Gemini 3.8 Flash leads on the shared-benchmark average
Ahead on 2 of 3 shared benchmarks, averaging 11.0 points higher
Summarised only from the 3 percentage-scale benchmarks scored by every selected model; details are below. A further 1 Elo/rating-scale benchmarks are left out of the average — their scale cannot be added to percentages.
“Best available” takes each model’s highest recorded non-parallel mode per benchmark, so it may combine modes into a virtual configuration that does not exist. Read it with the mode breakdown.

Gemini 3.6 Flash
Google Deep Mind
Benchmark-by-benchmark comparison. Changing the thinking mode or tool filters updates the chart and table below.
“Best available” picks the highest non-parallel mode separately for each benchmark. The resulting series can combine several reasoning levels and is not one reproducible runtime configuration. Choose a mode filter to compare like-for-like runs.
Data sourced primarily from official releases (GitHub, Hugging Face, papers), then benchmark leaderboards, then third-party evaluators. Learn about our data methodology
Each axis is the mean percentage score of one benchmark domain. It is an average, not a capability rating.
Relative edge: 多模态理解 +1.1 / Relative gap: 编程与软件工程 -24.7
Relative edge: 编程与软件工程 +24.7 / Relative gap: 多模态理解 -1.1
Method: for each model and benchmark, all scores in the current mode scope are averaged (not the best score), then those benchmark scores are averaged within each domain. Only benchmarks scored on a 0-100 scale by at least two of the selected models count — Elo and rating-scale benchmarks such as Codeforces or Arena are excluded, because averaging a 1500 rating with an 85% accuracy produces a meaningless number. Missing values are not counted as zero, and the averages are unweighted, so domains with harder benchmarks read lower.
Every model and runtime mode, benchmark by benchmark. Values are comparable along a row, not between different benchmarks.
4 benchmarks with comparable scores. Each model shows its best score; mode label is displayed below.
| Benchmark | Gemini 3.6 Flash | Gemini 3.8 Flash |
|---|---|---|
DeepSWE 编程与软件工程 | 49.00Thinking Enabled | Tools | 73.70Thinking Enabled | Tools |
Terminal-Bench 2.1 AI Agent - 工具使用 | 78.00Thinking Enabled | Tools | 89.40Thinking Enabled | Tools |
GDPval-AA v2 生产力知识 | 1421.00Thinking Enabled | 1545.00Thinking Enabled |
CharXiv RQ 多模态理解 | 89.40Thinking Enabled | Tools | 86.20Thinking Level · Medium |
Official list prices per model API, split by input and output. Unit: USD per 1M tokens.
Architecture, licensing and API modalities. "Not provided" means the field is missing from our database.
| Features & specs | Gemini 3.6 FlashGoogle Deep Mind | Gemini 3.8 FlashGoogle Deep Mind |
|---|---|---|
Core specsRelease | 2026-07-21 | 2026-09-02 |
Context length | 1M | 1M |
Max output length | 65,536 tokens | 65,536 tokens |
Architecture | Undisclosed | Undisclosed |
Runtime modes | 中高 | 低中高 |
Availability & licensingCode availability | Not public | Not public |
Weight availability | Not public | Not public |
Use & commercial terms | Official service only; subject to provider terms | Official service only; subject to provider terms |
API modality supportText Input/Output | Input:YesOutput:Yes | Input:YesOutput:Yes |
Image Input/Output | Input:YesOutput:No | Input:YesOutput:No |
Audio Input/Output | Input:YesOutput:No | Input:YesOutput:No |
Video Input/Output | Input:YesOutput:No | Input:YesOutput:No |
ResourcesPaper / report | Gemini 3.6 Flash | Gemini 3.8 Flash evaluation methodology |

Gemini 3.8 Flash
Google Deep Mind