TL;DR: Gemini has two live generations in mid-2026. Gemini 3 is the frontier family (3.1 Pro, 3.5 Flash, 3 Flash, 3.1 Flash-Lite). Gemini 2.5 (Pro, Flash, Flash-Lite) is the cheaper, proven fallback. Prices run from $0.10 to $12 per million tokens.
Google ships Gemini in two live generations. Gemini 3 is the current frontier family. Gemini 2.5 is the older but still useful family. Inside each, the tiers follow the same names. Pro is the most capable. Flash is the fast, balanced pick. Flash-Lite is the cheapest. The flagship is Gemini 3.1 Pro, and the newest model is Gemini 3.5 Flash, which Google launched in May 2026.
Key takeaways
- Two live families: Gemini 3 (frontier) and Gemini 2.5 (cheaper, proven). Each has Pro, Flash, and Flash-Lite.
- Gemini 3.1 Pro is the flagship, with a 2M token context window, the largest in the lineup.
- Gemini 3.5 Flash is the newest model and beats 3.1 Pro on coding and agent tasks while costing less.
- Output prices range from $0.40 per million on 2.5 Flash-Lite to $12 per million on 3.1 Pro.
- The names are out of sync. Flash jumped to 3.5 while Pro stayed at 3.1, so read the version and the tier together.
The current Gemini models at a glance
Here are the main text and coding models you can call today. Google also ships image, video, audio, and embedding models, which we cover further down. Use this table as your reference. Everything below explains the details.
| Model | Family | Context | Price in / out (per 1M) | Best for |
|---|---|---|---|---|
| Gemini 3.1 Pro | Gemini 3 | 2M | $2 / $12 | The hardest reasoning, agents, and creative work |
| Gemini 3.5 Flash | Gemini 3 | 1M | $1.50 / $9 | Fast frontier coding and agents, the new default |
| Gemini 3 Flash | Gemini 3 | 1M | $0.50 / $3 | Frontier quality at lower cost, high volume |
| Gemini 3.1 Flash-Lite | Gemini 3 | 1M | $0.25 / $1.50 | The cheapest Gemini 3, simple high-volume tasks |
| Gemini 2.5 Pro | Gemini 2.5 | 1M | $1.25 / $10 | Proven deep reasoning, a solid fallback |
| Gemini 2.5 Flash | Gemini 2.5 | 1M | $0.30 / $2.50 | Low-cost reasoning at high volume |
| Gemini 2.5 Flash-Lite | Gemini 2.5 | 1M | $0.10 / $0.40 | The cheapest and fastest multimodal model |
Which Gemini model fits your team?
Pick your main use case to see the right model.
For chatbots, tagging, and routing at scale, Gemini 3.1 Flash-Lite or 2.5 Flash-Lite keep costs low. You only pay for Pro when a task truly needs it. See the cost of building this in-house on our developer rate card.
For everyday coding, reviews, and agent tasks, 3.5 Flash is fast and strong at $1.50/$9. Pair it with engineers who build with the Gemini API. Hire AI agent developers who use it daily.
For deep reasoning, long context, and creative work, the flagship earns its price. For the hardest science and research, Deep Think goes further. Strong AI and machine learning engineers get the most from it.
The model is the easy part. The hard part is the team that builds and ships with it. We match you with pre-vetted engineers in about 24 hours. Tell us what you need.
Two generations, explained in plain English
Google keeps two families live at once. Gemini 3 is the new frontier family. Gemini 2.5 is the older family that still works well and costs less. Each family uses the same three tier names. Pro is the most capable. Flash is the balanced everyday pick. Flash-Lite is the cheapest. Once you know the tier names, the lineup is simpler than it looks.
One thing trips up most teams. The version numbers are out of sync across tiers. The newest Flash is Gemini 3.5 Flash, but the top Pro is still Gemini 3.1 Pro. A higher number on Flash does not mean it sits above Pro on every task. Read the tier and the version together. When in doubt, Pro is the deepest thinker and Flash is the fast worker.
The Gemini 3 family
Gemini 3.1 Pro
Gemini 3.1 Pro is the flagship. It is built for complex problems, agent work, and creative tasks. It has the largest context window in the lineup at 2M tokens, which is roughly 1.5 million words. Pricing is $2 input and $12 output per million tokens for prompts up to 200K tokens. Above 200K, both input and output move to a higher long-context rate, so very large prompts cost more per token.
Gemini 3.1 Deep Think
Deep Think is a high-reasoning mode built on the 3.1 base. Google aims it at the hardest problems in science, research, and engineering. It thinks longer before it answers, which raises both quality and cost on tough tasks. Most teams will not need it for daily work. Reach for it when a normal Pro answer is not deep enough.
Gemini 3.5 Flash
Gemini 3.5 Flash is the newest model, launched in May 2026. Google built it for frontier coding and agent work at speed. It beats Gemini 3.1 Pro on several coding and agent benchmarks while running faster and costing less. It has a 1M token context window and costs $1.50 input and $9 output per million tokens. For most production coding work in the Gemini 3 family, this is now the model to start with.
Gemini 3 Flash and 3.1 Flash-Lite
Gemini 3 Flash gives you frontier-class quality at a lower price, at $0.50 input and $3 output per million tokens with a 1M context window. Gemini 3.1 Flash-Lite is the cheapest model in the Gemini 3 family, at $0.25 input and $1.50 output per million tokens. Use Flash-Lite for high-volume, simple tasks where speed and cost matter more than depth. Google has also said a Gemini 3.5 Pro is coming, so the family will keep growing.
The Gemini 2.5 family
The Gemini 2.5 family is older but still supported and widely used. It is a strong fallback when you want lower cost or a stable model you have already tested. Gemini 2.5 Pro offers deep reasoning at $1.25 input and $10 output per million tokens. Gemini 2.5 Flash is a low-cost reasoning model at $0.30 input and $2.50 output. Gemini 2.5 Flash-Lite is the cheapest and fastest model on this list, at $0.10 input and $0.40 output per million tokens.
All three have a 1M token context window. For many teams, Gemini 2.5 Flash and Flash-Lite still handle support bots, tagging, and simple extraction at a tiny cost. There is no rush to move every workload to Gemini 3. Move when the new family gives you a clear quality gain on that specific task.
Image, video, audio, and other models
Gemini is more than text. Google ships separate models for other media, billed their own way. Imagen 4 makes images. Veo 3.1 makes video and is priced per second. Lyria 3 makes music. Gemini also has in-model image generation through its Flash Image and Pro Image variants, which output pictures inside a normal API call.
There are tool-focused models too. Gemini Embedding turns text into vectors for search and retrieval. Gemini 2.5 Computer Use lets an agent click and type in a browser. Robotics-ER targets robots and physical tasks. You do not need most of these for a normal app. But it helps to know they exist so your engineers pick the right tool instead of forcing the text model to do everything.
Pricing compared across the lineup
Output tokens drive most of the cost. Most apps read a prompt and write a longer answer, so output volume adds up fast. The gap is wide. Gemini 2.5 Flash-Lite output is $0.40 per million. Gemini 3.1 Pro output is $12 per million, which is thirty times more. The chart below shows output price for each main model.
Watch the long-context rule on Gemini 3.1 Pro. Prompts over 200K tokens jump to a higher rate for both input and output. If you regularly send huge prompts, that can double your bill without warning. One fix is to route easy requests to Flash-Lite and only escalate hard ones to Pro. Teams that build this routing layer well often cut their model bill by half or more.
Context windows and what they mean
The context window is how much text the model can read at once, counting your prompt and the conversation so far. Most Gemini models have a 1M token window, which is roughly 750,000 words. That is enough to drop a whole codebase or a stack of long documents into one request.
Gemini 3.1 Pro goes further with a 2M token window, the largest in the lineup. That helps with very large documents or long agent runs that build up a lot of history. The chart compares context size across the main models. Bigger is not always better, though. A huge window costs more to fill, and a tight, well-built prompt often beats a giant one.
Which model should you use
Start with Gemini 3.5 Flash for coding and agent work. It is fast, strong, and fairly priced. Drop to Flash-Lite when the task is simple and you run high volume. Move up to Gemini 3.1 Pro when the task needs deep reasoning, the largest context, or creative range. Use Deep Think only for the hardest research-grade problems. The table below maps common jobs to a model.
| Job | Recommended model | Why |
|---|---|---|
| Support chatbot, FAQ | 2.5 Flash-Lite or 3.1 Flash-Lite | Fast and cheap at high volume |
| Data tagging, routing | Gemini 3.1 Flash-Lite | Simple, repeated, low risk |
| Daily coding and code review | Gemini 3.5 Flash | Frontier coding speed at a fair price |
| Document summaries, extraction | Gemini 3 Flash or 2.5 Flash | 1M context, good accuracy, low cost |
| Deep reasoning, long context | Gemini 3.1 Pro | 2M context and strongest reasoning |
| Hardest research and science | Gemini 3.1 Deep Think | Thinks longest on the toughest problems |
A simple rule helps. If a cheaper model gives the right answer most of the time, use it and add a check. Escalate only the cases that fail. This keeps quality high and cost low. We worked with an analytics team that ran every request on 3.1 Pro. Moving routine checks to 3.5 Flash and tagging to Flash-Lite cut their bill by more than half with no drop in quality.
Deprecated and older models
Google retires older models over time. Gemini 2.0 Flash and 2.0 Flash-Lite are now deprecated, and the 1.5 family before them is gone. If your code still calls one of these, plan a move. A switch is usually small. Change the model name, then test the output, since the newer models can format and reason a little differently.
| Older model | Status | Move to |
|---|---|---|
| Gemini 2.0 Flash | Deprecated | Gemini 2.5 Flash or 3.5 Flash |
| Gemini 2.0 Flash-Lite | Deprecated | Gemini 2.5 Flash-Lite |
| Gemini 1.5 Pro, 1.5 Flash | Retired | Gemini 2.5 Pro or 3.1 Pro |
Test before you trust the swap. A newer model may change how it returns JSON or how long its answers run. Check your parsing and your token limits after any move. Google’s model and pricing pages list every active model in one place, so confirm the current names before you migrate.
What this means for hiring
Knowing the lineup is step one. Getting value from it is the harder step. The teams that win are not the ones using the biggest model. They route work to the right tier, cache context, watch the long-context rule, and ship a clean product around it. That is engineering, not just a model choice.
Strong AI engineers are scarce and costly in the United States. A senior AI engineer there runs $12,000 to $18,000 per month all in. The same skill from a pre-vetted engineer in Vietnam or the Philippines often costs $3,000 to $6,000 per month. The work is the same. The bill is not. Many of our clients build their whole Gemini stack with engineers from our network.
If you also use other models, it helps to compare. Our guide on Claude versus ChatGPT for coding shows how to judge models on real developer work, not just benchmarks. Read it next to round out your view before you commit a workload to one provider.
Compare other model families
Build your Gemini stack with the right team
Gemini gives you two families and a clear set of tiers. The model is the easy part. The team that builds with it is what ships value. We match you with pre-vetted senior engineers across Asia in about 24 hours, with no upfront cost and payroll handled.
