DeepSeek V4 Flash (0731) vs GPT-5.6
Which one, when — curated verdict, not a benchmark table
Both are text-in workhorses with roughly million-token context (1000000 for deepseek-v4-flash-0731, 1050000 for gpt-5.6) and the same chat, code, reasoning and tools flags, so the split is price and inputs. Pick deepseek-v4-flash-0731 for high-volume text work: at $0.308 input and $0.924 output it costs about 16x less on input and roughly 32x less on output than gpt-5.6 at $5 and $30, and it emits up to 393216 tokens versus 128000. Choose gpt-5.6 when you need image input or the option to disable thinking.
Pricing
| DeepSeek V4 Flash (0731) | GPT-5.6 | Δ | |
|---|---|---|---|
| Input / 1M tokens | $0.308 | $5 | 0.062× |
| Output / 1M tokens | $0.924 | $30 | 0.031× |
| Cache read / 1M tokens | $0.0308 | $0.5 | 0.062× |
| 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) | GPT-5.6 | |
|---|---|---|
| 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) | 5–10m, up to 1h |
| Minimum cached prefix | not published | 1024 tokens |
Specs
| DeepSeek V4 Flash (0731) | GPT-5.6 | |
|---|---|---|
| Input modalities | text | text image |
| Output modalities | text | text |
| Released | 2026-07-31 | 2026-07-09 |
| Knowledge cutoff | — | 2026-02 |
| 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 Flash (0731) · GPT-5.6
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
GPT-5.6 passed · 3 sentences
Enabling reasoning makes the model generate many additional internal tokens before producing an answer, so each request consumes substantially more compute. Those reasoning tokens are billed even though they are not visible to users, and longer processing can also increase tool calls, retries, and output length. The bill doubled because our cost per request rose, not necessarily because usage volume increased; we can control it by limiting reasoning effort and routing simpler tasks to cheaper models.
out 126 tok (+30 thinking) latency 3.0 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
GPT-5.6 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: best = max(best, cur) cur = 1 return max(best, cur) ``` The original code counted equal *transitions* rather than items, so a run of one had length `0`, a run of three had length `2`, and tracking from zero caused the off-by-one error. The corrected version starts each run at length `1`, resets to `1` when the value changes, and performs a final `max` after the loop so a run ending at the last element is included; an empty input still returns `0`.
out 277 tok (+93 thinking) latency 4.2 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
GPT-5.6 passed · 5/5 fields, guidance "withheld pending the Q4 close"
{ "company": "Meridian Robotics", "quarter": "September quarter", "revenue_usd_m": 412, "growth_pct": null, "guidance": "withheld pending the Q4 close" }
out 179 tok (+118 thinking) latency 3.7 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
GPT-5.6 passed · 120 words, 0 banned, 1 question
Today we are introducing Prompt Cache, a new API gateway feature that stores reusable prompt prefixes and serves them across supported model providers. By recognizing identical system instructions, tools, examples, and context blocks, Prompt Cache reduces repeated input processing, lowers latency, and helps control token costs without changing application code. Teams can configure retention windows, cache keys, provider routing, encryption, and regional storage policies from one dashboard or API. What happens when a preferred provider is unavailable? The gateway can route requests to another provider while reusing eligible cached content, preserving performance and consistency. Built-in metrics report hit rates, savings, latency, and provider usage, while audit logs support governance. Prompt Cache is available today in public preview for all customers.
out 628 tok (+473 thinking) latency 7.3 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="gpt-5.6", # 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: "gpt-5.6", // 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": "gpt-5.6", # 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: "gpt-5.6", // 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("gpt-5.6") // 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 GPT-5.6?
DeepSeek V4 Flash (0731) is cheaper on input / 1m tokens ($0.308 vs $5, 16× 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 GPT-5.6 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 GPT-5.6 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.