DeepSeek V4 Flash (0731) 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 deepseek-v4-flash-0731 and minimax-m3 are text-out models with a 1000000-token context, and minimax-m3 is the slightly cheaper side on fresh tokens: $0.3 per million input and $1.2 output against $0.44 and $1.32. deepseek-v4-flash-0731 keeps the edge on reuse, with cache reads at $0.044 against $0.06, roughly 1.4x less, though its 393216-token max output trails the 524288 cap. Pick minimax-m3 when you need image or video input, the larger max output, or the option to disable thinking. Pick deepseek-v4-flash-0731 for text-only chat, code and reasoning where cached prefixes dominate the bill.
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
| DeepSeek V4 Flash (0731) | MiniMax M3 | Δ | |
|---|---|---|---|
| Input / 1M tokens | $0.44 | $0.3 | 1.5× |
| Output / 1M tokens | $1.32 | $1.2 | 1.1× |
| Cache read / 1M tokens | $0.044 | $0.06 | 0.73× |
| Cache write | no separate charge | 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
| DeepSeek V4 Flash (0731) | MiniMax M3 | |
|---|---|---|
| Tool calling | yes | yes |
| Thinking control | yes (vendor dial not published) | configurable |
| Structured output | yes | - |
| Prompt caching | implicit (automatic) | implicit (automatic) |
| Cache lifetime | no fixed TTL (evicted when unused) | not published |
| Minimum cached prefix | not published | 512 tokens |
Specs
| DeepSeek V4 Flash (0731) | MiniMax M3 | |
|---|---|---|
| Input modalities | text | text image video |
| Output modalities | text | text |
| Released | 2026-07-31 | 2026-06-01 |
| Context window | 1M | 1M |
| Max output | 393K | 524K |
| 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: DeepSeek V4 Flash (0731) · 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="deepseek-v4-flash-0731",
# 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: "deepseek-v4-flash-0731",
// 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": "deepseek-v4-flash-0731",
# "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: "deepseek-v4-flash-0731",
// 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("deepseek-v4-flash-0731")
// .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, DeepSeek V4 Flash (0731) or MiniMax M3?
MiniMax M3 is cheaper on the "Input / 1M tokens" row ($0.3 vs $0.44, 1.5× 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 DeepSeek V4 Flash (0731) 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 DeepSeek V4 Flash (0731) 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.