Google Gemini 3.5 Flash (1M) vs Gemini 3.1 Flash
Side-by-side comparison of pricing, context window, capabilities, and a real-cost sample workload.
Option A
Google Gemini 3.5 Flash (1M)
by Google
Context: 1M
Input: $1.50 / 1M
Output: $9.00 / 1M
Released: 2026-05
Option B
Gemini 3.1 Flash
by Google
Context: 1M
Input: $0.35 / 1M
Output: $1.40 / 1M
Released: 2026-03
Detailed Comparison
| Dimension | Google Gemini 3.5 Flash (1M) | Gemini 3.1 Flash |
|---|---|---|
| Provider | ||
| Context window TieTie | 1M | 1M |
| Input price ($/1M) Winner | $1.50 | $0.35 |
| Output price ($/1M) Winner | $9.00 | $1.40 |
| Sample workload cost Winner 1M input + 500K output tokens
|
$6.00 | $1.05 |
| Released Winner | 2026-05 | 2026-03 |
| Tokenizer | gemini | gemini |
The verdict
On the dimensions we measured, Gemini 3.1 Flash wins more often - particularly on cost-effectiveness for a typical 1M+0.5M workload.
Google Gemini 3.5 Flash (1M) - Key features
Google's newest fast model (Google I/O 2026), built for agentic tasks and coding - beats Gemini 3.1 Pro on coding and agentic benchmarks at a fraction of the cost.
- Fast, low-cost agentic model
- 1M token context window
- Strong coding benchmarks
- Multimodal input
- Great price/performance
Gemini 3.1 Flash - Key features
Fast and affordable Gemini 3.1 Flash optimized for high-throughput applications at minimal cost.
- 1M context window
- Ultra-fast inference
- Very low cost
- Good reasoning
- High throughput
How to choose
- Pick the cheaper model if your workload is mostly straightforward classification, extraction, or summarization.
- Pick the bigger context if you process long documents, large codebases, or multi-document research.
- Pick the more recent release if you need state-of-the-art reasoning quality and don't mind paying a bit more.
- Use both via a routing layer - send simple tasks to the cheaper one and complex tasks to the smarter one. This is the highest-ROI optimization in production AI.
Estimate the real cost of either model for your prompts using our Token Calculator.