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Gemini 3.8 Flash

Released 2 Sep 2026$0.75 input, $3.75 output per million tokens1M contextproprietaryAlso written as gemini-3.8-flash

2 results on 2 of 13 boards, latest measured Sep 2026. Compare with other models

Clinical Benchmarks Index32.3Rank 47 of 1492 of 10 boardsAverage × multiplier 55.9 × 0.577 = 32.3This site’s own calculation. Working How it is computed

Results

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

Documentation and coding

  1. MedScribe (Vals AI)
    model ID google/gemini-3.8-flash; temperature=1; max_output_tokens=65536; reasoning_effort=high
    84.50
    Rank 32 of 105Leader Claude Opus 5.5 91.43
    Official leaderboard
    Measured Sep 2026
  2. MedCode (Vals AI)
    model ID google/gemini-3.8-flash; reasoning_effort=high; temperature=1; max_output_tokens=65536
    48.13
    Rank 28 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)84.5084.5×0.25, near its ceilingNear its ceiling: leader 91.4, weight 0.25model ID google/gemini-3.8-flash; temperature=1; max_output_tokens=65536; reasoning_effort=high
  • MedCode (Vals AI)48.1348.1×0.911model ID google/gemini-3.8-flash; reasoning_effort=high; temperature=1; max_output_tokens=65536
Weighted average of 2 boards
55.9
Multiplier for 2 of 10 boards
× 0.577
Index
55.9 × 0.577 = 32.3

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 2 results
  1. 32MedScribe (Vals AI) gemini-3.8-flash 84.50
    Printed as 84.50%Official leaderboard, measured Sep 2026Configuration: model ID google/gemini-3.8-flash; temperature=1; max_output_tokens=65536; reasoning_effort=high
    Vals AI MedScribe leaderboard official leaderboard, Vals AI, 3 Sep 2026. Vals AI MedScribe leaderboard, View: All Models, Task: Overall, row 32 of 105 (Gemini 3.8 Flash), Accuracy column; Updated 9/29/2026. Rendered BenchmarkView table; configuration from embedded astro-island BenchmarkView props, benchmarkView.default.tasks.overall["google/gemini-3.8-flash"].
    32 | Gemini 3.8 Flash | 84.50%±1.94 | $1.5/$7.5 | 21.14s
    Every result from this document
  2. 28MedCode (Vals AI) gemini-3.8-flash 48.13
    Printed as 48.13%Official leaderboard, measured Sep 2026Configuration: model ID google/gemini-3.8-flash; reasoning_effort=high; temperature=1; max_output_tokens=65536
    Vals AI MedCode leaderboard official leaderboard, Vals AI, 29 Sep 2026. MedCode leaderboard, Overall task, All Models expanded, rank 28 of 103; Accuracy column; board Updated 9/29/2026. Rendered table row; configuration from embedded BenchmarkView props benchmarkView.default.tasks.overall["google/gemini-3.8-flash"].
    28 | Gemini 3.8 Flash | 48.13%±2.18 | $1.5/$7.5 | 42.89s
    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, Gemini 3.5 Flash Lite, Gemini 3.6 Flash, Gemini 3.7 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