Qwen3.5 4B — Benchmarks
Benchmark scores for Qwen3.5 4B aggregated from public leaderboards (3 boards), with how it ranks among open models and how it compares with its nearest neighbours. See hardware requirements for what you need to run it.
- #64 of 106 open models
- #12 of 36 that fit 24 GB
- 3 boards · updated Nov 2026
- 100 = average open model
- Hardware requirements →
Qwen3.5 4B reasoning benchmarks
Multi-step logic and problem-solving — can the model think through a hard problem rather than recall an answer.
Qwen3.5 4B math benchmarks
Symbolic and quantitative problem-solving, from competition math to multi-step calculation.
55.8%
55.8%
median 54.4%open leader 99.4%
Overall best: Claude Fable 5 (high) 100.0%
52nd percentile · #43 of 88 open · #185 of 292 all
IndependentEpoch AI ·
Qwen3.5 4B knowledge benchmarks
Breadth and depth of factual knowledge across many subjects, paired with the reasoning to apply it.
Score vs VRAM in the 24 GB tier
- Qwen3.5 4B
- Other models
- Best Score for its VRAM
#12 of 36 that fit 24 GB
Each dot is a model at the best quant that fits a 24 GB GPU. The line steps up to the best Score available at each amount of VRAM; a dot below it is beaten by a model that needs no more memory.
Full 24 GB ranking →Qwen3.5 4B vs its neighbours
Same boards for the nearest open models per llmrun Score and the best model that fits 24 GB. The highest score in each row is marked.
| Board | Qwen3.5 4Bthis model | Qwen3.8 27Bbest in classQ4_K_M · 18 GB | Qwen3.5 9Bnearest by ScoreQ4_K_M · 6.6 GB | Qwen2.5 7B Instructnearest by ScoreQ4_K_M · 5.8 GB | Gemma 3 4B ITnearest by ScoreQ4_K_M · 5.0 GB |
|---|---|---|---|---|---|
| llmrun Score | 98.5 | 122.0 | 99.4 | 65.1 | 64.0 |
| CritPt | 0.0% | 5.4% | 0.6% | — | — |
| AIME 2024/2025 | 55.8% | — | 61.7% | 2.5% | 7.5% |
| MMLU-Pro | 79.1% | — | 82.5% | — | 43.6% |
| Compare head to head | Compare | Compare | Compare | Compare |
Scores aggregated from public benchmark sources (Epoch AI: 2 boards, TIGER-Lab: 1 board) — each linked from the benchmark pages. llmrun does not run these benchmarks. Percentiles and ranks are among open models in the llmrun catalogue; the median and leader marks refer to the same field.