GPT-6 Luna vs Qwen3.8 Max
Which one, when
These two cover the same ground on paper: text-and-image in, text out, with chat, vision, code, tools and reasoning on both, roughly a million tokens of context (1050000 for gpt-6-luna, 983616 for qwen3.8-max) and comparable ceilings of 128000 versus 131072 output tokens. The rate card is where they split: qwen3.8-max costs 20x more on input, 12x more on output and 25x more on cached reads, so gpt-6-luna is the default for high-volume work, and its ability to turn thinking off gives you a cheap non-reasoning mode too. Reach for qwen3.8-max only if you want Alibaba's long-context-flagged model specifically.
Benchmarks
Vendor-published: Alibaba (Qwen) Anthropic ByteDance DeepSeek Google MiniMax Moonshot OpenAI Tencent Z.ai
Pricing
| GPT-6 Luna | Qwen3.8 Max | Δ | |
|---|---|---|---|
| Input / 1M tokens | $0.1 | $2 | 0.05× |
| Output / 1M tokens | $0.5 | $6 | 0.083× |
| Cache read / 1M tokens | $0.01 | $0.25 | 0.04× |
| Cache write | no separate charge | 1.25x | - |
Rates from the live catalog at build time; each model page carries the current card.
Where they sit - input price per 1M tokens across all 74 chat models on this billing unit (log scale)
Capabilities
| GPT-6 Luna | Qwen3.8 Max | |
|---|---|---|
| Tool use | yes | yes |
| Thinking control | configurable | yes - vendor dial not published |
| Structured output | yes | yes |
| Prompt caching | implicit (automatic) | implicit + explicit |
| Cache lifetime | 5-10m, up to 1h | explicit: 5m, reset on hit |
| Minimum cached prefix | 1024 tokens | 1024 tokens |
Specs
| GPT-6 Luna | Qwen3.8 Max | |
|---|---|---|
| Input modalities | text image | text image |
| Output modalities | text | text |
| Released | 2026-09-22 | 2026-08-03 |
| Knowledge cutoff | 2026-05 | - |
| Context window | 1.1M | 984K |
| Max output | 128K | 131K |
| Thinking parameter | reasoning.effort | - |
| Accepted values | reasoning.effort
| - |
| Default | medium | - |
Specs are transcribed from each vendor’s documentation; a row a vendor does not publish is left out rather than inferred. Full sources: GPT-6 Luna · Qwen3.8 Max
One prompt, both models - measured through the gateway
GPT-6 Luna passed · 3 sentences
Enabling reasoning typically makes the model process more information and generate more internal tokens before producing its answer. Because usage is billed by tokens—and reasoning may also route requests to a more expensive model—the cost per request can rise sharply. If request volume stayed similar, that higher cost per request could explain why the bill doubled.
out 117 tok (+48 thinking) latency 3.4 s
Qwen3.8 Max passed · 3 sentences
Enabling reasoning makes the model produce additional hidden steps before responding, and those tokens are billable. It also tends to lengthen each interaction because the model works through more possibilities before settling on an answer. Therefore, the bill doubled mainly due to higher compute and token usage per request, not necessarily because the number of requests doubled.
out 378 tok (+305 thinking) latency 8.6 s
Instruction following (exactly three sentences - countable), audience shaping (CFO register), and the hidden-thinking billing gap the token meters below expose.
GPT-6 Luna passed · 8/8 cases
```python def longest_run(xs): if not xs: return 0 best = cur = 1 for i in range(1, len(xs)): if xs[i] == xs[i - 1]: cur += 1 else: cur = 1 best = max(best, cur) return best ``` The original code counted matching *adjacent pairs* rather than items, so a run of length `n` was reported as `n - 1`, and a single-item input returned `0`. This version counts the current item as part of each run and updates `best` at every index, including the last; it returns `0` for an empty input.
out 514 tok (+356 thinking) latency 6.9 s
Qwen3.8 Max passed · 8/8 cases
```python def longest_run(xs): if not xs: return 0 best = cur = 1 for i in range(1, len(xs)): if xs[i] == xs[i - 1]: cur += 1 else: cur = 1 best = max(best, cur) return best ``` The bug is that the original code starts `best` and `cur` at `0`, so it counts adjacent equal *transitions* rather than the number of items in the run. A run of length `n` has only `n - 1` equal-neighbor transitions, so single-element inputs return `0`, and runs that reach the end are undercounted by one. Initializing the current run to `1` for the first element, resetting it to `1` on a break, and updating `best` from that count fixes the off-by-one.
out 1616 tok (+1411 thinking) latency 34.7 s
Whether the fix is actually correct (runnable), explanation density, and token efficiency on a bounded task.
GPT-6 Luna passed · 5/5 fields, guidance "Withheld pending the Q4 close"
{"company":"Meridian Robotics","quarter":"September quarter","revenue_usd_m":412,"growth_pct":null,"guidance":"Withheld pending the Q4 close"}
out 161 tok (+119 thinking) latency 21.7 s
Qwen3.8 Max passed · 5/5 fields, guidance null
{ "company": "Meridian Robotics", "quarter": "September quarter", "revenue_usd_m": 412, "growth_pct": 11, "guidance": null }
out 1199 tok (+1141 thinking) latency 24.4 s
Schema obedience (no invented fields), hallucination pressure (guidance is explicitly withheld), and structured-output path differences.
GPT-6 Luna passed · 120 words, 0 banned, 1 question
Introducing Prompt Cache, a new API gateway feature that recognizes repeat prompt prefixes and reuses provider-side cached context across supported models. Teams can route requests to different AI providers while preserving eligible cache hits, reducing redundant input processing and helping lower latency and token costs. Configure cache policies in one place, monitor hit rates by provider, and keep existing client integrations unchanged. The gateway applies provider-specific rules automatically, so developers do not need to build separate caching logic for each endpoint. Which workflows could benefit from faster responses and predictable spend? Prompt Cache is available today in preview for eligible accounts, with usage details, supported providers, and setup guidance in the dashboard. Start with one route, compare results, then expand.
out 959 tok (+813 thinking) latency 14.6 s
Qwen3.8 Max passed · 120 words, 0 banned, 1 question
Today, our API gateway adds prompt caching across major model providers. It stores prompts and responses in one fast cache layer. Teams can lower token spend, reduce latency, and repeat reliable answers. The feature supports OpenAI, Anthropic, Google, and Mistral through one configuration. You can set retention rules, scope access, and invalidate entries quickly. How does your team maintain consistent results during provider outages? Approved cached responses keep applications stable while fallback routes recover. The dashboard shows hit rates, savings, latency, and provider usage. Engineers receive audit trails for every cached prompt, enabling safer testing. Product managers can compare cost trends before and after cache adoption. Start with a small route, then safely expand caching to production traffic right now.
out 2744 tok (+2591 thinking) latency 46.3 s
Constraint obedience (word budget, banned-word list, the single question), style fingerprint, and length control.
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-6-luna",
# 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: "gpt-6-luna",
// 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": "gpt-6-luna",
# "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: "gpt-6-luna",
// 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("gpt-6-luna")
// .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, GPT-6 Luna or Qwen3.8 Max?
GPT-6 Luna is cheaper on input / 1m tokens ($0.1 vs $2, 20× apart). Other rows may point the other way - the table above carries the full card, and real cost depends on your mix.
Can I A/B test GPT-6 Luna 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 model-string change, so you can route a fraction of traffic to each and compare bills directly.
Do GPT-6 Luna and Qwen3.8 Max support prompt caching?
Yes - both bill cached 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.