DeepSeek V4.1 Flash is the next release of DeepSeek's fast, economical V4 Flash line, and the headline change is that it is natively multimodal: the model card describes it as processing images and text natively and generating text autoregressively, so visual input no longer requires pairing it with a separate vision model.
- Input
- text image $0.3/M
- Output
- text $1.2/M
- Cache read
- $0.03/M
- Context
- 1M
- vs GPT-4o
- ~94% cheaper
Benchmarks
Vendor-published: Alibaba (Qwen) Anthropic ByteDance DeepSeek Google MiniMax Moonshot OpenAI Tencent Z.ai
Price in context
Where the price sits among 65 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 |
|---|---|
| Parameters | 552B total · 8B prefill / 16B decode active MoE, Causal Encoder-Decoder |
| License | MIT |
Natively multimodal successor to the V4 Flash line: a 552B-parameter MoE on a Causal Encoder-Decoder stack activating 8B parameters per token at prefill and 16B at decode, with the model card recommending a maximum output of at least 256K tokens.
One prompt, measured through the gateway
DeepSeek V4.1 Flash passed · 3 sentences
Enabling reasoning changed the unit economics: the model now spends extra billable tokens internally to “think” before producing the answer, and those reasoning tokens are charged like output tokens even though users never see them. That added token generation—plus longer contexts and occasional retries on harder prompts—roughly doubled our cost per request. We can control this by capping reasoning depth, routing only complex queries to reasoning models, and caching common prompts to bring the bill back down.
out 423 tok (+327 thinking) latency 14.6 s
Instruction following (exactly three sentences - countable), audience shaping (CFO register), and the hidden-thinking billing gap the token meters below expose.
DeepSeek V4.1 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 `cur` is counting equal adjacent pairs, not elements in the current run: every time `xs[i] == xs[i - 1]`, the run length increases by one element, but the first element of the run was never counted, so all runs are undercounted by one. Also, `best` starts at `0`, so single-element inputs return `0` instead of `1`, and a one-element run ending at the last index is missed entirely.
out 918 tok (+733 thinking) latency 11.4 s
Whether the fix is actually correct (runnable), explanation density, and token efficiency on a bounded task.
DeepSeek V4.1 Flash passed · 5/5 fields, guidance "withheld pending the Q4 close"
{"company":"Meridian Robotics","quarter":"September quarter","revenue_usd_m":412,"growth_pct":11.0,"guidance":"withheld pending the Q4 close"}
out 1707 tok (+1667 thinking) latency 15.1 s
Schema obedience (no invented fields), hallucination pressure (guidance is explicitly withheld), and structured-output path differences.
DeepSeek V4.1 Flash passed · 116 words, 0 banned, 1 question
Today we're launching Cross-Provider Prompt Cache for our API gateway. It stores identical prompt requests and their responses across supported model providers, then serves cached results when a match is found. Teams can cut duplicate inference costs, reduce latency, and keep behavior consistent during provider failover. The cache works with configurable TTLs, per-route rules, and cache-key controls, so you decide what is reusable and what must stay fresh. Does your application send the same prompts to multiple providers? Now your gateway can answer many of those calls without another upstream request. Existing observability dashboards show hit rates, saved tokens, and estimated spend reduction. Enable it in the gateway console, set your policy, and start caching today.
out 917 tok (+770 thinking) latency 9.0 s
Constraint obedience (word budget, banned-word list, the single question), style fingerprint, and length control.
Use DeepSeek V4.1 Flash 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",
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",
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",
"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",
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")
.addUserMessage("Summarize this diff")
.reasoningEffort(ReasoningEffort.MEDIUM)
.build());
System.out.println(resp.choices().get(0).message().content().orElse(""));About DeepSeek V4.1 Flash
- The design is a genuine departure rather than a retune.
- DeepSeek describes a Causal Encoder-Decoder architecture - a 40-layer Transformer arranged as a 20-layer causal encoder followed by a 20-layer decoder - over a 552B-parameter Mixture-of-Experts backbone that activates only 8B parameters per token during prefill and 16B during decode, a split aimed directly at input-heavy agentic workloads.
- Memory follows the same theme: Compressed Sparse Attention 2 together with FP4 main KV caching brings the global KV cache footprint to 890 bytes per token, roughly a quarter of DeepSeek V4 Flash, while SWA Bounded Replay cuts the persistent KV footprint to about an eighth.
- The context window runs to 1M tokens, the weights are MIT-licensed, and tool calling is supported.
- Reasoning is the part to budget for: the trace comes back separately from the answer and on short prompts it can account for almost all of the completion tokens, so a tight max_tokens will return an empty answer that is still billed in full for the tokens spent thinking.
- Reasoning effort is adjustable, and the model card documents it as a continuous control rather than a handful of named levels.
- Synthorai serves it through the OpenAI-compatible chat completions endpoint.
FAQ
Is the DeepSeek V4.1 Flash 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.
What is DeepSeek V4.1 Flash best at?
Natively multimodal - images and text in; 552B MoE, 8B active during prefill; 1M context with MIT-licensed weights. See the About section for the full picture from the vendor's own release notes.
How much does DeepSeek V4.1 Flash cost?
DeepSeek V4.1 Flash 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 $0.03/M.
Does DeepSeek V4.1 Flash support prompt caching?
Yes, automatically: DeepSeek-served prompts cache with no code changes. Cached input tokens bill at $0.03/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?
Point your existing OpenAI SDK at base_url="https://synthorai.io/v1", set model="deepseek-v4.1-flash", and you're done. One API key covers every model on the gateway.
Is DeepSeek V4.1 Flash 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.