DeepSeek V4.1 Flash (1017) vs GPT-6.1 Terra
Which one, when
For prompts up to 272000 input tokens, deepseek-v4.1-flash-1017 offers cheaper input, output and cache reads, with a much larger output ceiling. Choose gpt-6.1-terra when structured outputs or its slightly larger context window are required.
Pricing
| DeepSeek V4.1 Flash (1017) | GPT-6.1 Terra | Δ | |
|---|---|---|---|
| Input / 1M tokens | $0.3 | $1 | 0.3× |
| Output / 1M tokens | $1.2 | $5 | 0.24× |
| Cache read / 1M tokens | $0.006 | $0.05 | 0.12× |
| Cache write | no separate charge | 1.25x (30m) / 2.5x (12h) | - |
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 80 chat models on this billing unit (log scale)
Capabilities
| DeepSeek V4.1 Flash (1017) | GPT-6.1 Terra | |
|---|---|---|
| Tool calling | yes | yes |
| Thinking control | yes (vendor dial not published) | configurable |
| Structured output | - | yes |
| Prompt caching | implicit (automatic) | implicit + explicit |
| Cache lifetime | no fixed TTL (evicted when unused) | at least 30m by default, 12h option on the Responses API |
| Minimum cached prefix | not published | 1,024 tokens |
Specs
| DeepSeek V4.1 Flash (1017) | GPT-6.1 Terra | |
|---|---|---|
| Input modalities | text image | text image |
| Output modalities | text | text |
| Released | 2026-10-17 | 2026-10-26 |
| Knowledge cutoff | - | 2026-06 |
| 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 V4.1 Flash (1017) · GPT-6.1 Terra
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
GPT-6.1 Terra passed · 3 sentences
Enabling reasoning caused the model to process additional internal tokens before producing each answer, even though those tokens are not visible to users. Those tokens are generally billable, increasing the cost per request and potentially doubling the bill even if usage stayed the same. To confirm the cause, compare request counts, model pricing, and token usage—including reasoning tokens—before and after the change.
out 196 tok (+115 thinking) latency 4.6 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
GPT-6.1 Terra passed · 8/8 cases
```python def longest_run(xs): if len(xs) == 0: 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 original code counted matching *adjacent pairs* rather than items, so every nonempty run was undercounted by one, including a single-element input. It did update `best` for matches at the last index, but still undercounted those runs. The fix starts each run at length one and updates `best` as the run grows.
out 331 tok (+172 thinking) latency 6.3 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
GPT-6.1 Terra passed · 5/5 fields, guidance "Withheld pending the Q4 close"
{ "company": "Meridian Robotics", "quarter": "September quarter", "revenue_usd_m": null, "growth_pct": null, "guidance": "Withheld pending the Q4 close" }
out 317 tok (+261 thinking) latency 6.9 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
GPT-6.1 Terra passed · 120 words, 0 banned, 1 question
Meet Prompt Cache, a new API gateway feature that stores reusable prompts across providers, helping teams reduce latency and control costs without changing how they build. When a request matches a cached prompt, the gateway retrieves the stored result instead of sending the same work again. Need to route traffic between models or vendors? Prompt Cache works across supported providers, giving your applications a consistent caching layer as your stack evolves. Set expiration policies, monitor cache performance, and choose which requests qualify through familiar gateway controls. Your team can spend less time repeating requests and more time improving the experiences customers rely on. Start using Prompt Cache today and make every request work harder for your application and your budget.
out 579 tok (+440 thinking) latency 9.1 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="gpt-6.1-terra", # 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: "gpt-6.1-terra", // 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": "gpt-6.1-terra", # 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: "gpt-6.1-terra", // 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("gpt-6.1-terra") // 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 GPT-6.1 Terra?
DeepSeek V4.1 Flash (1017) is cheaper on the "Input / 1M tokens" row ($0.3 vs $1, 3.3× 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 GPT-6.1 Terra 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 GPT-6.1 Terra 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.