Grok 4.3 vs DeepSeek V3.1: Which AI Model Should You Choose?
Pricing, context windows, latency, capabilities, and a one-line code switch — everything you need to pick the right model.
Choose Grok 4.3 for cost-sensitive workloads — it is roughly 270.0× cheaper on input tokens. Choose DeepSeek V3.1 when you need its broader capabilities or stronger benchmarks.
Choose Grok 4.3 for long documents (1.0M tokens context). Choose DeepSeek V3.1 for shorter prompts where the smaller window keeps latency and cost down.
These models serve different use cases (Multimodal vs Text & Chat) — pick the one whose category matches your workload.
Side-by-side specs
| Spec | Grok 4.3 | DeepSeek V3.1 |
|---|---|---|
| Provider | xAI | DeepSeek |
| Category | Multimodal | Text & Chat |
| Input cost / 1M tokens | €0.0010 | €0.270 |
| Output cost / 1M tokens | €0.0030 | €1.10 |
| Context window | 1.0M tokens | 131K tokens |
| Max output tokens | 1,000,000 | 8,192 |
| Avg. latency | — | — |
| Featured | Yes | Yes |
| New | Yes | — |
| Capabilities | text image | text |
Pricing example
A typical chat workload of 100,000 input tokens plus 50,000 output tokens.
100K in × €0.0010 + 50K out × €0.0030
100K in × €0.270 + 50K out × €1.10
For this workload, Grok 4.3 is cheaper than DeepSeek V3.1 by €0.0818 per request.
Switch in one line
Both models live behind Railwail's OpenAI-compatible endpoint. Replace the model string and you are done.
import OpenAI from "openai";
const client = new OpenAI({
apiKey: process.env.RAILWAIL_API_KEY,
baseURL: "https://railwail.com/v1",
});
// Before — using Grok 4.3
let r = await client.chat.completions.create({
model: "grok-4.3",
messages: [{ role: "user", content: "Hello" }],
});
// After — switched to DeepSeek V3.1
r = await client.chat.completions.create({
model: "deepseek-chat",
messages: [{ role: "user", content: "Hello" }],
});from openai import OpenAI
client = OpenAI(
api_key=os.environ["RAILWAIL_API_KEY"],
base_url="https://railwail.com/v1",
)
# Before — using Grok 4.3
r = client.chat.completions.create(
model="grok-4.3",
messages=[{"role": "user", "content": "Hello"}],
)
# After — switched to DeepSeek V3.1
r = client.chat.completions.create(
model="deepseek-chat",
messages=[{"role": "user", "content": "Hello"}],
)# Before — using Grok 4.3
curl https://railwail.com/v1/chat/completions \
-H "Authorization: Bearer $RAILWAIL_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "grok-4.3",
"messages": [{"role": "user", "content": "Hello"}]
}'
# After — switched to DeepSeek V3.1
curl https://railwail.com/v1/chat/completions \
-H "Authorization: Bearer $RAILWAIL_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "deepseek-chat",
"messages": [{"role": "user", "content": "Hello"}]
}'Which one wins for...
Quick verdicts derived from public specs. Always validate on your own workload.
Higher coding category match or larger context wins.
Bigger context window helps maintain long-form coherence.
The larger context window is the deciding factor.
Multimodal/vision support is required for image inputs.
Lower average latency wins for interactive UX.
The model with the lower input-token price wins.
Frequently asked questions
Try Grok 4.3 and DeepSeek V3.1 side by side
One API key, one endpoint, both models. Start free — no credit card required.