DeepSeek V4.1 Flash (1017) vs GPT-6 Sol
Which one, when
These two cover the same ground on paper: text and image in, text out, with chat, vision, code, reasoning and tools, and context windows of 1000000 and 1050000 tokens respectively, so the split is price and output shape. deepseek-v4.1-flash-1017 costs about 6.7x less on input ($0.3 vs $2), about 8.3x less on output ($1.2 vs $10), and allows 393216 output tokens against 128000, which suits long generations and cache-heavy pipelines at $0.006 per million cached input tokens. Pick gpt-6-sol when you want its toggle to disable thinking or its stated 2026-04 knowledge cutoff.
Benchmarks
DeepSeek V4.1 Flash (1017): the vendor has not published benchmark scores.
Vendor-published: Alibaba (Qwen) Anthropic DeepSeek Google OpenAI Z.ai
Pricing
| DeepSeek V4.1 Flash (1017) | GPT-6 Sol | Δ | |
|---|---|---|---|
| Input / 1M tokens | $0.3 | $2 | 0.15× |
| Output / 1M tokens | $1.2 | $10 | 0.12× |
| Cache read / 1M tokens | $0.006 | $0.2 | 0.03× |
| Cache write | no separate charge | no separate charge | - |
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 79 chat models on this billing unit (log scale)
Capabilities
| DeepSeek V4.1 Flash (1017) | GPT-6 Sol | |
|---|---|---|
| Tool calling | yes | yes |
| Thinking control | yes (vendor dial not published) | configurable |
| Structured output | - | yes |
| Prompt caching | implicit (automatic) | implicit (automatic) |
| Cache lifetime | no fixed TTL (evicted when unused) | 5-10m, up to 1h |
| Minimum cached prefix | not published | 1024 tokens |
Specs
| DeepSeek V4.1 Flash (1017) | GPT-6 Sol | |
|---|---|---|
| Input modalities | text image | text image |
| Output modalities | text | text |
| Released | 2026-10-17 | 2026-09-22 |
| Knowledge cutoff | - | 2026-04 |
| Context window | 1M | 1.1M |
| Max output | 393K | 128K |
| 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: DeepSeek V4.1 Flash (1017) · GPT-6 Sol
One prompt, both models, measured through the gateway
DeepSeek V4.1 Flash (1017) passed · 3 sentences
Enabling reasoning caused each request to generate additional internal reasoning tokens, which are billed like output tokens and can be several times longer than the final answer. Those extra tokens raise cost per call, and longer responses plus occasional retries pushed total usage to roughly double. We can control this with reasoning-effort limits, token budgets, caching, and spend monitoring by use case.
out 253 tok (+178 thinking) latency 5.4 s
GPT-6 Sol passed · 3 sentences
Enabling reasoning made the model generate extra internal tokens to work through each request, and those tokens are billed even though users don’t see them. If request volume stayed roughly the same, more billed tokens per request could explain why the bill doubled. We should check the usage breakdown to confirm, then lower reasoning effort or limit it to tasks that need it.
out 188 tok (+111 thinking) latency 5.0 s
Instruction following (exactly three sentences, so countable), audience shaping (CFO register), and the hidden-thinking billing gap the token meters below expose.
DeepSeek V4.1 Flash (1017) 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 `cur` starts at `0` and is incremented only when two adjacent items match, so it counts equal adjacent pairs rather than run length; a run of length `k` is recorded as `k - 1`. Consequently, a single-element input returns `0`, and a run ending at the last index—especially a one-element final run—is undercounted or never counted. The fix initializes every run to length `1`, increments on a match, resets to `1` on a mismatch, updates `best` each iteration, and handles the empty input separately.
out 1300 tok (+1095 thinking) latency 11.8 s
GPT-6 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 `cur` counted matching *adjacent pairs*, not items, so every run was one too short—including a single-element input, which returned 0. Starting each run at 1 counts its first item, and updating `best` on every iteration ensures a run that reaches the last index is included.
out 206 tok (+59 thinking) latency 5.9 s
Whether the fix is actually correct (runnable), explanation density, and token efficiency on a bounded task.
DeepSeek V4.1 Flash (1017) passed · 5/5 fields, guidance "withheld pending the Q4 close"
{"company":"Meridian Robotics","quarter":"September quarter","revenue_usd_m":412,"growth_pct":11,"guidance":"withheld pending the Q4 close"}
out 1375 tok (+1337 thinking) latency 12.2 s
GPT-6 Sol 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 227 tok (+185 thinking) latency 5.2 s
Schema obedience (no invented fields), hallucination pressure (guidance is explicitly withheld), and structured-output path differences.
DeepSeek V4.1 Flash (1017) passed · 120 words, 0 banned, 1 question
Today we’re introducing Cross-Provider Prompt Cache in the API gateway. It stores identical prompt requests once and serves cached responses across supported model providers, reducing duplicate calls, latency, and spend. Teams can set cache keys by model, temperature, system prompt, and custom metadata, then choose TTLs per route or workspace. The gateway handles provider fallback while keeping cached results consistent with your policy. Is your team paying twice for the same prompt? Cross-Provider Prompt Cache helps you answer that. Configure it in the gateway dashboard, inspect hit rates by provider, and invalidate entries on demand. Existing gateway routes need no code changes; enable the feature and define your caching rules. Cross-Provider Prompt Cache is available for all API gateway customers.
out 2487 tok (+2335 thinking) latency 17.1 s
GPT-6 Sol passed · 120 words, 0 banned, 1 question
Today, we’re introducing cross-provider prompt caching for our API gateway, helping teams reuse repeated prompt content when routing requests among supported AI providers. The gateway identifies eligible prompt prefixes, stores cache references, and applies them to subsequent requests where provider capabilities allow. Why send the same context again if it can be reused? With fewer duplicate input tokens, applications can reduce costs and improve latency without changing how developers call the gateway. Configure caching by route, monitor hit rates and savings in your dashboard, and keep existing provider fallback rules in place. Availability and cache behavior vary by provider, so the gateway reports eligibility and usage for each request. Get started today with our documentation and test your existing workloads.
out 586 tok (+443 thinking) latency 7.7 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="deepseek-v4.1-flash-1017",
# model="gpt-6-sol", # 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: "deepseek-v4.1-flash-1017",
// model: "gpt-6-sol", // 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": "deepseek-v4.1-flash-1017",
# "model": "gpt-6-sol", # 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: "deepseek-v4.1-flash-1017",
// Model: "gpt-6-sol", // 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("deepseek-v4.1-flash-1017")
// .model("gpt-6-sol") // 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, DeepSeek V4.1 Flash (1017) or GPT-6 Sol?
DeepSeek V4.1 Flash (1017) is cheaper on the "Input / 1M tokens" row ($0.3 vs $2, 6.7× 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 DeepSeek V4.1 Flash (1017) against GPT-6 Sol 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 DeepSeek V4.1 Flash (1017) and GPT-6 Sol 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.