Gemini 3.7 Flash is Google's most capable Flash model, generally available since 13 August 2026 and positioned for agentic workflows and multimodal reasoning.
- 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 DeepSeek Google Moonshot OpenAI Tencent Z.ai
Price in context
Where the price sits among 68 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 capable Flash model for agentic workflows and multimodal reasoning
- output price includes thinking tokens, and audio and video input bill at the text rate
One prompt, measured through the gateway
Gemini 3.7 Flash passed · 3 sentences
Enabling reasoning causes the model to generate a high volume of invisible "thinking tokens" behind the scenes to deliberate and solve complex logic before delivering a final response. Because our vendor bills strictly on token volume, this additional internal computation effectively doubled our billable output per query. In short, our costs increased because we are now paying for the extra compute capacity required to produce significantly higher-accuracy answers.
out 878 tok (+799 thinking) latency 6.9 s
Instruction following (exactly three sentences - countable), audience shaping (CFO register), and the hidden-thinking billing gap the token meters below expose.
Gemini 3.7 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 else: cur = 1 best = max(best, cur) return best ``` ### Explanation of the Bug The original function tracked equal adjacent transitions instead of the actual number of elements in a run, causing all non-empty run lengths to be undercounted by one (e.g., returning `0` instead of `1` for single-element lists because the loop never executes, and `1` instead of `2` for pairs). Initializing both `best` and `cur` to `1` (after handling empty inputs) and resetting `cur` to `1` on a mismatch ensures every individual element is counted as a valid run of at least length one.
out 1050 tok (+827 thinking) latency 6.4 s
Whether the fix is actually correct (runnable), explanation density, and token efficiency on a bounded task.
Gemini 3.7 Flash passed · 5/5 fields, guidance "withheld"
```json { "company": "Meridian Robotics", "quarter": "September quarter", "revenue_usd_m": 412, "growth_pct": null, "guidance": "withheld" } ```
out 918 tok (+858 thinking) latency 6.2 s
Schema obedience (no invented fields), hallucination pressure (guidance is explicitly withheld), and structured-output path differences.
Gemini 3.7 Flash passed · 120 words, 0 banned, 1 question
Why pay twice for identical AI queries simply because you routed them to different model vendors? Today, we introduce Universal Prompt Caching directly within our unified API gateway architecture. This capability stores repeated prompt contexts across OpenAI, Anthropic, and local models, instantly returning stored results to eliminate redundant computation fees. When your application sends an LLM request, the gateway inspects the payload, identifies semantic matches, and returns accurate cached responses in under ten milliseconds. Engineering teams can now slash inference latency by eighty percent while dramatically reducing monthly token expenditures across diverse production deployments. You retain complete privacy control, flexible cache eviction policies, and granular metrics through a single dashboard. Update your routing settings today to accelerate overall system performance.
out 2858 tok (+2718 thinking) latency 14.1 s
Constraint obedience (word budget, banned-word list, the single question), style fingerprint, and length control.
Use Gemini 3.7 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-3.7-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-3.7-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-3.7-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-3.7-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-3.7-flash")
.addUserMessage("Summarize this diff")
.reasoningEffort(ReasoningEffort.MEDIUM)
.build());
System.out.println(resp.choices().get(0).message().content().orElse(""));About Gemini 3.7 Flash
- It takes 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 stitching several models into a pipeline.
- Two pricing details make it unusually easy to budget for.
- There is no context-length step: a 900,000-token prompt costs the same per token as a 900-token one, so long-context work does not fall off a cliff partway through a document.
- And there is no modality surcharge either - audio and video input bill at the text rate, which is the opposite of how most multimodal pricing works and removes the usual reason to downsample media before sending 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.
- Context caching is available at roughly a tenth of the input rate, with a separate hourly storage charge, which makes it worth pinning a stable prefix for agent loops that replay the same system prompt.
- Grounding with Google Search is included for a monthly 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 3.7 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 3.7 Flash.
What is Gemini 3.7 Flash best at?
Most capable Flash tier for agentic work; text, image, audio and video input; no context-length or modality surcharge. See the About section for the full picture from the vendor's own release notes.
How much does Gemini 3.7 Flash cost?
Gemini 3.7 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.
Does Gemini 3.7 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 3.7 Flash?
Point your existing OpenAI SDK at base_url="https://synthorai.io/v1", set model="gemini-3.7-flash", and you're done. One API key covers every model on the gateway.
What is Gemini 3.7 Flash's knowledge cutoff?
Gemini 3.7 Flash's knowledge cutoff is 2026-03, per the vendor's official documentation (as of 2026-08-15).
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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.