GPT-6 Astra vs Qwen3.7 Plus
Which one, when
Choose qwen3.7-plus for workloads requiring video input or lower costs, as it charges $0.4 per million input tokens and $1.6 per million output tokens. Opt for gpt-6-astra if your application demands larger generations, as it provides a 128000 maximum output limit compared to 65536. You will pay 25 times more for input with gpt-6-astra at $10 per million input tokens and $50 per million output tokens, though both models offer comparable context windows of 1050000 and 1000000 tokens respectively.
Benchmarks
Vendor-published: Alibaba (Qwen) Anthropic ByteDance DeepSeek Google MiniMax Moonshot OpenAI Tencent Z.ai
Pricing
| GPT-6 Astra | Qwen3.7 Plus | Δ | |
|---|---|---|---|
| Input / 1M tokens | $10 | $0.4 | 25× |
| Output / 1M tokens | $50 | $1.6 | 31× |
| Cache read / 1M tokens | $1 | $0.08 | 13× |
| 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 68 chat models on this billing unit (log scale)
Capabilities
| GPT-6 Astra | Qwen3.7 Plus | |
|---|---|---|
| Tool use | yes | yes |
| Thinking control | configurable | configurable |
| 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 Astra | Qwen3.7 Plus | |
|---|---|---|
| Input modalities | text image | text image video |
| Output modalities | text | text |
| Released | 2026-09-03 | 2026-06-01 |
| Knowledge cutoff | 2026-04 | - |
| Context window | 1.1M | 1M |
| Max output | 128K | 66K |
| Thinking parameter | reasoning.effort |
|
| Accepted values | reasoning.effort
| enable_thinking
thinking_budget
preserve_thinking
|
| Default | medium | on the Qwen3.7 Plus series is hybrid thinking with thinking enabled by default, and preserve_thinking is off |
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 Astra · Qwen3.7 Plus
One prompt, both models - measured through the gateway
GPT-6 Astra passed · 3 sentences
Enabling reasoning makes the model do more work per request, often generating internal reasoning tokens that are billed even though they do not appear in the final answer. If traffic, model choice, and pricing stayed the same, those extra tokens likely explain why our bill doubled—not longer answers or more users. We should verify the usage breakdown, then reserve higher reasoning levels for tasks where improved results justify the added cost.
out 283 tok (+196 thinking) latency 17.2 s
Qwen3.7 Plus passed · 3 sentences
Enabling reasoning requires the model to generate extensive internal "thinking" tokens to process complex logic before delivering a final answer. Because our vendor charges us per token consumed, this hidden computational overhead effectively doubles our output volume for the exact same number of user requests. Consequently, our costs have doubled because we are now paying for the model's increased processing time to achieve higher-quality, more accurate results.
out 1428 tok (+1345 thinking) latency 25.2 s
Instruction following (exactly three sentences - countable), audience shaping (CFO register), and the hidden-thinking billing gap the token meters below expose.
GPT-6 Astra 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 counts equal adjacent pairs rather than items, so every nonempty run is undercounted by one: a single-element input returns `0`, and a run of three equal items returns `2`. Starting and resetting `cur` to `1` counts the first item of each run, while the empty-input check preserves a result of `0`. Updating `best` after each iteration includes runs ending at the last index without needing a final flush; the original also updated during trailing runs, but undercounted them.
out 321 tok (+131 thinking) latency 13.5 s
Qwen3.7 Plus passed · 8/8 cases
## Fixed Code ```python def longest_run(xs): if not xs: return 0 best = 1 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 ``` ## Bug Explanation The function has two related bugs rooted in how it counts run lengths. First, `cur` is initialized to `0` and only incremented on matches, so it counts the number of *transitions* between equal elements rather than the total elements in the run—a run of N identical items only reaches `cur = N − 1`, undercounting by one. This also causes the single-element case to return `0`, since the loop never executes and `cur` never increments. […]
out 2317 tok (+2005 thinking) latency 40.5 s
Whether the fix is actually correct (runnable), explanation density, and token efficiency on a bounded task.
GPT-6 Astra passed · 5/5 fields, guidance "Formal guidance is withheld pending the "
{ "company": "Meridian Robotics", "quarter": "September quarter", "revenue_usd_m": null, "growth_pct": "eleven and change", "guidance": "Formal guidance is withheld pending the Q4 close." }
out 329 tok (+267 thinking) latency 16.9 s
Qwen3.7 Plus passed · 5/5 fields, guidance null
{ "company": "Meridian Robotics", "quarter": "September", "revenue_usd_m": 412, "growth_pct": 11, "guidance": null }
out 1807 tok (+1750 thinking) latency 31.6 s
Schema obedience (no invented fields), hallucination pressure (guidance is explicitly withheld), and structured-output path differences.
GPT-6 Astra passed · 120 words, 0 banned, 1 question
Introducing Cross Provider Prompt Cache, a new API gateway feature that reuses eligible prompt content across supported AI providers. Why pay to process the same context every time? Configure caching once at the gateway, then route requests between models while keeping shared instructions, reference material, and conversation prefixes ready for reuse. Caching controls let teams set expiration windows, isolate tenants, and exclude sensitive content. Cache analytics show hit rates, estimated savings, and latency trends, helping developers tune performance with confidence. Existing routing policies continue to work, so adoption fits your current architecture. Start with a single application, measure the results, and expand as needed. Available today in the dashboard and API, with documentation and examples to guide your first deployment.
out 665 tok (+516 thinking) latency 19.3 s
Qwen3.7 Plus passed · 120 words, 0 banned, 1 question
Introducing PromptCache, our new API gateway feature designed to store and reuse prompts across multiple artificial intelligence providers. Have you ever noticed how repeatedly sending identical queries drains your budget and increases latency? PromptCache solves this by intelligently storing responses at the gateway level. When your application requests the same prompt from a different provider, the system instantly returns the cached result. This drastically reduces API costs and accelerates response times for your users. You can configure custom expiration times and set specific fallback rules for each vendor. You must stop paying twice for the exact same computation. Please upgrade your entire infrastructure today and experience much faster and cheaper integrations without changing a single line of your application code.
out 4453 tok (+4312 thinking) latency 76.8 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-astra",
# model="qwen3.7-plus", # 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-astra",
// model: "qwen3.7-plus", // 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-astra",
# "model": "qwen3.7-plus", # 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-astra",
// Model: "qwen3.7-plus", // 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-astra")
// .model("qwen3.7-plus") // 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 Astra or Qwen3.7 Plus?
Qwen3.7 Plus is cheaper on input / 1m tokens ($0.4 vs $10, 25× 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 Astra against Qwen3.7 Plus 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 Astra and Qwen3.7 Plus 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.