GPT-5.6 Sol 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 and image in and return text, both can turn thinking off, and their context windows are close at 1050000 tokens for gpt-5.6-sol versus 1000000 for minimax-m3, so the real split is price and output shape. minimax-m3 costs about 16.7x less on input and 25x less on output ($0.3/$1.2 per million versus $5/$30), also accepts video, and can emit up to 524288 tokens against 128000 - pick it for high-volume, long-output, long-context work. Choose gpt-5.6-sol when you want OpenAI's July 2026 generation with an explicit vision capability flag and a 2026-02 knowledge cutoff.
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
| GPT-5.6 Sol | MiniMax M3 | Δ | |
|---|---|---|---|
| Input / 1M tokens | $5 | $0.3 | 17× |
| Output / 1M tokens | $30 | $1.2 | 25× |
| Cache read / 1M tokens | $0.5 | $0.06 | 8.3× |
| 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
| GPT-5.6 Sol | MiniMax M3 | |
|---|---|---|
| Tool calling | yes | yes |
| Thinking control | configurable | configurable |
| Structured output | yes | - |
| Prompt caching | implicit (automatic) | implicit (automatic) |
| Cache lifetime | 5-10m, up to 1h | not published |
| Minimum cached prefix | 1024 tokens | 512 tokens |
Specs
| GPT-5.6 Sol | MiniMax M3 | |
|---|---|---|
| Input modalities | text image | text image video |
| Output modalities | text | text |
| Released | 2026-07-09 | 2026-06-01 |
| Knowledge cutoff | 2026-02 | - |
| Context window | 1.1M | 1M |
| Max output | 128K | 524K |
| Thinking parameter | reasoning.effort |
|
| Accepted values | reasoning.effort
| thinking.type
reasoning_split
|
| Default | medium | 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: GPT-5.6 Sol · 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="gpt-5.6-sol",
# 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: "gpt-5.6-sol",
// 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": "gpt-5.6-sol",
# "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: "gpt-5.6-sol",
// 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("gpt-5.6-sol")
// .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, GPT-5.6 Sol or MiniMax M3?
MiniMax M3 is cheaper on the "Input / 1M tokens" row ($0.3 vs $5, 17× 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 GPT-5.6 Sol 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 GPT-5.6 Sol 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.