GPT-6 Sol vs Kimi K2.7 Code
Which one, when
Pick gpt-6-sol when the job needs room: its 1050000-token context is roughly 4x the 256000 of kimi-k2.7-code, it can emit up to 128000 tokens against 32768, it carries the vision flag, and its thinking can be turned off for latency-sensitive calls. Pick kimi-k2.7-code to cut cost on high-volume coding and tool work, at $0.95 input and $4 output versus $2 and $10, roughly 2.1x and 2.5x cheaper, with video accepted as input and reasoning always on. Cache reads are near-identical, $0.19 against $0.2, so caching strategy barely shifts the choice.
Benchmarks
Vendor-published: Alibaba (Qwen) Anthropic DeepSeek Google Moonshot OpenAI Z.ai
Pricing
| GPT-6 Sol | Kimi K2.7 Code | Δ | |
|---|---|---|---|
| Input / 1M tokens | $2 | $0.95 | 2.1× |
| Output / 1M tokens | $10 | $4 | 2.5× |
| Cache read / 1M tokens | $0.2 | $0.19 | 1.1× |
| Cache write | no separate charge | - | - |
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 74 chat models on this billing unit (log scale)
Capabilities
| GPT-6 Sol | Kimi K2.7 Code | |
|---|---|---|
| Tool use | yes | yes |
| Thinking control | configurable | always on |
| Structured output | yes | - |
| Prompt caching | implicit (automatic) | implicit (automatic) |
| Cache lifetime | 5-10m, up to 1h | not published |
| Minimum cached prefix | 1024 tokens | not published |
Specs
| GPT-6 Sol | Kimi K2.7 Code | |
|---|---|---|
| Input modalities | text image | text image video |
| Output modalities | text | text |
| Released | 2026-09-22 | 2026-06 |
| Knowledge cutoff | 2026-04 | - |
| Context window | 1.1M | 256K |
| Max output | 128K | 33K |
| Thinking parameter | reasoning.effort |
|
| Accepted values | reasoning.effort
| type
keep
|
| Default | medium | 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: GPT-6 Sol · Kimi K2.7 Code
One prompt, both models - measured through the gateway
GPT-6 Sol passed · 3 sentences
Enabling reasoning made the model generate extra internal tokens to work through each request, and those tokens are billed even though users don’t see them. If request volume stayed roughly the same, more billed tokens per request could explain why the bill doubled. We should check the usage breakdown to confirm, then lower reasoning effort or limit it to tasks that need it.
out 188 tok (+111 thinking) latency 5.0 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.
GPT-6 Sol passed · 8/8 cases
```python def longest_run(xs): if not xs: return 0 best = 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 ``` The original `cur` counted matching *adjacent pairs*, not items, so every run was one too short—including a single-element input, which returned 0. Starting each run at 1 counts its first item, and updating `best` on every iteration ensures a run that reaches the last index is included.
out 206 tok (+59 thinking) latency 5.9 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.
GPT-6 Sol passed · 5/5 fields, guidance "Withheld pending the Q4 close"
{"company":"Meridian Robotics","quarter":"September quarter","revenue_usd_m":null,"growth_pct":null,"guidance":"Withheld pending the Q4 close"}
out 227 tok (+185 thinking) latency 5.2 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.
GPT-6 Sol passed · 120 words, 0 banned, 1 question
Today, we’re introducing cross-provider prompt caching for our API gateway, helping teams reuse repeated prompt content when routing requests among supported AI providers. The gateway identifies eligible prompt prefixes, stores cache references, and applies them to subsequent requests where provider capabilities allow. Why send the same context again if it can be reused? With fewer duplicate input tokens, applications can reduce costs and improve latency without changing how developers call the gateway. Configure caching by route, monitor hit rates and savings in your dashboard, and keep existing provider fallback rules in place. Availability and cache behavior vary by provider, so the gateway reports eligibility and usage for each request. Get started today with our documentation and test your existing workloads.
out 586 tok (+443 thinking) latency 7.7 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="gpt-6-sol",
# 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: "gpt-6-sol",
// 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": "gpt-6-sol",
# "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: "gpt-6-sol",
// 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("gpt-6-sol")
// .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, GPT-6 Sol or Kimi K2.7 Code?
Kimi K2.7 Code is cheaper on input / 1m tokens ($0.95 vs $2, 2.1× 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 GPT-6 Sol 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 GPT-6 Sol 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.