Kimi K3 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 take text, image and video in and return text, with chat, code, reasoning and tool use on either side, so the real split is price and output headroom: kimi-k3 costs 10x more on input, 12.5x more on output and 5x more on cached reads than minimax-m3. Pick kimi-k3 when you need its 1048576-token context and matching 1048576-token max output for single-pass generation of very long artifacts, since minimax-m3 caps output at 524288. Pick minimax-m3 for cost-sensitive volume, its long-context flag, and the option to turn thinking off, which kimi-k3 does not allow.
Benchmarks
MiniMax M3: the vendor has not published benchmark scores.
Vendor-published: Alibaba (Qwen) Anthropic ByteDance DeepSeek Google MiniMax Moonshot OpenAI Tencent Z.ai
Pricing
| Kimi K3 | MiniMax M3 | Δ | |
|---|---|---|---|
| Input / 1M tokens | $3 | $0.3 | 10× |
| Output / 1M tokens | $15 | $1.2 | 13× |
| Cache read / 1M tokens | $0.3 | $0.06 | 5× |
| 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 K3 | MiniMax M3 | |
|---|---|---|
| Tool calling | yes | yes |
| Thinking control | always on | configurable |
| Structured output | yes | - |
| Prompt caching | implicit (automatic) | implicit (automatic) |
| Cache lifetime | not published | not published |
| Minimum cached prefix | not published | 512 tokens |
Specs
| Kimi K3 | MiniMax M3 | |
|---|---|---|
| Input modalities | text image video | text image video |
| Output modalities | text | text |
| Released | 2026-07-16 | 2026-06-01 |
| Context window | 1M | 1M |
| Max output | 1M | 524K |
| Thinking parameter | reasoning_effort (top-level; the thinking object is not accepted) |
|
| Accepted values | reasoning_effort
| thinking.type
reasoning_split
|
| Default | max | 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 K3 · 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-k3",
# 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-k3",
// 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-k3",
# "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-k3",
// 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-k3")
// .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 K3 or MiniMax M3?
MiniMax M3 is cheaper on the "Input / 1M tokens" row ($0.3 vs $3, 10× 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 K3 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 K3 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.