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Test 2 — Grain Robustness Results

Test 2 measures how closed-loop accuracy changes when deterministic monochrome Gaussian grain is added to the benchmark images. The evaluation uses the fixed 680-image core subset: 20 images from each of 34 domains, with five questions per image, giving 3,400 evaluated question rows per model at each noise level.

Noise conditions

  • σ = 0: original image, without added grain.
  • σ = 15, 25, 40: one deterministic monochrome Gaussian-noise field is added equally to all three RGB channels.
  • Every model receives the same altered pixels for a given image and noise level.
  • Δ is the change from σ = 0 to σ = 40. Negative values indicate an accuracy loss under the strongest tested grain.

Accuracy at each noise level (%)

Rank at σ=0 Model σ=0 σ=15 σ=25 σ=40 Δ
1 Gemini 3.8 Flash 64.1 65.5 64.7 64.0 −0.1
2 Claude Opus 5 62.4 59.9 58.6 56.2 −6.1
3 Grok 4.6 52.7 51.2 51.3 50.4 −2.4
4 GPT-5.6 Sol 50.8 50.9 51.0 49.8 −1.1
5 Claude Sonnet 5 49.7 48.4 47.6 47.1 −2.6
6 Muse Glimmer 30B 44.4 42.4 43.6 42.4 −1.9
7 DeepSeek V4.1 Flash 43.2 39.3 40.1 39.1 −4.1
8 GPT-5.6 Luna 42.4 41.8 42.0 40.3 −2.1
9 Perplexity Sonar Pro 41.8 41.4 40.9 39.4 −2.4
10 Inkling 38.7 36.8 35.7 34.4 −4.3

Main findings

  • Gemini 3.8 Flash is both the most accurate model and the most robust to added grain. Its reported change at σ = 40 is only −0.1 percentage points.
  • GPT-5.6 Sol has the second-smallest degradation at −1.1 points.
  • Claude Opus 5 experiences the largest reported degradation, falling from 62.4% to 56.2%, with a reported Δ of −6.1 points.
  • Inkling and DeepSeek V4.1 Flash also show comparatively large losses of −4.3 and −4.1 points, respectively.
  • Small non-monotonic changes at intermediate noise levels are visible for several models. For example, Gemini improves at σ = 15, while GPT-5.6 Sol and Muse Glimmer 30B partially recover at σ = 25.
  • The ranking is broadly stable under the strongest grain: Gemini remains first, Claude Opus remains second, and Inkling remains last.

Rounding note

The values above are transcribed from the supplied result table. Accuracy columns and Δ were calculated from underlying unrounded values before display, so subtracting the displayed one-decimal endpoints may differ from the displayed Δ by 0.1 point for some models.