Gemini 3.6 Flash
Released 21 Jul 2026$0.75 input, $3.75 output per million tokens1M contextproprietary
3 results on 3 of 13 boards, latest measured Sep 2026. Compare with other models
Results
Each line sits on its own board's scale. Tall tick: the board leader.
Clinical reasoning and knowledge
- 59.3Rank 3 of 8 here, 11 models on the boardLeader GPT-5.6 Sol 60.2
Documentation and coding
- MedScribe (Vals AI)model ID google/gemini-3.6-flash; temperature=1; max_output_tokens=30000; reasoning_effort=high79.66Rank 58 of 105Leader Claude Opus 5.5 91.43
- 53.15Rank 10 of 103Leader Claude Opus 5 63.57
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)79.6679.7×0.25, near its ceilingNear its ceiling: leader 91.4, weight 0.25model ID google/gemini-3.6-flash; temperature=1; max_output_tokens=30000; reasoning_effort=high
- MedCode (Vals AI)53.1553.2×0.911
- MAST (Medical AI Superintelligence Test)59.359.3×0.995
- Weighted average of 3 boards
- 59.1
- Multiplier for 3 of 10 boards
- × 0.707
- Index
- 59.1 × 0.707 = 41.8
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
58MedScribe (Vals AI) gemini-3.6-flash 79.66
Printed as 79.66%Official leaderboard, measured Sep 2026Configuration: model ID google/gemini-3.6-flash; temperature=1; max_output_tokens=30000; reasoning_effort=highVals AI MedScribe leaderboard official leaderboard, Vals AI, 3 Sep 2026. Vals AI MedScribe leaderboard, View: All Models, Task: Overall, row 58 of 105 (Gemini 3.6 Flash), Accuracy column; Updated 9/29/2026. Rendered BenchmarkView table; configuration from embedded astro-island BenchmarkView props, benchmarkView.default.tasks.overall["google/gemini-3.6-flash"].58 | Gemini 3.6 Flash | 79.66%±1.86 | $1.5/$7.5 | 33.55s
Every result from this document10MedCode (Vals AI) 53.15
Printed as 53.15%Official leaderboard, measured Sep 2026Vals AI MedCode leaderboard official leaderboard, Vals AI, 29 Sep 2026. MedCode leaderboard, Overall task, All Models expanded, rank 10 of 103; Accuracy column; board Updated 9/29/2026. Rendered table row; configuration from embedded BenchmarkView props benchmarkView.default.tasks.overall["google/gemini-3.6-flash"].10 | Gemini 3.6 Flash | 53.15%±2.16 | $1.5/$7.5 | 16.68s
Every result from this document3MAST (Medical AI Superintelligence Test) 59.3
Printed as 59.3%Official leaderboard, measured Aug 2026MAST: Medical AI Superintelligence Test leaderboard (General board) official leaderboard, ARISE AI Research Network, 15 Aug 2026. arise-ai.org/mast, 'Which AI can you trust for medical questions?' General tab, composite score table (8 of 11 models shown; 'Last updated August 15, 2026')3 Gemini 3.6 Flash Google 59.3%
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 3.7 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