Qwen3.8 Max
Provider list prices: no platform markup, pay-as-you-go. These are official list prices. Logged-in customers may see effective prices including workspace discounts on /console/pricing. Effective input at a 70% cache-hit rate:$0.775/M. Implicit context cache is automatic; an explicit cache_control mode offers deeper discounts (1,024-token minimum blocks, 5-minute TTL that resets on hit). Per-model cached-read prices are in the pricing table.
Use Qwen3.8 Max in 30 seconds
OpenAI-compatible: swap the base_url, keep your SDK. POST /v1/chat/completions
from openai import OpenAI
client = OpenAI(
base_url="https://synthorai.io/v1",
api_key="sk-syn-...",
)
resp = client.chat.completions.create(
model="qwen3.8-max",
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: "qwen3.8-max",
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": "qwen3.8-max",
"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: "qwen3.8-max",
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("qwen3.8-max")
.addUserMessage("Summarize this diff")
.reasoningEffort(ReasoningEffort.MEDIUM)
.build());
System.out.println(resp.choices().get(0).message().content().orElse(""));Specs & limits
Tokens
- Context window (vendor spec)
- 983,616
- Max output (vendor spec)
- 131,072
Prompt caching
- How it caches
- automatic + explicit
- Lifetime
- explicit: 5m, reset on hit
Thinking
- Parameter
- reasoning_effort
- Values
- minimal · low · medium · high accepted set is the provider's
Model
- Modalities
- text + image → text
- Parameters
- 2.4T total MoE
- Flagship Qwen3.8 Max: natively vision-language, 2.4T-parameter MoE
- thinking trace returned in reasoning_content
- text and image in, text out
About Qwen3.8 Max
Qwen3.8 Max is Alibaba's flagship Qwen model, published on 3 August 2026.
- Alibaba describes it as a natively vision-language Max model built on a Mixture-of-Experts architecture with 2.4 trillion parameters, and as the most capable model in the Qwen line to date.
- It takes text and images and returns text, so a single request can mix a prompt with screenshots, charts or scanned pages instead of routing images through a separate vision model.
- Thinking is part of the default behaviour: the model returns its reasoning trace in a separate reasoning_content field alongside the answer, which means short replies still consume reasoning tokens and a very small max_tokens budget can come back with an empty answer even though the request succeeded.
- Function calling, strict JSON-schema structured output and streaming with usage in the final chunk are all available, so it drops into an existing OpenAI-compatible integration without special handling.
- Context is just under one million tokens and a single response can run to 131,072 tokens, which is double the output ceiling of the previous Max generation and the concrete reason to move long-form generation onto it.
- Pricing is tiered by cache behaviour rather than by context length: standard input, a cheaper rate for automatic cache hits, and a separate pair of rates for explicitly created and read cache entries, so prompt-heavy agent loops benefit materially from reusing a stable prefix.
- Alibaba lists availability in its Beijing and Singapore regions.
- Synthorai serves it through the OpenAI-compatible chat completions endpoint.
FAQ
Is the Qwen3.8 Max API free to try?
Yes: new accounts get 10 trial calls and up to $1 in free credit, no card required. At $2/M input tokens, that credit alone covers roughly 62 requests of ~8K tokens against Qwen3.8 Max.
What is Qwen3.8 Max best at?
2.4T-parameter MoE, natively vision-language; text and image in, 131K max output; tiered cache pricing for prompt-heavy loops. See the About section for the full picture from the vendor's own release notes.
How much does Qwen3.8 Max cost?
Qwen3.8 Max costs $2 per million input tokens and $6 per million output tokens on Synthorai. That is the provider's list price, with no platform markup. Cached input tokens bill at $0.25/M.
Does Qwen3.8 Max support prompt caching?
Yes: automatic caching is on by default, with an explicit mode for guaranteed savings. Cached input tokens bill at $0.25/M vs $2/M uncached (TTL explicit: 5m, reset on hit). Prompt caching guide →
How do I get access to Qwen3.8 Max?
Point your existing OpenAI SDK at base_url="https://synthorai.io/v1", set model="qwen3.8-max", and you're done. One API key covers every model on the gateway.