Written by Jakub Rusinowski · Last updated August 15, 2026
Multi-step autonomous loops where the model calls tools, reads results and decides what to do next.
Top pick: Ternary Bonsai 27B
Scores 91.7/100 for autonomous agents and tool use. 27B parameters, needing about 17.1 GB at Q4_K_M, 256K context, Apache 2.0.
| Model | Score | Params | Context | Licence | Quality index |
|---|---|---|---|---|---|
| 1. Ternary Bonsai 27B | 91.7 | 27B | 256K | Apache 2.0 | — (estimated) |
| 2. Inkling (NVFP4) | 88.5 | 1000B | 977K | Apache 2.0 | — (estimated) |
| 3. GLM-4.7-Flash 30B-A3B | 88.2 | 30B | 193K | MIT | — (estimated) |
| 4. Poolside Laguna XS 2.1 Laguna XS 2.1 33B-A3B | 88.1 | 33B | 256K | OpenMDW-1.1 | — (estimated) |
| 5. Kimi K2.5 | 88 | 32B | 125K | Kimi License (research) | — (estimated) |
| 6. Kimi K2.5 1T | 87.9 | 1000B | 250K | Open-weight | — (estimated) |
The strongest model overall is rarely the right answer — what matters is the strongest model that fits the memory you have. These picks are re-ranked per tier, so each one uses its budget rather than simply being small.
| Memory | Typical hardware | Recommended models |
|---|---|---|
| 8 GB | RTX 4060, RTX 3070, base MacBook Air | GLM-6 9B (86.8) GLM-5 9B (85.4) Llama 3.1 8B Instruct (75.4) |
| 12 GB | RTX 3060 12 GB, RTX 5070 | GLM-6 9B (86) GLM-5 9B (84.6) Qwen 3.5 14B (84.6) |
| 16 GB | RTX 5080, RTX 4080, RX 9070 XT | GLM-6 9B (84.7) Qwen 3.5 14B (84.4) GLM-5 9B (83.4) |
| 24 GB | RTX 4090, RTX 3090, RX 7900 XTX | Ternary Bonsai 27B (94) GLM-4.7-Flash 30B-A3B (90.4) Poolside Laguna XS 2.1 Laguna XS 2.1 33B-A3B (90.4) |
| 48 GB | RTX 6000 Ada, MacBook Pro M4 Max 48 GB | Ternary Bonsai 27B (91.6) Poolside Laguna XS 2.1 Laguna XS 2.1 33B-A3B (88.7) Kimi K2.5 (88.5) |
| 128 GB+ | Mac Studio, DGX Spark, multi-GPU | Ternary Bonsai 27B (89) Qwen 3.5 122B-A10B (MoE) (87.5) Nemotron 3 Super 120B-A12B (86.6) |
Reliable structured tool calls are a hard gate here, not a bonus: a model with no tool-use tag is excluded regardless of score. Coding weight is high (40%) because tool arguments are effectively code. Context needs match RAG — an agent loop accumulates every observation it has ever seen.
Worked example — Ternary Bonsai 27B: capability 89.3 × 0.427, quality 87.7 × 0.251, context 100 × 0.163, license 70 × 0.045, accessibility 80 × 0.113 + 3 tag bonus (agents).
Requirements applied: context floor 32,768 tokens (ideal 262,144), quality floor 60, licence weight 0.4, latency weight 0.6. Tool-calling capability is required.
Ternary Bonsai 27B, scoring 91.7/100 against this workload's published requirements. 30 models qualified.
A credible answer starts at 8 GB of memory. Larger budgets unlock materially stronger models — the table above lists the best pick at each tier.
Reliable structured tool calls are a hard gate here, not a bonus: a model with no tool-use tag is excluded regardless of score. Coding weight is high (40%) because tool arguments are effectively code. Context needs match RAG — an agent loop accumulates every observation it has ever seen.