Gemini 3.5 Flash vs Gemini 3.7 Flash
Which one, when — curated verdict, not a benchmark table
These two Google Flash models match on the essentials — a 1048576-token context, 65536 max output, and text, image, video and audio in with text out — so the split is price and generation: gemini-3.7-flash (released 2026-08-13) charges $0.75 input and $3.75 output against $1.5 and $9 for gemini-3.5-flash, making it 2x cheaper on input, 2.4x on output, and about 6.7x cheaper on audio input at $0.75 versus $5 per million. It also carries the reasoning capability flag. Pick gemini-3.5-flash only if you are already pinned to it; otherwise gemini-3.7-flash covers the same shape for less.
Pricing
| Gemini 3.5 Flash | Gemini 3.7 Flash | Δ | |
|---|---|---|---|
| Input / 1M tokens | $1.5 | $0.75 | 2× |
| Output / 1M tokens | $9 | $3.75 | 2.4× |
| Cache read / 1M tokens | $0.15 | $0.075 | 2× |
Rates from the live catalog at build time; each model page carries the current card.
Where they sit — input price per 1M tokens across all 63 chat models on this billing unit (log scale)
Capabilities
| Gemini 3.5 Flash | Gemini 3.7 Flash | |
|---|---|---|
| Tool use | yes | yes |
| Thinking control | always on | yes — vendor dial not published |
| Structured output | yes | yes |
| Prompt caching | implicit + explicit | implicit + explicit |
| Cache lifetime | not published | not published |
| Minimum cached prefix | 4096 tokens | 4096 tokens |
Specs
| Gemini 3.5 Flash | Gemini 3.7 Flash | |
|---|---|---|
| Input modalities | text image audio video | text image audio video |
| Output modalities | text | text |
| Released | 2026-05-19 | 2026-08-13 |
| Knowledge cutoff | 2025-01 | — |
| Context window | 1M | 1M |
| Max output | 66K | 66K |
| Thinking parameter | thinkingLevel | — |
| Accepted values |
| — |
| Default | medium | — |
Specs are transcribed from each vendor’s documentation; a row a vendor does not publish is left out rather than inferred. Full sources: Gemini 3.5 Flash · Gemini 3.7 Flash
One prompt, both models — measured through the gateway
Gemini 3.5 Flash passed · 3 sentences
Reasoning models do not just generate a final answer; they perform a silent, multi-step "thinking" process beforehand to ensure higher accuracy. We are billed for every single one of these invisible reasoning steps (called "thinking tokens"), which drastically increases the computational volume per query. Consequently, while our user activity remained flat, our unit costs doubled because the AI is now doing significantly more backend processing to deliver these higher-quality results.
out 1152 tok (+1065 thinking) latency 7.7 s
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 3.5 Flash passed · 8/8 cases
Here is the corrected version of the 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 code initialized `best` and `cur` to `0` instead of `1` (the minimum run length for any non-empty list), which caused it to count the number of adjacent duplicate *transitions* rather than the actual number of *elements* in a run. This resulted in an off-by-one error for all runs and caused single-element lists to incorrectly return `0`. […]
out 2386 tok (+2110 thinking) latency 12.7 s
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 3.5 Flash passed · 5/5 fields, guidance "withheld pending the Q4 close"
```json { "company": "Meridian Robotics", "quarter": "September quarter", "revenue_usd_m": 412, "growth_pct": null, "guidance": "withheld pending the Q4 close" } ```
out 1950 tok (+1885 thinking) latency 12.7 s
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 3.5 Flash missed · 50 words, 0 banned, 0 questions
thought 51: OpenAI, 52: Anthropic, 53: and 54: Cohere. 55: When 56: a 57: client 58: submits 59: a 60: matching 61: request, 62: the 63: gateway 64: retrieves 65: the 66: cached 67: result 68: instantly, 69: slashing 70: latency 71: and 72: reducing 73: your 74: monthly 75: […]
out 4092 tok (+3933 thinking) latency 19.1 s
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.
Switch between them with one line
Both ids are in every tab below — the highlighted pair of lines is the only edit. Same endpoint, same key, same request shape.
from openai import OpenAI
client = OpenAI(
base_url="https://synthorai.io/v1",
api_key="sk-syn-...",
)
resp = client.chat.completions.create(
model="gemini-3.5-flash",
# model="gemini-3.7-flash", # uncomment this line, comment the one above
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-3.5-flash",
// model: "gemini-3.7-flash", // uncomment this line, comment the one above
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-3.5-flash",
# "model": "gemini-3.7-flash", # uncomment this line, comment the one above
"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-3.5-flash",
// Model: "gemini-3.7-flash", // uncomment this line, comment the one above
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-3.5-flash")
// .model("gemini-3.7-flash") // uncomment this line, comment the one above
.addUserMessage("Summarize this diff")
.build());
System.out.println(resp.choices().get(0).message().content().orElse(""));FAQ
Which is cheaper, Gemini 3.5 Flash or Gemini 3.7 Flash?
Gemini 3.7 Flash is cheaper on input / 1m tokens ($0.75 vs $1.5, 2.0× apart). Other rows may point the other way — the table above carries the full card, and real cost depends on your mix.
Can I A/B test Gemini 3.5 Flash against Gemini 3.7 Flash without two integrations?
Yes. Both are served through the same OpenAI-compatible endpoint with one API key — switching is a one-line model-string change, so you can route a fraction of traffic to each and compare bills directly.
Do Gemini 3.5 Flash and Gemini 3.7 Flash support prompt caching?
Yes — both bill cached reads below their input rate, so warm-prefix workloads cost less than the list rates suggest. The exact cache-read rows are in the pricing table above.