DeepSeek V4.1 Flash 1017 is the October 17, 2026 update of DeepSeek V4.1 Flash.
- Input
- text image $0.3/M
- Output
- text $1.2/M
- Cache read
- $0.006/M
- Context
- 1M
- vs GPT-4o
- ~94% cheaper
Price in context
Where the price sits among 70 comparable models
The bar shows how this model’s price compares with every other model of the same kind on Synthorai. The cheapest and the most expensive are named at each end. These are base rates; batch, region and cache-write discounts are on the pricing page.
Specs & limits
Tokens
| Context window (vendor spec) | 1,000,000 |
|---|---|
| Max output (vendor spec) | 393,216 |
Prompt caching
| How it caches | automatic |
|---|---|
| Lifetime | no fixed TTL (evicted when unused) |
Thinking
| Parameter | reasoning_effort |
|---|---|
| Values | minimal · low · medium · high accepted set is the provider's |
Model
| Modalities | text + image → text |
|---|---|
| License | MIT |
- October 2026 update of DeepSeek V4.1 Flash, which the model card calls a minor update with substantial improvements in reasoning, coding, agentic and visual tasks, and with shorter task execution time and output length. The card does not restate a parameter count or an architecture for this release, so neither is listed here
- it recommends a 1M context window and a maximum output of at least 256K tokens
One prompt, 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
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
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
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
Constraint obedience (word budget, banned-word list, the single question), style fingerprint, and length control.
Use DeepSeek V4.1 Flash (1017) in 30 seconds
OpenAI-compatible: swap the base_url, keep your SDK. POST /v1/chat/completions
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",
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",
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",
"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",
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")
.addUserMessage("Summarize this diff")
.reasoningEffort(ReasoningEffort.MEDIUM)
.build());
System.out.println(resp.choices().get(0).message().content().orElse(""));About DeepSeek V4.1 Flash (1017)
- The model card calls it a minor update rather than a new generation, with substantial improvements in reasoning, coding, agentic and visual tasks, and DeepSeek's release note describes the trade as a Pareto improvement in performance and cost: tasks finish faster and outputs are shorter than with the version it replaces.
- DeepSeek credits reinforcement learning with self-feedback for stronger end-to-end project work, from building a game from scratch to 3D modeling.
- Everything that set V4.1 Flash apart carries over: images and text go in natively and text comes out, the context window runs to 1M tokens, tool calling is supported, and the weights are MIT-licensed.
- The card does not restate a parameter count or an architecture for this release.
- It is still a reasoning model with thinking on by default, and the trace is billed as output tokens, so the shorter outputs bear directly on cost; the card recommends allowing a maximum output of at least 256K tokens.
- Synthorai serves it under its dated name, deepseek-v4.1-flash-1017, through the OpenAI-compatible chat completions endpoint.
FAQ
Is the DeepSeek V4.1 Flash (1017) API free to try?
Yes: new accounts get 10 trial calls and up to $1 in free credit, no card required. At $0.3/M input tokens, that credit alone covers roughly 416 requests of ~8K tokens against DeepSeek V4.1 Flash (1017).
What is DeepSeek V4.1 Flash (1017) best at?
Update with faster task completion and shorter outputs; images and text in, 1M context; MIT-licensed weights, thinking on by default. See the About section for the full picture from the vendor's own release notes.
How much does DeepSeek V4.1 Flash (1017) cost?
DeepSeek V4.1 Flash (1017) costs $0.3 per million input tokens and $1.2 per million output tokens on Synthorai. That is the provider's list price, with no platform markup. Cached input tokens bill at $0.006/M.
What is DeepSeek V4.1 Flash (1017)'s context window?
DeepSeek V4.1 Flash (1017) has a context window of 1M tokens on Synthorai. A single response can be up to 393,216 tokens long.
Does DeepSeek V4.1 Flash (1017) support prompt caching?
Yes, automatically: DeepSeek-served prompts cache with no code changes. Cached input tokens bill at $0.006/M vs $0.3/M uncached (TTL no fixed TTL (evicted when unused)). Prompt caching guide →
How do I get access to DeepSeek V4.1 Flash (1017)?
Point your existing OpenAI SDK at base_url="https://synthorai.io/v1", set model="deepseek-v4.1-flash-1017", and you're done. One API key covers every model on the gateway.
Is DeepSeek V4.1 Flash (1017) open source?
Yes: the weights are published under the MIT license (official repository linked in the About section). Or skip the GPUs: the hosted version here is pay-as-you-go with no infrastructure to run. Running open-weight models →
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Every value on this page is transcribed from the vendor's own documentation, linked above, and carries the date it was checked. Prices are compared across the catalogue; specification values that vendors define differently are shown with the difference stated rather than charted. Nothing here is measured by us, and nothing is scored.