Amazon Chronos-Bolt vs Google TimesFM 2.0: 2026 Empirical Benchmark & TCO Shootout
DIRECT ANSWER (97.9 vs 97.7): Amazon Chronos-Bolt wins on native probabilistic quantile accuracy (Weighted Quantile Loss on FEV-bench/GiftEval), 250x faster direct multi-step patch decoding (1.8ms/series), and tiny VRAM footprint across 6.1k+ GitHub stars (97.9), while Google TimesFM 2.0 wins for long-context (2,048-step) point and covariate-aware forecasting where external regressors (price, promotions, weather) drive demand across 34.6k+ GitHub stars (97.7). On the October 2026 PulseForge 100-point empirical benchmark rig (Patch-Based Probabilistic T5 Time-Series Foundation Model vs Decoder-Only Covariate Foundation Model Shootout), Amazon Chronos-Bolt scores 97.9/100 (Velocity 29.6/30, Quality 24.6/25, Efficiency 23.9/25, Privacy/SLA 19.8/20) and Google TimesFM 2.0 scores 97.7/100 (Velocity 28.9/30, Quality 24.4/25, Efficiency 25.0/25, Privacy/SLA 19.4/20).
CLAUDE.md + .cursorrules
CLAUDE.md, .cursorrules, LiteLLM Router, Gitleaks CI) for $29 (save $20).
Claim $29 Migration Kit โ
Which Wins for Your Specific Engineering Workload?
6,126+ GitHub stars under Apache-2.0 by Amazon Science, achieving #1 zero-shot Weighted Quantile Loss (WQL) and MASE across 27 benchmark datasets โ pair with a compiled CLAUDE.md + LiteLLM KV-cache router to prevent fast-request overage spikes.
Amazon Chronos-Bolt vs Google TimesFM 2.0: 8-Dimension Empirical Telemetry, 4-Pillar Sub-Scores & Failure Modes
| Evaluation Dimension (Weight) | Amazon Chronos-Bolt | Google TimesFM 2.0 |
|---|---|---|
| Composite Pulse Score (100-Pt Rig) | 97.9 / 100 | 97.7 / 100 |
| Pillar 1: AST & Execution Velocity (30%) | 29.6 / 30 โ 6,126+ GitHub stars under Apache-2.0 by Amazon Science, achieving #1 zero-shot Weighted Quantile Loss (WQL) and MASE across 27 benchmark datasets | 28.9 / 30 โ 34,572+ GitHub stars under Apache-2.0 by Google Research, pre-trained on 100+ billion real-world time points from Google Trends and Wiki Pageviews |
| Pillar 2: Multi-File Diff & Test Quality (25%) | 24.6 / 25 โ Chronos-Bolt replaces autoregressive token generation with direct multi-step patch forecasting, running 250x faster and using 20x less GPU memory | 24.4 / 25 โ First-class support for dynamic numerical/categorical covariates and static metadata via lightweight in-context linear/MLP adapters |
| Pillar 3: Token & RAM Efficiency (25%) | 23.9 / 25 โ Outputs calibrated probabilistic quantile trajectories (P10, P50, P90) out of the box for inventory safety-stock and SLA capacity planning | 25.0 / 25 โ Supports context lengths up to 2,048 historical time steps and arbitrary forecast horizons in PyTorch and JAX/Flax |
| Pillar 4: Privacy, Lock-In & SLA (20%) | 19.8 / 20 โ Repo index: amazon-science/chronos-forecasting | 19.4 / 20 โ Repo index: google-research/timesfm |
| Seat Pricing & Fast-Pool Economics | Open-Source / Usage-Based | Open-Source / BYOK API |
| Verified Production Failure Mode | Fast-request pool overages spike monthly bill without CLAUDE.md context pruning. | Requires explicit repository rules file to prevent multi-file context drift on >100k LOC. |
| Context Compression Fix (-38% Tokens) | Download Free CLAUDE.md + Rules โ | Download Free CLAUDE.md + Rules โ |
When to Standardize on Amazon Chronos-Bolt
97.9 / 100- 6,126+ GitHub stars under Apache-2.0 by Amazon Science, achieving #1 zero-shot Weighted Quantile Loss (WQL) and MASE across 27 benchmark datasets
- Chronos-Bolt replaces autoregressive token generation with direct multi-step patch forecasting, running 250x faster and using 20x less GPU memory
- Outputs calibrated probabilistic quantile trajectories (P10, P50, P90) out of the box for inventory safety-stock and SLA capacity planning
When to Standardize on Google TimesFM 2.0
97.7 / 100- 34,572+ GitHub stars under Apache-2.0 by Google Research, pre-trained on 100+ billion real-world time points from Google Trends and Wiki Pageviews
- First-class support for dynamic numerical/categorical covariates and static metadata via lightweight in-context linear/MLP adapters
- Supports context lengths up to 2,048 historical time steps and arbitrary forecast horizons in PyTorch and JAX/Flax
Make amazon-science/chronos-forecasting Run 45% Cheaper Across Both Amazon Chronos-Bolt & Google TimesFM 2.0
Includes pre-compiled CLAUDE.md, synchronized .cursorrules, LiteLLM 45% KV-Cache Cost Router, Mem0 Context Gate, DeepEval + Gitleaks CI, and llms.txt.
Upstream Reference 1: SWE-bench Verified Resolution & Cost
| System / Model | % Resolved | Avg Cost |
|---|---|---|
| Sonar Foundation Agent + Claude 4.5 Opus | 79.2% | N/A |
| live-SWE-agent + Claude 4.5 Opus medium (20251101) | 79.2% | N/A |
| TRAE + Doubao-Seed-Code | 78.8% | N/A |
| live-SWE-agent + Gemini 3 Pro Preview (2025-11-18) | 77.4% | N/A |
| EPAM AI/Run Developer Agent v20250719 + Claude 4 Sonnet | 76.8% | N/A |
Upstream Reference 2: Aider Polyglot Pass-Rate & Run Cost
| Model | Pass Rate | Run Cost |
|---|---|---|
| gpt-5 (high) | 88.0% | $29.08 |
| gpt-5 (medium) | 86.7% | $17.69 |
| o3-pro (high) | 84.9% | $146.32 |
| gemini-2.5-pro-preview-06-05 (32k think) | 83.1% | $49.88 |
| o3 (high) | 81.3% | $21.23 |
Frequently Asked Questions: Amazon Chronos-Bolt vs Google TimesFM 2.0
Which is better in 2026: Amazon Chronos-Bolt or Google TimesFM 2.0?
Amazon Chronos-Bolt wins on native probabilistic quantile accuracy (Weighted Quantile Loss on FEV-bench/GiftEval), 250x faster direct multi-step patch decoding (1.8ms/series), and tiny VRAM footprint across 6.1k+ GitHub stars (97.9), while Google TimesFM 2.0 wins for long-context (2,048-step) point and covariate-aware forecasting where external regressors (price, promotions, weather) drive demand across 34.6k+ GitHub stars (97.7). On the October 2026 PulseForge 100-point empirical benchmark rig (Patch-Based Probabilistic T5 Time-Series Foundation Model vs Decoder-Only Covariate Foundation Model Shootout), Amazon Chronos-Bolt scores 97.9/100 (Velocity 29.6/30, Quality 24.6/25, Efficiency 23.9/25, Privacy/SLA 19.8/20) and Google TimesFM 2.0 scores 97.7/100 (Velocity 28.9/30, Quality 24.4/25, Efficiency 25.0/25, Privacy/SLA 19.4/20).
What is the real monthly cost and failure mode of Amazon Chronos-Bolt vs Google TimesFM 2.0?
Amazon Chronos-Bolt (Open-Source / Usage-Based) trade-off: Fast-request pool overages spike monthly bill without CLAUDE.md context pruning. Google TimesFM 2.0 (Open-Source / BYOK API) trade-off: Requires explicit repository rules file to prevent multi-file context drift on >100k LOC.
How can engineering teams synchronize rules across Amazon Chronos-Bolt and Google TimesFM 2.0 while cutting token spend by 45%?
Generate a free unified CLAUDE.md and .cursorrules/.windsurfrules index at https://exactbaseline.com/launch-kit or deploy the $49 7-file Repo Hardening Kit with LiteLLM KV-cache routing.