Clinical Benchmarks
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Gemini 3.5 Flash

Released 19 May 2026$1.5 input, $9 output per million tokens1M contextproprietary

3 results on 3 of 13 boards, latest measured Sep 2026. Compare with other models

Clinical Benchmarks Index43.8Rank 22 of 1493 of 10 boardsAverage × multiplier 62.0 × 0.707 = 43.8This site’s own calculation. Working How it is computed

Results

Each line sits on its own board's scale. Tall tick: the board leader.

Clinical reasoning and knowledge

  1. MedHELM
    0.642
    Rank 2 of 10 here, 11 models on the boardLeader Gemini 3.1 Pro (Preview) 0.652
    Official leaderboard
    Measured May 2026

Documentation and coding

  1. MedScribe (Vals AI)
    model ID google/gemini-3.5-flash; temperature=1; max_output_tokens=30000; reasoning_effort=high
    76.57
    Rank 71 of 105Leader Claude Opus 5.5 91.43
    Official leaderboard
    Measured Sep 2026
  2. MedCode (Vals AI)
    55.83
    Rank 5 of 103Leader Claude Opus 5 63.57
    Official leaderboard
    Measured Sep 2026

Index working

The Clinical Benchmarks Index is this site's own calculation: a weighted average of the board scores times a multiplier for the boards behind it. How it is computed

  • MedScribe (Vals AI)76.5776.6×0.25, near its ceilingNear its ceiling: leader 91.4, weight 0.25model ID google/gemini-3.5-flash; temperature=1; max_output_tokens=30000; reasoning_effort=high
  • MedCode (Vals AI)55.8355.8×0.911
  • MedHELM0.64264.2×0.87
Weighted average of 3 boards
62.0
Multiplier for 3 of 10 boards
× 0.707
Index
62.0 × 0.707 = 43.8

Score: the result on the board’s own 0 to 100 scale. Dot: this model; tick: the board’s leader. Weight: how much the board counts in the weighted average; bold where the board is near its ceiling and counts for less. Each result links to its quote and page; the line under it is the setting it was measured with.

Sources

The quote, page and document for each of the 3 results
  1. 71MedScribe (Vals AI) gemini-3.5-flash 76.57
    Printed as 76.57%Official leaderboard, measured Sep 2026Configuration: model ID google/gemini-3.5-flash; temperature=1; max_output_tokens=30000; reasoning_effort=high
    Vals AI MedScribe leaderboard official leaderboard, Vals AI, 3 Sep 2026. Vals AI MedScribe leaderboard, View: All Models, Task: Overall, row 71 of 105 (Gemini 3.5 Flash), Accuracy column; Updated 9/29/2026. Rendered BenchmarkView table; configuration from embedded astro-island BenchmarkView props, benchmarkView.default.tasks.overall["google/gemini-3.5-flash"].
    71 | Gemini 3.5 Flash | 76.57%±1.92 | $1.5/$9 | 57.08s
    Every result from this document
  2. 5MedCode (Vals AI) 55.83
    Printed as 55.83%Official leaderboard, measured Sep 2026
    Vals AI MedCode leaderboard official leaderboard, Vals AI, 29 Sep 2026. MedCode leaderboard, Overall task, All Models expanded, rank 5 of 103; Accuracy column; board Updated 9/29/2026. Rendered table row; configuration from embedded BenchmarkView props benchmarkView.default.tasks.overall["google/gemini-3.5-flash"].
    5 | Gemini 3.5 Flash | 55.83%±2.11 | $1.5/$9 | 25.29s
    Every result from this document
  3. 2MedHELM 0.642
    Printed as 0.642Official leaderboard, measured May 2026
    MedHELM leaderboard (medhelm.org), v5.0.0 official leaderboard, Stanford CRFM (MedHELM), 14 May 2026. medhelm.org home, 'Current leaders Mean win rate v5.0.0' table ('10 of 11 models · Updated 14 May 2026')
    2 Gemini-3.5-flash Google 0.642
    Every result from this document

Other Google models: Gemini 2.0 Flash, Gemini 2.5 Flash, Gemini 2.5 Flash (7/17), Gemini 2.5 Flash Lite, Gemini 2.5 Flash Lite (9/25), Gemini 2.5 Flash Preview (9/25), Gemini 2.5 Pro, Gemini 3.1 Flash Lite Preview, Gemini 3.1 Pro, Gemini 3.5 Flash Lite, Gemini 3.6 Flash, Gemini 3.7 Flash, Gemini 3.8 Flash, Gemini 3 Flash, Gemini 3 Pro, Gemini 3 Pro (11/25), Gemma 3 12B, Gemma 3 27B, Gemma 4 12B, Gemma 4 26B A4B, Gemma 4 31B, Gemma 4 E2B, Gemma 4 E4B, MedGemma 27B Text, MedGemma 4B