DeepSeek V4 Pro vs Qwen3.7 Plus
Which one, when — curated verdict, not a benchmark table
Both share a 1,000,000-token context and the same chat, code, reasoning and tools flags with thinking that can be turned off, so the split is really about price shape, output length and inputs. Pick deepseek-v4-pro when you need very long single responses (393216 max output tokens, 6x qwen3.7-plus's 65536) or heavy cache reuse, since its $0.0134 cache read is about a sixth of the $0.08 charged by qwen3.7-plus. Pick qwen3.7-plus for image and video input, its long-context flag, and cheaper fresh tokens at $0.4 input and $1.6 output versus $1.608 and $3.216 — roughly 4x and 2x less.
Pricing
| DeepSeek V4 Pro | Qwen3.7 Plus | Δ | |
|---|---|---|---|
| Input / 1M tokens | $1.608 | $0.4 | 4× |
| Output / 1M tokens | $3.216 | $1.6 | 2× |
| Cache read / 1M tokens | $0.0134 | $0.08 | 0.17× |
| Cache write | no separate charge | 1.25x | — |
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 Pro | Qwen3.7 Plus | |
|---|---|---|
| Tool use | yes | yes |
| Thinking control | configurable | configurable |
| Structured output | yes | yes |
| Prompt caching | implicit (automatic) | implicit + explicit |
| Cache lifetime | no fixed TTL (evicted when unused) | explicit: 5m, reset on hit |
| Minimum cached prefix | not published | 1024 tokens |
Specs
| DeepSeek V4 Pro | Qwen3.7 Plus | |
|---|---|---|
| Input modalities | text | text image video |
| Output modalities | text | text |
| Released | 2026-04-24 | 2026-06-01 |
| Context window | 1M | 1M |
| Max output | 393K | 66K |
| Thinking parameter |
|
|
| Accepted values | thinking.type
reasoning_effort
| enable_thinking
thinking_budget
preserve_thinking
|
| Default | enabled, with reasoning_effort high some complex agent requests are automatically set to max | on the Qwen3.7 Plus series is hybrid thinking with thinking enabled by default, and preserve_thinking is off |
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 Pro · Qwen3.7 Plus
One prompt, both models — measured through the gateway
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
Qwen3.7 Plus passed · 3 sentences
Enabling reasoning requires the model to generate extensive internal "thinking" tokens to process complex logic before delivering a final answer. Because our vendor charges us per token consumed, this hidden computational overhead effectively doubles our output volume for the exact same number of user requests. Consequently, our costs have doubled because we are now paying for the model's increased processing time to achieve higher-quality, more accurate results.
out 1428 tok (+1345 thinking) latency 25.2 s
Instruction following (exactly three sentences — countable), audience shaping (CFO register), and the hidden-thinking billing gap the token meters below expose.
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
Qwen3.7 Plus passed · 8/8 cases
## Fixed Code ```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 ``` ## Bug Explanation The function has two related bugs rooted in how it counts run lengths. First, `cur` is initialized to `0` and only incremented on matches, so it counts the number of *transitions* between equal elements rather than the total elements in the run—a run of N identical items only reaches `cur = N − 1`, undercounting by one. This also causes the single-element case to return `0`, since the loop never executes and `cur` never increments. […]
out 2317 tok (+2005 thinking) latency 40.5 s
Whether the fix is actually correct (runnable), explanation density, and token efficiency on a bounded task.
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
Qwen3.7 Plus passed · 5/5 fields, guidance null
{ "company": "Meridian Robotics", "quarter": "September", "revenue_usd_m": 412, "growth_pct": 11, "guidance": null }
out 1807 tok (+1750 thinking) latency 31.6 s
Schema obedience (no invented fields), hallucination pressure (guidance is explicitly withheld), and structured-output path differences.
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
Qwen3.7 Plus passed · 120 words, 0 banned, 1 question
Introducing PromptCache, our new API gateway feature designed to store and reuse prompts across multiple artificial intelligence providers. Have you ever noticed how repeatedly sending identical queries drains your budget and increases latency? PromptCache solves this by intelligently storing responses at the gateway level. When your application requests the same prompt from a different provider, the system instantly returns the cached result. This drastically reduces API costs and accelerates response times for your users. You can configure custom expiration times and set specific fallback rules for each vendor. You must stop paying twice for the exact same computation. Please upgrade your entire infrastructure today and experience much faster and cheaper integrations without changing a single line of your application code.
out 4453 tok (+4312 thinking) latency 76.8 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-pro",
# model="qwen3.7-plus", # 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-pro",
// model: "qwen3.7-plus", // 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-pro",
# "model": "qwen3.7-plus", # 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-pro",
// Model: "qwen3.7-plus", // 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-pro")
// .model("qwen3.7-plus") // 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 Pro or Qwen3.7 Plus?
Qwen3.7 Plus is cheaper on input / 1m tokens ($0.4 vs $1.608, 4.0× 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 Pro against Qwen3.7 Plus 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 Pro and Qwen3.7 Plus 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.