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Field Note 05ModelsExperiment · 10 Oct 2026

Bigger did not always answer better.

A non-thinking Qwen3.5 comparison against Qwen3-8B and Gemma 3 4B, including a separate sampling check.

A camera, speech bubble and code brackets orbiting a compact compute cube.
182measured requests in the main run
Exploratory benchmark

First-party engineering evidence. Results apply to the tested configuration and sample; they are not an SLA or a general model ranking.

THE TAKEAWAY

Measure the task, not the parameter count.

What the replies revealed

Qwen3-8B was strongest on the seven English fixtures in the initial screen. Qwen3.5-9B recovered all five image tasks’ required facts, but contradicted its own English arithmetic. Qwen3.5-4B omitted delivery from an invoice calculation. Gemma 3 also made arithmetic and policy errors.

All 182 measured requests produced visible replies without truncation or transport errors. That did not make every reply correct.

Sampling is part of the result

The separate recommended-sampling check corrected some 9B English mistakes, but Swahili translations and clock conversions remained wrong. One corrected seeded answer does not establish reliability. Repeated identical greedy outputs are not independent quality samples.

Next experiment

The later seven-family study evaluated thinking-enabled profiles with different token budgets and one slot. Keep the two studies separate: merging their latency distributions would conceal the configuration change.

THE MEASUREMENTS

Results, in context.

Non-thinking profiles
Scroll sideways to compare all columns →
ModelEnglish median (s)Swahili median (s)Vision median (s)
Qwen3.5-4B1.101.601.86
Qwen3.5-9B1.691.083.35
Qwen3-8B1.010.85Not supported
Gemma 3 4B0.941.381.43

Readiness, download and separate cold text requests are excluded. Vision includes first-image initialization.

TEST SETUP

How we ran it.

  1. Cape Town T4, llama.cpp b11429, Q4_K_M and F16 vision projectors; two 4,096-token slots, four CPU threads, 768 output tokens, thinking disabled and initial temperature zero.
  2. Seven English, nine Swahili and five vision fixtures repeated twice, plus three concurrent pairs per model and four separate cold requests. Qwen3-8B is text-only. A separate Qwen sensitivity run used temperature 0.7, top-p 0.8, top-k 20 and seeds 42/43.
WHAT THIS DOES NOT PROVE

The limits matter.

  • Small diagnostic samples, not a production SLA or a population accuracy estimate. Different output lengths affect completion time.
  • Synthetic tasks assessed by assistant review; no native-speaker validation. First image calls remain in vision medians. All timings exclude public networking.
  • Qwen GGUFs were third-party Unsloth conversions; results do not describe original full-precision weights.
OPEN FINDINGS

Evidence & sources.

The public extract contains the methods, selected results and caveats in this note. It is a curated summary, not the full raw run. Fingerprints identify the retained source artifacts; gated audio and private operational records are not included.

Download results extract
Source artifact fingerprints · SHA-256
README.mdcc1dbb7d1f411399950dba8846064352c4ae5cd17183f8a43fc65f2f4d382953
summary.jsone01e0d924ff6b2c34e8d77e41444da2c9f264edcee77628122c92d4db34472f3
sampled-results.jsonb683387ca29aeb8871e55d83ed5369686fbf3582006cecac96c377b0b3610e11

Suggested citation: Embiro Labs Research. “Bigger did not always answer better.” Published 12 Oct 2026. embiro.ai/research/qwen35-small-multimodal

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