excellent PostgreSQL extension for time-series, but it has specific architectural boundaries” Gemini 3.5 Flash · negative prompt · soft negative
“Be Cautious regarding Write Throughput: TimescaleDB ... may struggle to keep up without aggressive tuning” Qwen 3.7 Flash · negative prompt · soft negative
“IoT / high-volume sensor data: Use TimescaleDB (Postgres extension) — it supports full SQL, handles high cardinality gracefully” DeepSeek V4 Flash · negative prompt · first choice
“For most mid-market B2B companies, TimescaleDB (or a managed Timescale Cloud) is the best balance of cost, simplicity, and power.” GLM 4.7 FlashX · direct prompt · first choice
“the best time series database is TimescaleDB. It is the clear leader in adoption and recommendation for mid-market organizations” Mistral Small · direct prompt · first choice
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Five of five in this category shown.
“its cluster design prioritizes availability over consistency, and moving data from single-node to cluster requires re-ingestion” GPT-6 Luna · negative prompt · soft negative
“not ideal for logs, events, or diversified time series data” Claude Haiku 4.5 · negative prompt · soft negative
“Choose established options like Prometheus, VictoriaMetrics, or ClickHouse for most use cases” MiniMax M2.5 · negative prompt · first choice
“Often praised as a drop-in Influx replacement—stable, efficient, Prometheus-compatible, and scalable.” Grok 4.20 · negative prompt · first choice
“Monitoring / long-term metrics with minimal operational burden and storage cost → VictoriaMetrics.” Muse Glimmer 30B · budget prompt · first choice
Comparisons are drawn for the top eight products in each category, each against each. The output is the models' output; nothing here is a recommendation by the index.