Gemini 3.8 Flash vs Kimi K2.7 Code
Which one, when
Pick gemini-3.8-flash when the job needs long or mixed input: it takes text, image, audio and video across a 1048576-token context, four times kimi-k2.7-code's 256000, with double the max output (65536 vs 32768) and cheaper cache reads at $0.075 per million against $0.19, roughly 2.5x less. Its fresh tokens are cheaper too, $0.75 input versus $0.95 and $3.75 output versus $4, so the gap widens on cache-heavy workloads. Choose kimi-k2.7-code for text, image and video coding and tool work where reasoning should always stay on, since its thinking mode cannot be disabled.
Benchmarks
Vendor-published: Alibaba (Qwen) Anthropic ByteDance DeepSeek Google MiniMax Moonshot OpenAI Tencent Z.ai
Pricing
| Gemini 3.8 Flash | Kimi K2.7 Code | Δ | |
|---|---|---|---|
| Input / 1M tokens | $0.75 | $0.95 | 0.79× |
| Output / 1M tokens | $3.75 | $4 | 0.94× |
| Cache read / 1M tokens | $0.075 | $0.19 | 0.39× |
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 71 chat models on this billing unit (log scale)
Capabilities
| Gemini 3.8 Flash | Kimi K2.7 Code | |
|---|---|---|
| Tool use | yes | yes |
| Thinking control | yes - vendor dial not published | always on |
| Structured output | yes | - |
| Prompt caching | implicit + explicit | implicit (automatic) |
| Cache lifetime | not published | not published |
| Minimum cached prefix | 4096 tokens | not published |
Specs
| Gemini 3.8 Flash | Kimi K2.7 Code | |
|---|---|---|
| Input modalities | text image audio video | text image video |
| Output modalities | text | text |
| Released | 2026-09-02 | 2026-06 |
| Knowledge cutoff | 2026-03 | - |
| Context window | 1M | 256K |
| Max output | 66K | 33K |
| Thinking parameter | - |
|
| Accepted values | - | type
keep
|
| Default | - | thinking on with Preserved Thinking on |
Specs are transcribed from each vendor’s documentation; a row a vendor does not publish is left out rather than inferred. Full sources: Gemini 3.8 Flash · Kimi K2.7 Code
One prompt, both models - 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
Kimi K2.7 Code passed · 3 sentences
Enabling reasoning mode causes the model to generate an internal chain-of-thought before producing each answer, and that hidden reasoning text is billed like any other output. Those extra reasoning tokens typically add up to much more than the final response itself, so every API call now costs significantly more than before. Because our overall usage volume did not change, the doubled bill reflects that each query consumes roughly twice as many billable tokens.
out 259 tok (+174 thinking) latency 5.4 s
Instruction following (exactly three sentences - 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
Kimi K2.7 Code passed · 8/8 cases
```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 ``` The bug is that the function counts *transitions* between equal neighbors instead of counting the items themselves, so a single-element list never enters the loop and returns `0` instead of `1`. Likewise, resetting `cur` to `0` on a mismatch forgets the current item that starts a new run, which causes runs that end at the last index to be undercounted. Initializing both `best` and `cur` to `1` and resetting `cur` to `1` fixes both issues.
out 410 tok (+222 thinking) latency 9.4 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
Kimi K2.7 Code passed · 5/5 fields, guidance "withheld pending the Q4 close"
```json { "company": "Meridian Robotics", "quarter": "September quarter", "revenue_usd_m": 412, "growth_pct": 11, "guidance": "withheld pending the Q4 close" } ```
out 721 tok (+663 thinking) latency 13.2 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
Kimi K2.7 Code passed · 120 words, 0 banned, 1 question
We're introducing Prompt Cache for our API Gateway, a new capability that stores prompt responses across multiple AI providers. By caching replies, you reduce redundant calls, lower latency, and cut costs without changing how you build. Teams using several model providers can now reuse identical prompt results instantly, keeping traffic consistent and budgets predictable. Responses are keyed by provider, model, and exact prompt, so you always get the right result. Configure TTL, hit thresholds, and eviction rules from a single dashboard. It fits into your existing routing and requires no code changes. Setup takes minutes and works with your current endpoints. Want to see how much latency and spend you can trim? Check the docs to enable Prompt Cache today.
out 2375 tok (+2235 thinking) latency 38.6 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-3.8-flash",
# model="kimi-k2.7-code", # uncomment this line, comment the one above
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",
// model: "kimi-k2.7-code", // uncomment this line, comment the one above
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",
# "model": "kimi-k2.7-code", # uncomment this line, comment the one above
"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",
// Model: "kimi-k2.7-code", // uncomment this line, comment the one above
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")
// .model("kimi-k2.7-code") // uncomment this line, comment the one above
.addUserMessage("Summarize this diff")
.reasoningEffort(ReasoningEffort.MEDIUM)
.build());
System.out.println(resp.choices().get(0).message().content().orElse(""));FAQ
Which is cheaper, Gemini 3.8 Flash or Kimi K2.7 Code?
Gemini 3.8 Flash is cheaper on input / 1m tokens ($0.75 vs $0.95, 1.3× 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 3.8 Flash against Kimi K2.7 Code 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 3.8 Flash and Kimi K2.7 Code 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.