DeepSeek V4.1 Flash (1017) vs GPT-6.1 Sol
Which one, when
These two match on inputs (text plus image in, text out) and on chat, vision, code, reasoning and tools, with context windows of 1000000 and 1050000 tokens, so the split is price and output length. 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 max output tokens versus 128000, making it the pick for high-volume or long-generation work. Choose gpt-6.1-sol when you need structured output or batch requests, which deepseek-v4.1-flash-1017 does not list, or its stated 2026-04 knowledge cutoff.
Pricing
| DeepSeek V4.1 Flash (1017) | GPT-6.1 Sol | Δ | |
|---|---|---|---|
| Input / 1M tokens | $0.3 | $2 | 0.15× |
| Output / 1M tokens | $1.2 | $10 | 0.12× |
| Cache read / 1M tokens | $0.006 | $0.1 | 0.06× |
| 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-6.1 Sol | |
|---|---|---|
| Tool calling | yes | yes |
| Thinking control | yes (vendor dial not published) | always on |
| 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-6.1 Sol | |
|---|---|---|
| Input modalities | text image | text image |
| Output modalities | text | text |
| Released | 2026-10-17 | 2026-09-29 |
| 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-6.1 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-6.1 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
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-6.1 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
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-6.1 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
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-6.1 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
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-6.1-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-6.1-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-6.1-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-6.1-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-6.1-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-6.1 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-6.1 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-6.1 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.