Written by Jakub Rusinowski · Last updated June 26, 2026
Condensing long inputs — transcripts, threads, reports — into accurate short output.
Top pick: Gemma 4 31B
Scores 94.6/100 for summarization. 31B parameters, needing about 19.5 GB at Q4_K_M, 250K context, Apache-2.0.
| Model | Score | Params | Context | Licence | Quality index |
|---|---|---|---|---|---|
| 1. Gemma 4 31B | 94.6 | 31B | 250K | Apache-2.0 | — (estimated) |
| 2. Qwen 3.6 27B | 93.3 | 28B | 256K | Apache-2.0 | — (estimated) |
| 3. Qwen 3.5 14B | 92.3 | 14B | 125K | Apache 2.0 | — (estimated) |
| 4. Qwen 3.5 32B | 92.3 | 32B | 125K | Apache 2.0 | — (estimated) |
| 5. Gemma 4 12B | 92 | 12B | 125K | Gemma License (commercial OK) | — (estimated) |
| 6. Qwen 3.7 35B-A3B | 91.7 | 35B | 256K | Apache-2.0 | — (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-4.7 9B (90.1) Qwen 3 8B (89.8) GLM-6 9B (89.8) |
| 12 GB | RTX 3060 12 GB, RTX 5070 | Qwen 3.5 14B (93.2) Gemma 4 12B (92.8) Qwen 3 14B (92.4) |
| 16 GB | RTX 5080, RTX 4080, RX 9070 XT | Qwen 3.5 14B (92.8) Mistral Small 3.1 24B (92.6) Qwen 3 14B (92.4) |
| 24 GB | RTX 4090, RTX 3090, RX 7900 XTX | Gemma 4 31B (98.1) Qwen 3.6 27B (96.8) Qwen 3.5 32B (95.8) |
| 48 GB | RTX 6000 Ada, MacBook Pro M4 Max 48 GB | GLM-5.1 72B (97.4) Qwen 2.5 VL 72B Instruct (95.4) Gemma 4 31B (95.2) |
| 128 GB+ | Mac Studio, DGX Spark, multi-GPU | Qwen 3.5 122B-A10B (95.6) Qwen 3.5 122B-A10B (MoE) (93.8) GLM-5.1 72B (93.6) |
Reasoning-led but with a real creative weight (35%), because a summary is judged on readability as well as coverage. The low quality floor (45) reflects that summarization is the workload small models handle best relative to their size.
Worked example — Gemma 4 31B: capability 92.8 × 0.401, quality 91.7 × 0.236, context 100 × 0.153, license 100 × 0.032, accessibility 80 × 0.177 + 3 tag bonus (long-context).
Requirements applied: context floor 32,768 tokens (ideal 131,072), quality floor 40, licence weight 0.3, latency weight 0.5.
Gemma 4 31B, scoring 94.6/100 against this workload's published requirements. 111 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.
Reasoning-led but with a real creative weight (35%), because a summary is judged on readability as well as coverage. The low quality floor (45) reflects that summarization is the workload small models handle best relative to their size.