Gemini 3.8 Flash is Google's most intelligent Flash model, described on its official page as engineered for long-horizon software engineering, autonomous agents and complex enterprise workflows.
- Input
- text image audio video $0.75/M
- Output
- text $3.75/M
- Audio input
- $0.75/M
- Cache read
- $0.075/M
- Context
- 1M
- vs GPT-4o
- ~85% cheaper
- Knowledge cutoff
- 2026-03
Benchmarks
Vendor-published: Alibaba (Qwen) Anthropic ByteDance DeepSeek Google MiniMax Moonshot OpenAI Tencent Z.ai
Price in context
Where the price sits among 69 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 | 2026-03 |
Prompt caching
| How it caches | automatic + explicit |
|---|---|
| Min prefix | 4,096 |
Thinking
| Parameter | reasoning_effort |
|---|---|
| Values | minimal · low · medium · high accepted set is the provider's |
Model
| Modalities | text + image + audio + video → text |
|---|
Google's most intelligent Flash model, engineered for long-horizon software engineering, autonomous agents and complex enterprise workflows. The output price includes thinking tokens. The published rate is introductory: input and output both double on 1 January 2027, and context caching read and hourly storage double with them.
One prompt, measured through the gateway
Gemini 3.8 Flash passed · 3 sentences
When we enabled reasoning, the model began generating hidden "thinking tokens" to work through logic step-by-step before producing a final answer. Because AI vendors bill for every single token processed—visible or not—this internal deliberation dramatically inflated our billable volume per query. In short, while our total number of user requests remained flat, the cost per transaction doubled to buy higher accuracy on complex tasks.
out 705 tok (+624 thinking) latency 6.4 s
Instruction following (exactly three sentences, so countable), audience shaping (CFO register), and the hidden-thinking billing gap the token meters below expose.
Gemini 3.8 Flash passed · 8/8 cases
Here is the corrected function: ```python def longest_run(xs): if not xs: return 0 best = 1 cur = 1 for i in range(1, len(xs)): if xs[i] == xs[i - 1]: cur += 1 best = max(best, cur) else: cur = 1 return best ``` ### Explanation of the Bug The original function counted equal-neighbor transitions rather than the actual items making up each run. Because `cur` started at `0` (and was reset to `0` on mismatches), every run was undercounted by one: single-element lists returned `0` because the loop never executed, and any subsequent run—including those ending at the last index—recorded a count of $N - 1$ instead of $N$. The fix handles empty input upfront, initializes both `cur` and `best` to `1` (since any single element forms a run of length 1), and resets `cur` to `1` whenever a new run begins.
out 1393 tok (+1149 thinking) latency 10.1 s
Whether the fix is actually correct (runnable), explanation density, and token efficiency on a bounded task.
Gemini 3.8 Flash passed · 5/5 fields, guidance null
```json { "company": "Meridian Robotics", "quarter": "September quarter", "revenue_usd_m": 412, "growth_pct": null, "guidance": null } ```
out 3989 tok (+3932 thinking) latency 30.4 s
Schema obedience (no invented fields), hallucination pressure (guidance is explicitly withheld), and structured-output path differences.
Gemini 3.8 Flash passed · 120 words, 0 banned, 1 question
Why pay multiple model providers for the exact same output? Introducing Universal Prompt Cache, our latest API gateway capability engineered to cut compute expenses and drop inference latency. When your application makes a call, the gateway inspects a central memory layer before routing traffic to external LLMs. If an identical query was previously processed by OpenAI, Anthropic, or Mistral, our gateway returns that response immediately. This shared cache eliminates duplicate token fees and insulates your production apps from vendor rate limits or unexpected downtime. Developers can easily customize expiration settings, enforce strict data privacy controls, and configure invalidation logic across every endpoint. Stop wasting your budget on repeated queries. Enable prompt caching in your dashboard to accelerate your pipeline today.
out 3833 tok (+3688 thinking) latency 21.8 s
Constraint obedience (word budget, banned-word list, the single question), style fingerprint, and length control.
Use Gemini 3.8 Flash 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-3.8-flash",
messages=[{"role": "user", "content": "Summarize this diff"}],
reasoning_effort="medium",
)
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-3.8-flash",
messages: [{ role: "user", content: "Summarize this diff" }],
reasoning_effort: "medium",
});
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-3.8-flash",
"messages": [{"role": "user", "content": "Hello"}],
"reasoning_effort": "medium"
}'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-3.8-flash",
Messages: []openai.ChatCompletionMessageParamUnion{
openai.UserMessage("Summarize this diff"),
},
ReasoningEffort: openai.ReasoningEffortMedium,
})
fmt.Println(resp.Choices[0].Message.Content)
}import com.openai.client.OpenAIClient;
import com.openai.client.okhttp.OpenAIOkHttpClient;
import com.openai.models.chat.completions.*;
import com.openai.models.ReasoningEffort;
OpenAIClient client = OpenAIOkHttpClient.builder()
.baseUrl("https://synthorai.io/v1")
.apiKey("sk-syn-...")
.build();
ChatCompletion resp = client.chat().completions().create(
ChatCompletionCreateParams.builder()
.model("gemini-3.8-flash")
.addUserMessage("Summarize this diff")
.reasoningEffort(ReasoningEffort.MEDIUM)
.build());
System.out.println(resp.choices().get(0).message().content().orElse(""));About Gemini 3.8 Flash
- It accepts text, images, audio and video and returns text, so a single call can reason over a screenshot, a recording and a prompt together instead of chaining several models into a pipeline.
- The pricing detail to plan around is that the published rate is introductory rather than settled: $0.75 per million input tokens and $3.75 per million output tokens hold through 31 December 2026, and both double on 1 January 2027, to $1.50 and $7.50.
- Context caching moves on the same date, from $0.075 to $0.15 per million tokens read, with the hourly storage charge going from $0.50 to $1.00 per million tokens per hour.
- Anything sized on today's price is worth re-checking before the new year rather than after it.
- The output price includes thinking tokens, so a reasoning-heavy answer is billed on the full trace rather than only the visible text; budget max output tokens with that in mind.
- Google's published Standard table lists a single input rate with no context-length step and no separate audio or video line, so a long prompt and media input bill at the same rate as text.
- Grounding with Google Search and grounding with Google Maps each carry a monthly free allowance shared across the Gemini 3 family before per-request charges begin.
- Synthorai serves it through the OpenAI-compatible chat completions endpoint, so it drops into an existing integration without a Google-specific client.
FAQ
Is the Gemini 3.8 Flash API free to try?
Yes: new accounts get 10 trial calls and up to $1 in free credit, no card required. At $0.75/M input tokens, that credit alone covers roughly 166 requests of ~8K tokens against Gemini 3.8 Flash.
What is Gemini 3.8 Flash best at?
Most intelligent Flash tier for long-horizon agents; introductory pricing doubles on 1 January 2027; no context-length step, no modality surcharge. See the About section for the full picture from the vendor's own release notes.
How much does Gemini 3.8 Flash cost?
Gemini 3.8 Flash costs $0.75 per million input tokens and $3.75 per million output tokens on Synthorai. That is the provider's list price, with no platform markup. Cached input tokens bill at $0.075/M.
What is Gemini 3.8 Flash's context window?
Gemini 3.8 Flash has a context window of 1M tokens on Synthorai. A single response can be up to 65,536 tokens long.
Does Gemini 3.8 Flash support prompt caching?
Yes: automatic caching is on by default, with an explicit mode for guaranteed savings. Cached input tokens bill at $0.075/M vs $0.75/M uncached; prompts need a 4,096-token stable prefix to cache. Prompt caching guide →
How do I get access to Gemini 3.8 Flash?
Point your existing OpenAI SDK at base_url="https://synthorai.io/v1", set model="gemini-3.8-flash", and you're done. One API key covers every model on the gateway.
What is Gemini 3.8 Flash's knowledge cutoff?
Gemini 3.8 Flash's knowledge cutoff is 2026-03, per the vendor's official documentation (as of 2026-09-03).
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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.