GPT-6.1 Sol vs Qwen3.8 Flash
Which one, when
Both cover text and image input with chat, code, tools, reasoning and vision at near-equal scale (1050000 tokens of context and 128000 max output for gpt-6.1-sol, 1000000 and 131072 for qwen3.8-flash), so the real split is price and control. qwen3.8-flash costs about 13x less on input ($0.15 vs $2), roughly 21x less on output ($0.47 vs $10) and about 6x less on cache reads ($0.016 vs $0.1), also accepts video and lets you turn thinking off. Pick gpt-6.1-sol when you want OpenAI's newer 2026-09-29 release and the cost is acceptable.
Benchmarks
GPT-6.1 Sol: 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 Sol | Qwen3.8 Flash | Δ | |
|---|---|---|---|
| Input / 1M tokens | $2 | $0.15 | 13× |
| Output / 1M tokens | $10 | $0.47 | 21× |
| Cache read / 1M tokens | $0.1 | $0.016 | 6.3× |
| 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 76 chat models on this billing unit (log scale)
Capabilities
| GPT-6.1 Sol | Qwen3.8 Flash | |
|---|---|---|
| Tool use | yes | yes |
| Thinking control | always on | 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-6.1 Sol | Qwen3.8 Flash | |
|---|---|---|
| Input modalities | text image | text image video |
| Output modalities | text | text |
| Released | 2026-09-29 | 2026-08-27 |
| Knowledge cutoff | 2026-04 | - |
| Context window | 1.1M | 1M |
| Max output | 128K | 131K |
| Thinking parameter | reasoning.effort |
|
| Accepted values | reasoning.effort
| enable_thinking
thinking_budget
preserve_thinking
|
| Default | medium | on qwen3.8-flash is hybrid thinking with thinking enabled by default, thinking_budget defaults to the 262,144-token chain maximum, 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-6.1 Sol · Qwen3.8 Flash
One prompt, both models - measured through the gateway
GPT-6.1 Sol passed · 3 sentences
Enabling reasoning makes the model do more computational work before answering, and many providers bill the resulting reasoning tokens even though users never see them. That means the same number of requests can cost twice as much because each request consumes more billable tokens, even if the visible answers stay the same length. We should confirm the increase in our usage breakdown and reserve higher reasoning levels for tasks where the improvement justifies the added cost.
out 144 tok (+54 thinking) latency 6.6 s
Qwen3.8 Flash passed · 3 sentences
Enabling reasoning makes the model spend additional output tokens to plan and validate answers before responding, and those tokens are billed. It also increases compute and latency per request, so each task consumes more capacity than a non-reasoning call. As a result, billable tokens and compute roughly doubled, directly driving the higher invoice.
out 330 tok (+262 thinking) latency 8.9 s
Instruction following (exactly three sentences - countable), audience shaping (CFO register), and the hidden-thinking billing gap the token meters below expose.
GPT-6.1 Sol 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 function counts equal adjacent pairs rather than items in a run: a run of length `n` has only `n - 1` equal pairs, so a single-element input returns `0`, and a run ending at the last index is also undercounted. It does process the last index, so the issue is not a missing final check. Initializing each nonempty run to `1` and updating `best` after every iteration correctly counts items, including singleton and trailing runs; empty inputs still return `0`.
out 189 tok latency 6.3 s
Qwen3.8 Flash passed · 8/8 cases
```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 ``` The bug is that the original version counts adjacent equal pairs rather than items in a run, starting both `cur` and `best` at `0`, and it resets `cur` to `0` after a mismatch, thereby discarding the first element of the new run. That makes single-element inputs return `0`, and because every run is effectively measured in “steps” between equal elements, the last element is not counted—particularly noticeable when the longest run ends at the final index.
out 2123 tok (+1933 thinking) latency 43.8 s
Whether the fix is actually correct (runnable), explanation density, and token efficiency on a bounded task.
GPT-6.1 Sol passed · 5/5 fields, guidance "Withheld pending the Q4 close"
{ "company": "Meridian Robotics", "quarter": "September quarter", "revenue_usd_m": null, "growth_pct": "eleven and change", "guidance": "Withheld pending the Q4 close" }
out 345 tok (+285 thinking) latency 10.2 s
Qwen3.8 Flash passed · 5/5 fields, guidance null
{ "company": "Meridian Robotics", "quarter": "September quarter", "revenue_usd_m": 412, "growth_pct": 11, "guidance": null }
out 519 tok (+463 thinking) latency 10.5 s
Schema obedience (no invented fields), hallucination pressure (guidance is explicitly withheld), and structured-output path differences.
GPT-6.1 Sol passed · 120 words, 0 banned, 1 question
Introducing Cross-Provider Prompt Cache, a new API gateway feature that stores reusable prompts and manages caching across your supported AI providers. Why rebuild the same context every time your application switches models? With one configuration, teams can reuse shared instructions, standardize cache policies, and reduce repeated prompt processing wherever provider caching is available. The gateway handles provider-specific requirements while giving you clear visibility into cache hits, usage, and estimated savings. Set expiration windows, isolate cached content by project, and invalidate entries when prompts change. Your existing routing logic stays intact, so you can compare models without rebuilding your caching workflow. […]
out 588 tok (+435 thinking) latency 13.9 s
Qwen3.8 Flash passed · 120 words, 0 banned, 1 question
We are announcing prompt caching across providers in our API gateway. Store approved prompt outputs once and reuse them across supported model providers, cutting latency, cost, and duplicate token spend. The feature matches identical requests, checks validity, and returns cached results while preserving routing controls. Developers keep existing endpoints; the gateway manages storage, invalidation, and provider differences. This reduces noisy repeat calls, improves steady responses, and frees teams to focus on better agent workflows. OpenAI, Anthropic, Google, Mistral, and custom routes are supported. Cache hits appear in analytics with latency, token, and cost reductions visible. Check retention and privacy rules before enabling it. Ready to add cache controls to your gateway? Enable it in settings and watch spend drop now.
out 5958 tok (+5805 thinking) latency 88.8 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-6.1-sol",
# model="qwen3.8-flash", # 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-sol",
// model: "qwen3.8-flash", // 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-sol",
# "model": "qwen3.8-flash", # 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-sol",
// Model: "qwen3.8-flash", // 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-sol")
// .model("qwen3.8-flash") // 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 Sol or Qwen3.8 Flash?
Qwen3.8 Flash is cheaper on input / 1m tokens ($0.15 vs $2, 13× 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-6.1 Sol against Qwen3.8 Flash 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-6.1 Sol and Qwen3.8 Flash 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.