GPT-6.1 Terra vs Qwen3.8 Max
Which one, when
Up to 272000 input tokens, gpt-6.1-terra has lower input and output rates. Above 272000 input tokens, input prices match and qwen3.8-max has cheaper output; gpt-6.1-terra has more context, while qwen3.8-max allows slightly longer responses.
Benchmarks
GPT-6.1 Terra: the vendor has not published benchmark scores.
Vendor-published: Alibaba (Qwen) Anthropic ByteDance DeepSeek Google MiniMax Moonshot OpenAI Tencent Z.ai
Pricing
| GPT-6.1 Terra | Qwen3.8 Max | Δ | |
|---|---|---|---|
| Input / 1M tokens | $1 | $2 | 0.5× |
| Output / 1M tokens | $5 | $6 | 0.83× |
| Cache read / 1M tokens | $0.05 | $0.25 | 0.2× |
| Cache write | 1.25x (30m) / 2.5x (12h) | 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 80 chat models on this billing unit (log scale)
Capabilities
| GPT-6.1 Terra | Qwen3.8 Max | |
|---|---|---|
| Tool calling | yes | yes |
| Thinking control | configurable | yes (vendor dial not published) |
| Structured output | yes | yes |
| Prompt caching | implicit + explicit | implicit + explicit |
| Cache lifetime | at least 30m by default, 12h option on the Responses API | explicit: 5m, reset on hit |
| Minimum cached prefix | 1,024 tokens | 1,024 tokens |
Specs
| GPT-6.1 Terra | Qwen3.8 Max | |
|---|---|---|
| Input modalities | text image | text image |
| Output modalities | text | text |
| Released | 2026-10-26 | 2026-08-03 |
| Knowledge cutoff | 2026-06 | - |
| 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-6.1 Terra · Qwen3.8 Max
One prompt, both models, measured through the gateway
GPT-6.1 Terra passed · 3 sentences
Enabling reasoning caused the model to process additional internal tokens before producing each answer, even though those tokens are not visible to users. Those tokens are generally billable, increasing the cost per request and potentially doubling the bill even if usage stayed the same. To confirm the cause, compare request counts, model pricing, and token usage—including reasoning tokens—before and after the change.
out 196 tok (+115 thinking) latency 4.6 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, so countable), audience shaping (CFO register), and the hidden-thinking billing gap the token meters below expose.
GPT-6.1 Terra passed · 8/8 cases
```python def longest_run(xs): if len(xs) == 0: 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 every nonempty run was undercounted by one, including a single-element input. It did update `best` for matches at the last index, but still undercounted those runs. The fix starts each run at length one and updates `best` as the run grows.
out 331 tok (+172 thinking) latency 6.3 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-6.1 Terra passed · 5/5 fields, guidance "Withheld pending the Q4 close"
{ "company": "Meridian Robotics", "quarter": "September quarter", "revenue_usd_m": null, "growth_pct": null, "guidance": "Withheld pending the Q4 close" }
out 317 tok (+261 thinking) latency 6.9 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-6.1 Terra passed · 120 words, 0 banned, 1 question
Meet Prompt Cache, a new API gateway feature that stores reusable prompts across providers, helping teams reduce latency and control costs without changing how they build. When a request matches a cached prompt, the gateway retrieves the stored result instead of sending the same work again. Need to route traffic between models or vendors? Prompt Cache works across supported providers, giving your applications a consistent caching layer as your stack evolves. Set expiration policies, monitor cache performance, and choose which requests qualify through familiar gateway controls. Your team can spend less time repeating requests and more time improving the experiences customers rely on. Start using Prompt Cache today and make every request work harder for your application and your budget.
out 579 tok (+440 thinking) latency 9.1 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-6.1-terra",
# 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-6.1-terra",
// 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-6.1-terra",
# "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-6.1-terra",
// 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-6.1-terra")
// .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-6.1 Terra or Qwen3.8 Max?
GPT-6.1 Terra is cheaper on the "Input / 1M tokens" row ($1 vs $2, 2.0× 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-6.1 Terra 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 GPT-6.1 Terra 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.