DeepSeek V4 Pro (0813) vs GPT-6 Astra
Which one, when
Choose deepseek-v4-pro-0813 for text-based tasks requiring long responses, as it supports a 393216 maximum output and costs just $1.32 for input and $3.96 for output per million tokens. Select gpt-6-astra if your workload requires image inputs and vision capabilities, or if you need its slightly larger 1050000 token context window. These added capabilities come at a steep price increase, as gpt-6-astra charges $10 per million input tokens and $50 per million output tokens.
Benchmarks
Vendor-published: Alibaba (Qwen) Anthropic DeepSeek Google MiniMax Moonshot OpenAI Tencent Z.ai
Pricing
| DeepSeek V4 Pro (0813) | GPT-6 Astra | Δ | |
|---|---|---|---|
| Input / 1M tokens | $1.32 | $10 | 0.13× |
| Output / 1M tokens | $3.96 | $50 | 0.079× |
| Cache read / 1M tokens | $0.132 | $1 | 0.13× |
| 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 68 chat models on this billing unit (log scale)
Capabilities
| DeepSeek V4 Pro (0813) | GPT-6 Astra | |
|---|---|---|
| Tool use | yes | yes |
| Thinking control | always on | 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 Pro (0813) | GPT-6 Astra | |
|---|---|---|
| Input modalities | text | text image |
| Output modalities | text | text |
| Released | 2026-08-13 | 2026-09-03 |
| Knowledge cutoff | - | 2026-04 |
| Context window | 1M | 1.1M |
| Max output | 393K | 128K |
| Thinking parameter | reasoning_effort | reasoning.effort |
| Accepted values | reasoning_effort
| 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 Pro (0813) · GPT-6 Astra
One prompt, both models - measured through the gateway
DeepSeek V4 Pro (0813) passed · 3 sentences
Enabling reasoning causes the model to generate a hidden chain-of-thought with many additional tokens before producing the final answer, which sharply increases compute consumption per request. Those extra reasoning tokens are billed at the same or higher rates, so total usage doubled even though the volume of final outputs stayed similar. We can control this by adjusting reasoning effort, setting token limits, or routing only complex requests through reasoning mode.
out 308 tok (+226 thinking) latency 5.1 s
GPT-6 Astra passed · 3 sentences
Enabling reasoning makes the model do more work per request, often generating internal reasoning tokens that are billed even though they do not appear in the final answer. If traffic, model choice, and pricing stayed the same, those extra tokens likely explain why our bill doubled—not longer answers or more users. We should verify the usage breakdown, then reserve higher reasoning levels for tasks where improved results justify the added cost.
out 283 tok (+196 thinking) latency 17.2 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 (0813) 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 else: cur = 1 best = max(best, cur) return best ``` The bug is that `cur` was initialized to `0` and only counted adjacent matching pairs, so a run of length `n` produced `n - 1` (or `0` for a single element) instead of `n`. The code also reset to `0` on a change, when the new run should start at length `1`. Initializing `cur` and `best` to `1` fixes both the single-element case and runs ending at the last index.
out 1315 tok (+1130 thinking) latency 16.9 s
GPT-6 Astra 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 original code counts equal adjacent pairs rather than items, so every nonempty run is undercounted by one: a single-element input returns `0`, and a run of three equal items returns `2`. Starting and resetting `cur` to `1` counts the first item of each run, while the empty-input check preserves a result of `0`. Updating `best` after each iteration includes runs ending at the last index without needing a final flush; the original also updated during trailing runs, but undercounted them.
out 321 tok (+131 thinking) latency 13.5 s
Whether the fix is actually correct (runnable), explanation density, and token efficiency on a bounded task.
DeepSeek V4 Pro (0813) passed · 5/5 fields, guidance null
{"company":"Meridian Robotics","quarter":"September quarter","revenue_usd_m":412,"growth_pct":11.0,"guidance":null}
out 2155 tok (+2121 thinking) latency 26.0 s
GPT-6 Astra passed · 5/5 fields, guidance "Formal guidance is withheld pending the "
{ "company": "Meridian Robotics", "quarter": "September quarter", "revenue_usd_m": null, "growth_pct": "eleven and change", "guidance": "Formal guidance is withheld pending the Q4 close." }
out 329 tok (+267 thinking) latency 16.9 s
Schema obedience (no invented fields), hallucination pressure (guidance is explicitly withheld), and structured-output path differences.
DeepSeek V4 Pro (0813) passed · 121 words, 0 banned, 1 question
Introducing cross-provider prompt caching in our API gateway. Send a prompt once, and the gateway stores its computed prefix across Anthropic, OpenAI, and other LLM providers. Subsequent requests with the same prompt hit the cache, cutting latency and token costs while keeping outputs consistent across routing decisions and provider failovers. Teams can route identical prompts between providers without reprocessing shared context or lengthy system instructions. How much could you save on repeated prompt prefixes? The cache respects provider-specific key formats, handles TTLs automatically, and works with streaming and batch requests. Enable it with one configuration flag—no changes to your application code. Available today on all plans. Monitor cache hit rates, token savings, and provider-specific performance metrics in the live dashboard.
out 2845 tok (+2694 thinking) latency 25.5 s
GPT-6 Astra passed · 120 words, 0 banned, 1 question
Introducing Cross Provider Prompt Cache, a new API gateway feature that reuses eligible prompt content across supported AI providers. Why pay to process the same context every time? Configure caching once at the gateway, then route requests between models while keeping shared instructions, reference material, and conversation prefixes ready for reuse. Caching controls let teams set expiration windows, isolate tenants, and exclude sensitive content. Cache analytics show hit rates, estimated savings, and latency trends, helping developers tune performance with confidence. Existing routing policies continue to work, so adoption fits your current architecture. Start with a single application, measure the results, and expand as needed. Available today in the dashboard and API, with documentation and examples to guide your first deployment.
out 665 tok (+516 thinking) latency 19.3 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-pro-0813",
# model="gpt-6-astra", # 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-0813",
// model: "gpt-6-astra", // 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-0813",
# "model": "gpt-6-astra", # 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-0813",
// Model: "gpt-6-astra", // 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-0813")
// .model("gpt-6-astra") // 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 (0813) or GPT-6 Astra?
DeepSeek V4 Pro (0813) is cheaper on input / 1m tokens ($1.32 vs $10, 7.6× 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 (0813) against GPT-6 Astra 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 (0813) and GPT-6 Astra 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.