DeepSeek V4 Flash (0731) vs DeepSeek V4 Pro
Which one, when — curated verdict, not a benchmark table
These two DeepSeek siblings match on the specs that usually drive a choice — a 1000000-token context, 393216 max output, text in and text out, and the same chat, code, reasoning and tools flags — so the real split is price and thinking control. Pick deepseek-v4-flash-0731 for volume work: at $0.308 input and $0.924 output it runs about 5.2x cheaper on input and 3.5x cheaper on output than deepseek-v4-pro's $1.608 and $3.216. Choose deepseek-v4-pro when you want to disable thinking per call, or when cache reads dominate, since its $0.0134 is roughly 2.3x cheaper there than Flash's $0.0308.
Pricing
| DeepSeek V4 Flash (0731) | DeepSeek V4 Pro | Δ | |
|---|---|---|---|
| Input / 1M tokens | $0.308 | $1.608 | 0.19× |
| Output / 1M tokens | $0.924 | $3.216 | 0.29× |
| Cache read / 1M tokens | $0.0308 | $0.0134 | 2.3× |
| Cache write | no separate charge | no separate charge | — |
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 63 chat models on this billing unit (log scale)
Capabilities
| DeepSeek V4 Flash (0731) | DeepSeek V4 Pro | |
|---|---|---|
| Tool use | yes | yes |
| Thinking control | yes — vendor dial not published | configurable |
| Structured output | yes | yes |
| Prompt caching | implicit (automatic) | implicit (automatic) |
| Cache lifetime | no fixed TTL (evicted when unused) | no fixed TTL (evicted when unused) |
| Minimum cached prefix | not published | not published |
Specs
| DeepSeek V4 Flash (0731) | DeepSeek V4 Pro | |
|---|---|---|
| Input modalities | text | text |
| Output modalities | text | text |
| Released | 2026-07-31 | 2026-04-24 |
| Context window | 1M | 1M |
| Max output | 393K | 393K |
| Thinking parameter | — |
|
| Accepted values | — | thinking.type
reasoning_effort
|
| Default | — | enabled, with reasoning_effort high some complex agent requests are automatically set to max |
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 Flash (0731) · DeepSeek V4 Pro
One prompt, both models — measured through the gateway
DeepSeek V4 Flash (0731) passed · 3 sentences
Enabling reasoning mode prompts the model to generate extensive internal chain-of-thought tokens before answering, often multiplying total token output per query by several times. This directly inflates costs because we are billed per token, and these reasoning tokens are processed at a premium compute tier that carries a higher price per million tokens. However, this spend buys measurably higher accuracy and fewer failed calls, so we are tracking it against specific error-reduction KPIs to ensure the ROI justifies the increase.
out 365 tok (+266 thinking) latency 5.1 s
DeepSeek V4 Pro passed · 3 sentences
Enabling reasoning causes the model to generate a lengthy internal chain‑of‑thought before it delivers a visible answer, and we are billed for every one of those hidden reasoning tokens just as we are for the final output. In most interactions, the volume of these unseen tokens at least equals the visible response, effectively doubling the total tokens consumed per query. That doubled token consumption translates directly into a doubled per‑query cost, which is why our overall LLM bill doubled.
out 641 tok (+545 thinking) latency 11.5 s
Instruction following (exactly three sentences — countable), audience shaping (CFO register), and the hidden-thinking billing gap the token meters below expose.
DeepSeek V4 Flash (0731) 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 best = max(best, cur) else: cur = 1 return best ``` The original bug stems from initializing `cur` to 0 and only incrementing it when adjacent elements are equal, which counts the number of *equal transitions* rather than the actual run length—so every run of length L is reported as L−1, and a single‑element run (with no transitions) yields 0. Moreover, because `best` is updated only inside the equality branch, it never accounts for a run that ends at the last element if that run is longer than any previously seen (though the real issue remains the off‑by‑one). […]
out 1268 tok (+1016 thinking) latency 14.0 s
DeepSeek V4 Pro 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 best = max(best, cur) else: cur = 1 return best ``` **Explanation:** The bug stems from initializing `cur = 0` and only incrementing it when two adjacent elements match, which counts the number of *additional* equal elements rather than the actual run length (which is `cur + 1`). As a result, the returned `best` is always one less than the true longest run—most obviously returning 0 for a single-element list instead of 1. […]
out 2418 tok (+2131 thinking) latency 35.6 s
Whether the fix is actually correct (runnable), explanation density, and token efficiency on a bounded task.
DeepSeek V4 Flash (0731) passed · 5/5 fields, guidance null
{ "company": "Meridian Robotics", "quarter": "Q3", "revenue_usd_m": 412, "growth_pct": null, "guidance": null }
out 1119 tok (+1072 thinking) latency 12.3 s
DeepSeek V4 Pro passed · 5/5 fields, guidance null
{ "company": "Meridian Robotics", "quarter": "September quarter", "revenue_usd_m": 412, "growth_pct": "eleven and change", "guidance": null }
out 1204 tok (+1153 thinking) latency 19.9 s
Schema obedience (no invented fields), hallucination pressure (guidance is explicitly withheld), and structured-output path differences.
DeepSeek V4 Flash (0731) missed · 141 words, 0 banned, 1 question
**Introducing Prompt Cache for API Gateway** We are pleased to announce Prompt Cache, a new feature that stores and reuses identical prompt prefixes across all connected LLM providers. By automatically recognizing repeated requests, this cache reduces token consumption and lowers latency without altering your application logic. Whether you use OpenAI, Anthropic, or open-source models, the cache operates transparently, ensuring consistent behavior while cutting costs. Developers can configure cache TTL and scoping rules via a simple API call, giving you precise control over data freshness and privacy. This feature also minimizes redundant upstream calls, freeing provider rate limits for more critical workloads. Start caching today through the dashboard or CLI, and watch your operational expenses drop significantly. […]
out 254 tok (+80 thinking) latency 4.4 s
DeepSeek V4 Pro no answer to grade · no answer text within 16,384 tokens (all of it went to thinking)
The model returned no answer text — the whole token budget went to hidden thinking.
out 8193 tok (+8192 thinking) latency 106.2 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-flash-0731",
# model="deepseek-v4-pro", # 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-flash-0731",
// model: "deepseek-v4-pro", // 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-flash-0731",
# "model": "deepseek-v4-pro", # 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-flash-0731",
// Model: "deepseek-v4-pro", // 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-flash-0731")
// .model("deepseek-v4-pro") // 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 Flash (0731) or DeepSeek V4 Pro?
DeepSeek V4 Flash (0731) is cheaper on input / 1m tokens ($0.308 vs $1.608, 5.2× 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 DeepSeek V4 Flash (0731) against DeepSeek V4 Pro 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 DeepSeek V4 Flash (0731) and DeepSeek V4 Pro 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.