Kimi K2.7 Code vs MiniMax M3
MiniMax M3 has been retired from our catalogue. Its figures below are the last published rates; calls to it are no longer served, while the other model in this comparison is.
Which one, when
Both models take text, image and video in and return text, and both cover chat, code, reasoning and tools, so the split is price and scale: minimax-m3 runs $0.3 input and $1.2 output against $0.95 and $4 for kimi-k2.7-code, roughly 3.2x cheaper on input and 3.3x on output, with a 1000000-token context and 524288 max output versus 256000 and 32768. Pick minimax-m3 for very long inputs, long generations, or when you want to turn thinking off, since kimi-k2.7-code always reasons. Choose kimi-k2.7-code if you specifically want Moonshot's model within its 256000-token limit.
Benchmarks
MiniMax M3: the vendor has not published benchmark scores.
Vendor-published: Alibaba (Qwen) Moonshot OpenAI Z.ai
Pricing
| Kimi K2.7 Code | MiniMax M3 | Δ | |
|---|---|---|---|
| Input / 1M tokens | $0.95 | $0.3 | 3.2× |
| Output / 1M tokens | $4 | $1.2 | 3.3× |
| Cache read / 1M tokens | $0.19 | $0.06 | 3.2× |
| Cache write | - | no separate charge | - |
Rates from the live catalogue at build time; each model page carries the current rate card.
Where they sit · input price per 1M tokens across all 76 chat models on this billing unit (log scale)
Capabilities
| Kimi K2.7 Code | MiniMax M3 | |
|---|---|---|
| Tool calling | yes | yes |
| Thinking control | always on | configurable |
| Prompt caching | implicit (automatic) | implicit (automatic) |
| Cache lifetime | not published | not published |
| Minimum cached prefix | not published | 512 tokens |
Specs
| Kimi K2.7 Code | MiniMax M3 | |
|---|---|---|
| Input modalities | text image video | text image video |
| Output modalities | text | text |
| Released | 2026-06 | 2026-06-01 |
| Context window | 256K | 1M |
| Max output | 33K | 524K |
| Thinking parameter |
|
|
| Accepted values | type
keep
| thinking.type
reasoning_split
|
| Default | thinking on with Preserved Thinking on | adaptive: thinking on, with the model deciding when extra reasoning helps |
Specs are transcribed from each vendor’s documentation; a row a vendor does not publish is left out rather than inferred. Full sources: Kimi K2.7 Code · MiniMax M3
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="kimi-k2.7-code",
# model="minimax-m3", # 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: "kimi-k2.7-code",
// model: "minimax-m3", // 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": "kimi-k2.7-code",
# "model": "minimax-m3", # 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: "kimi-k2.7-code",
// Model: "minimax-m3", // 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("kimi-k2.7-code")
// .model("minimax-m3") // 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, Kimi K2.7 Code or MiniMax M3?
MiniMax M3 is cheaper on the "Input / 1M tokens" row ($0.3 vs $0.95, 3.2× apart). Other rows may point the other way; the table above carries the full rate card, and real cost depends on your mix.
Can I A/B test Kimi K2.7 Code against MiniMax M3 without two integrations?
Yes. Both are served through the same OpenAI-compatible endpoint with one API key. Switching is a one-line change to the model id, so you can route a fraction of traffic to each and compare bills directly.
Do Kimi K2.7 Code and MiniMax M3 support prompt caching?
Yes. Both bill cache 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.