MiniMax M3 vs Qwen3.8 Max
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
minimax-m3 is the cheaper of the two on every line of the rate card, at $0.3 input and $1.2 output versus $2 and $6 for qwen3.8-max - roughly 6.7x and 5x less - and it also accepts video alongside text and image, carries a 1000000-token context with up to 524288 tokens of output, and lets you turn thinking off. qwen3.8-max costs more and caps output at 131072 tokens on a 983616-token context, so reach for it when you want Alibaba's newer August 2026 model with an explicit vision flag on text and image work. For high-volume, long-output or video-input jobs, minimax-m3 is the economical pick.
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
| MiniMax M3 | Qwen3.8 Max | Δ | |
|---|---|---|---|
| Input / 1M tokens | $0.3 | $2 | 0.15× |
| Output / 1M tokens | $1.2 | $6 | 0.2× |
| Cache read / 1M tokens | $0.06 | $0.25 | 0.24× |
| Cache write | no separate charge | 1.25x | - |
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
| MiniMax M3 | Qwen3.8 Max | |
|---|---|---|
| Tool calling | yes | yes |
| Thinking control | configurable | yes (vendor dial not published) |
| Structured output | - | yes |
| Prompt caching | implicit (automatic) | implicit + explicit |
| Cache lifetime | not published | explicit: 5m, reset on hit |
| Minimum cached prefix | 512 tokens | 1024 tokens |
Specs
| MiniMax M3 | Qwen3.8 Max | |
|---|---|---|
| Input modalities | text image video | text image |
| Output modalities | text | text |
| Released | 2026-06-01 | 2026-08-03 |
| Context window | 1M | 984K |
| Max output | 524K | 131K |
| Thinking parameter |
| - |
| Accepted values | thinking.type
reasoning_split
| - |
| Default | 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: MiniMax M3 · Qwen3.8 Max
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="minimax-m3",
# model="qwen3.8-max", # 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: "minimax-m3",
// model: "qwen3.8-max", // 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": "minimax-m3",
# "model": "qwen3.8-max", # 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: "minimax-m3",
// Model: "qwen3.8-max", // 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("minimax-m3")
// .model("qwen3.8-max") // 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, MiniMax M3 or Qwen3.8 Max?
MiniMax M3 is cheaper on the "Input / 1M tokens" row ($0.3 vs $2, 6.7× 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 MiniMax M3 against Qwen3.8 Max 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 MiniMax M3 and Qwen3.8 Max 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.