LLM Comparison
Apodex 1.1 vs TimesFM 2.5
Side-by-side specs, pricing & capabilities · Updated August 2026
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2/6 modelsSame tier:
| Organization | ||
| OpenTools Score | 24 | |
| Family | Apodex | TimesFM |
| Status | Current | Current |
| Release Date | Aug 2026 | Sep 2025 |
| Context Window | 262K tokens | 16K tokens |
| Input Price | Free | Free |
| Output Price | Free | Free |
| Pricing Notes | The official API campaign currently sets input, cached input, and output to $0.00 per 1M tokens. Published list prices are $0.30 input, $0.03 cached input, and $3.00 output per 1M tokens; requests above 200K input tokens are charged at twice the listed rates when paid pricing resumes. Apodex has not published the campaign end date, so pricing requires daily revalidation. | Open-source (Apache 2.0). Free to download and run locally. Enterprise options via BigQuery ML and Vertex AI with their respective pricing. |
| Capabilities | textreasoningcodelong-contextstreaming | textextended-thinking |
| Max Output | 262K tokens | 1K tokens |
| API Identifier | apodex-1.1 | google/timesfm-2.5-200m-pytorch |
| Benchmarks | ||
| Artificial Analysis Intelligence Index v4.1.1 | 44artificial-analysis | — |
| Monash Archive | — | 85google-research |
| ETT Long-horizon | — | 78google-research |
| View Apodex 1.1 | View TimesFM 2.5 | |
Cost Calculator
Enter your expected monthly token usage to compare costs.
| Model | Input | Output | Total / mo | vs Best |
|---|---|---|---|---|
| Apodex 1.1Cheapest | $0.00 | $0.00 | $0.00 | — |
| TimesFM 2.5Cheapest | $0.00 | $0.00 | $0.00 | — |
Apodex
Apodex 1.1
Apodex 1.1 is a proprietary long-context reasoning model for complex professional work, research, coding, and evidence-heavy analysis through a first-party API.
TimesFM 2.5
TimesFM (Time Series Foundation Model) is a pretrained decoder-only foundation model by Google Research for time-series forecasting. It uses a patched-decoder attention architecture where patches of time-points are treated as tokens, enabling zero-shot forecasting across domains and temporal granularities from minutes to yearly.
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