Gemini 2.5 Flash-Lite is Google's most cost-efficient multimodal Gemini 2.5 model, offering the fastest performance in the family for high-frequency, lightweight tasks such as classification, data extraction, and summarization.
- Input
- text image video audio $0.1/M
- Output
- text $0.4/M
- Audio input
- $0.3/M
- Cache read
- $0.01/M
- Context
- 1M
- vs GPT-4o
- ~98% cheaper
- Knowledge cutoff
- 2025-01
Price in context
Where the price sits among 60 comparable models
The bar shows how this model’s price compares with every other model of the same kind on Synthorai. The cheapest and the most expensive are named at each end. These are base rates; batch, region and cache-write discounts are on the pricing page.
Specs & limits
Tokens
| Context window (vendor spec) | 1,048,576 |
|---|---|
| Max output (vendor spec) | 65,536 |
| Knowledge cutoff | 2025-01 |
Prompt caching
| How it caches | automatic + explicit |
|---|---|
| Min prefix | 4,096 |
Thinking
| Vendor control | thinkingBudget (generateContent) · thinking_level (Interactions API) |
|---|---|
| Accepted values | thinkingBudget 512 to 24576, or -1 for dynamic · Interactions API: low, medium, high |
| Default | off, and the model does not think unless a budget is set applied when the request sets nothing |
| Can be turned off | Yes |
| Thinking behaviour | The only Gemini that ships with thinking off by default; the budget range starts at 512, yet thinkingBudget 0 is separately valid and keeps thinking off, while -1 turns on dynamic thinking. |
| Parameter | reasoning_effort |
| Values | minimal · low · medium · high the gateway's parameter surface - the vendor mapping above applies |
Model
| Modalities | text + image + video + audio → text |
|---|
- 1,048,576-token input / 65,536 output
- text+image+video+audio+PDF input
- thinking supported
One prompt, measured through the gateway
Gemini 2.5 Flash-Lite passed · 3 sentences
Enabling reasoning allows our LLM to perform more complex, multi-step computations, directly increasing the computational resources and time required per query. This enhanced capability, while powerful, inherently consumes more processing power and leads to a proportional rise in our operational costs. Consequently, the doubled bill accurately reflects the significant increase in the LLM's workload and the underlying infrastructure expenses associated with its advanced reasoning functions.
out 80 tok latency 1.0 s
Instruction following (exactly three sentences - countable), audience shaping (CFO register), and the hidden-thinking billing gap the token meters below expose.
Gemini 2.5 Flash-Lite passed · 8/8 cases
The bug in the `longest_run` function is that it doesn't account for the initial run length of 1 for any element, and it fails to consider a run that might end at the very last element of the list. The `cur` variable is incremented *after* checking for equality, meaning for a single element, it never gets incremented from its initial value of 0. Similarly, if the longest run extends to the end of the list, the `best` variable might not be updated with the final `cur` value. […]
out 728 tok latency 2.8 s
Whether the fix is actually correct (runnable), explanation density, and token efficiency on a bounded task.
Gemini 2.5 Flash-Lite passed · 5/5 fields, guidance null
```json { "company": "Meridian Robotics", "quarter": "September", "revenue_usd_m": 412, "growth_pct": 11, "guidance": null } ```
out 58 tok latency 0.9 s
Schema obedience (no invented fields), hallucination pressure (guidance is explicitly withheld), and structured-output path differences.
Gemini 2.5 Flash-Lite missed · 78 words, 0 banned, 1 question
Introducing Enhanced Prompt Caching for our API Gateway! This new capability intelligently stores your prompts, reducing repeated API calls to multiple providers. Significantly cut down latency and optimize your application's performance by leveraging cached responses. How much could this speed boost improve your user experience? Our advanced caching ensures consistency and faster access to information, no matter the underlying service. Experience a more efficient and cost-effective integration strategy. Get started today and see the difference prompt caching makes.
out 97 tok latency 1.0 s
Constraint obedience (word budget, banned-word list, the single question), style fingerprint, and length control.
Use Gemini 2.5 Flash-Lite in 30 seconds
OpenAI-compatible: swap the base_url, keep your SDK. POST /v1/chat/completions
from openai import OpenAI
client = OpenAI(
base_url="https://synthorai.io/v1",
api_key="sk-syn-...",
)
resp = client.chat.completions.create(
model="gemini-2.5-flash-lite",
messages=[{"role": "user", "content": "Summarize this diff"}],
)
print(resp.choices[0].message.content)import OpenAI from "openai";
const client = new OpenAI({
baseURL: "https://synthorai.io/v1",
apiKey: "sk-syn-...",
});
const resp = await client.chat.completions.create({
model: "gemini-2.5-flash-lite",
messages: [{ role: "user", content: "Summarize this diff" }],
});
console.log(resp.choices[0].message.content);curl https://synthorai.io/v1/chat/completions \
-H "Authorization: Bearer sk-syn-..." \
-H "Content-Type: application/json" \
-d '{
"model": "gemini-2.5-flash-lite",
"messages": [{"role": "user", "content": "Hello"}]
}'package main
import (
"context"
"fmt"
"github.com/openai/openai-go/v3"
"github.com/openai/openai-go/v3/option"
)
func main() {
client := openai.NewClient(
option.WithBaseURL("https://synthorai.io/v1"),
option.WithAPIKey("sk-syn-..."),
)
resp, _ := client.Chat.Completions.New(context.TODO(), openai.ChatCompletionNewParams{
Model: "gemini-2.5-flash-lite",
Messages: []openai.ChatCompletionMessageParamUnion{
openai.UserMessage("Summarize this diff"),
},
})
fmt.Println(resp.Choices[0].Message.Content)
}import com.openai.client.OpenAIClient;
import com.openai.client.okhttp.OpenAIOkHttpClient;
import com.openai.models.chat.completions.*;
OpenAIClient client = OpenAIOkHttpClient.builder()
.baseUrl("https://synthorai.io/v1")
.apiKey("sk-syn-...")
.build();
ChatCompletion resp = client.chat().completions().create(
ChatCompletionCreateParams.builder()
.model("gemini-2.5-flash-lite")
.addUserMessage("Summarize this diff")
.build());
System.out.println(resp.choices().get(0).message().content().orElse(""));About Gemini 2.5 Flash-Lite
- Google names its best uses as high-volume classification, simple data extraction, and "extremely low-latency applications where budget and speed are the primary constraints," which is also the line for choosing it over 2.5 Flash.
- It accepts text, image, video, audio, and PDF input with a 1,048,576-token context window and 65,536-token output limit, and returns text.
- Function calling, structured outputs, code execution, Search and Maps grounding, URL context, context caching, the Batch API, and Flex and Priority inference are all supported; Live API, image generation, and audio generation are not.
- Its defining behaviour is thinking: alone in this lineup, Flash-Lite ships with thinking off by default, so nothing is spent on reasoning tokens unless you ask for them. thinkingBudget accepts 512 through 24,576 for a fixed allowance or -1 for dynamic thinking, and 0 keeps it disabled, which matters because thinking tokens are billed at the output rate.
- Google's newer Interactions API expresses the same control as a thinking_level string at low, medium, or high.
- The knowledge cutoff is January 2025, and Google has published no retirement date for the 2.5 generally available models.
- Synthorai routes requests to it through the standard OpenAI-compatible chat completions endpoint.
FAQ
Is the Gemini 2.5 Flash-Lite API free to try?
Yes: new accounts get 10 trial calls and up to $1 in free credit, no card required. At $0.1/M input tokens, that credit alone covers roughly 1,250 requests of ~8K tokens against Gemini 2.5 Flash-Lite.
What is Gemini 2.5 Flash-Lite best at?
Most cost-efficient, fastest in its family; optional thinking for harder problems; built for classification, extraction, and summarization. See the About section for the full picture from the vendor's own release notes.
How much does Gemini 2.5 Flash-Lite cost?
Gemini 2.5 Flash-Lite costs $0.1 per million input tokens and $0.4 per million output tokens on Synthorai. That is the provider's list price, with no platform markup. Cached input tokens bill at $0.01/M.
Does Gemini 2.5 Flash-Lite support prompt caching?
Yes: automatic caching is on by default, with an explicit mode for guaranteed savings. Cached input tokens bill at $0.01/M vs $0.1/M uncached; prompts need a 4,096-token stable prefix to cache. Prompt caching guide →
How do I get access to Gemini 2.5 Flash-Lite?
Point your existing OpenAI SDK at base_url="https://synthorai.io/v1", set model="gemini-2.5-flash-lite", and you're done. One API key covers every model on the gateway.
What is Gemini 2.5 Flash-Lite's knowledge cutoff?
Gemini 2.5 Flash-Lite's knowledge cutoff is 2025-01, per the vendor's official documentation (as of 2026-07-09).
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Every value on this page is transcribed from the vendor's own documentation, linked above, and carries the date it was checked. Prices are compared across the catalogue; specification values that vendors define differently are shown with the difference stated rather than charted. Nothing here is measured by us, and nothing is scored.