DeepSeek V4.1 Flash (1017) vs Gemini 4 Argon
Which one, when
Both are text-out, reasoning-and-tools models with roughly a million tokens of context (1000000 for deepseek-v4.1-flash-1017, 1048576 for gemini-4-argon), so the split is cost and inputs. Pick deepseek-v4.1-flash-1017 for high-volume text and image work: at $0.3 input and $1.2 output it runs about 6.7x and 8.3x cheaper than gemini-4-argon's $2 and $10, with cache reads at $0.006 versus $0.1, and it allows 393216 output tokens against 65536. Choose gemini-4-argon when you need audio or video input, which deepseek-v4.1-flash-1017 does not accept.
Pricing
| DeepSeek V4.1 Flash (1017) | Gemini 4 Argon | Δ | |
|---|---|---|---|
| Input / 1M tokens | $0.3 | $2 | 0.15× |
| Output / 1M tokens | $1.2 | $10 | 0.12× |
| Cache read / 1M tokens | $0.006 | $0.1 | 0.06× |
| Cache write | no separate charge | - | - |
Rates from the live catalogue at build time; each model page carries the current rate card.
Where they sit · input price per 1M tokens across all 79 chat models on this billing unit (log scale)
Capabilities
| DeepSeek V4.1 Flash (1017) | Gemini 4 Argon | |
|---|---|---|
| Tool calling | yes | yes |
| Thinking control | yes (vendor dial not published) | yes (vendor dial not published) |
| Structured output | - | yes |
| Prompt caching | implicit (automatic) | implicit + explicit |
| Cache lifetime | no fixed TTL (evicted when unused) | not published |
| Minimum cached prefix | not published | 4096 tokens |
Specs
| DeepSeek V4.1 Flash (1017) | Gemini 4 Argon | |
|---|---|---|
| Input modalities | text image | text image audio video |
| Output modalities | text | text |
| Released | 2026-10-17 | 2026-10-13 |
| Knowledge cutoff | - | 2026-03 |
| Context window | 1M | 1M |
| Max output | 393K | 66K |
Specs are transcribed from each vendor’s documentation; a row a vendor does not publish is left out rather than inferred. Full sources: DeepSeek V4.1 Flash (1017) · Gemini 4 Argon
One prompt, both models, 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
Gemini 4 Argon passed · 3 sentences
Enabling the reasoning feature forces the AI to generate an extensive, hidden "chain of thought" to solve complex problems before delivering the final answer. Because LLM providers bill us per token generated, we are now paying for thousands of these invisible "thinking" words on top of the standard visible output. While this doubles our raw usage costs per query, it represents an investment in significantly higher accuracy and drastically reduced error rates for our most critical tasks.
out 91 tok latency 4.7 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
Gemini 4 Argon 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 bug occurs because the original function counts the number of matching adjacent *pairs* rather than the actual number of *elements* in a run, causing all non-empty results to be off by one. Because a single element constitutes a valid run of length 1, initializing and resetting `cur` and `best` to `0` instead of `1` means the first element of any sequence is never counted; this causes single-element lists (where the loop doesn't run) to wrongly return `0` and makes it seem like runs at the end are missed because a pair of matching elements only increments `cur` to `1` instead of `2`. […]
out 286 tok latency 7.5 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
Gemini 4 Argon passed · 5/5 fields, guidance "withheld pending the Q4 close"
```json { "company": "Meridian Robotics", "quarter": "September quarter", "revenue_usd_m": null, "growth_pct": "eleven and change", "guidance": "withheld pending the Q4 close" } ```
out 68 tok latency 5.8 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
Gemini 4 Argon passed · 120 words, 0 banned, 1 question
Are you paying twice for the exact same AI responses? Today, we are introducing Cross-Provider Prompt Caching for our API gateway, a new feature designed to reduce your artificial intelligence infrastructure costs and latency. When your applications send identical prompts to different large language models, our gateway now intercepts the request and serves a verified, cached response in milliseconds. It works across OpenAI, Anthropic, and open-source models simultaneously. If your primary provider experiences an outage, your fallback model still benefits from the shared cache Layer, preventing expensive redundant computations. Engineering teams can configure custom time-to-live settings, set similarity thresholds, and monitor cache hit rates directly from the dashboard. Activate Cross-Provider Prompt Caching in your gateway settings today and start saving.
out 156 tok latency 6.3 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-v4.1-flash-1017",
# model="gemini-4-argon", # 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-v4.1-flash-1017",
// model: "gemini-4-argon", // 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-v4.1-flash-1017",
# "model": "gemini-4-argon", # 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-v4.1-flash-1017",
// Model: "gemini-4-argon", // 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-v4.1-flash-1017")
// .model("gemini-4-argon") // 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 V4.1 Flash (1017) or Gemini 4 Argon?
DeepSeek V4.1 Flash (1017) is cheaper on the "Input / 1M tokens" row ($0.3 vs $2, 6.7× apart). Other rows may point the other way; the table above carries the full rate card, and real cost depends on your mix.
Can I A/B test DeepSeek V4.1 Flash (1017) against Gemini 4 Argon without two integrations?
Yes. Both are served through the same OpenAI-compatible endpoint with one API key. Switching is a one-line change to the model id, so you can route a fraction of traffic to each and compare bills directly.
Do DeepSeek V4.1 Flash (1017) and Gemini 4 Argon support prompt caching?
Yes. Both bill cache 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.