WizardCoder 33B vs Code Llama 70B Instruct: Which AI Model Should You Choose?
Pricing, context windows, latency, capabilities, and a one-line code switch โ everything you need to pick the right model.
WizardCoder 33B and Code Llama 70B Instruct are closely matched on pricing and context. The right choice depends on your specific workload โ see the table below for the full breakdown.
Side-by-side specs
| Spec | WizardCoder 33B | Code Llama 70B Instruct |
|---|---|---|
| Provider | Replicate | Replicate |
| Category | Code | Code |
| Input cost / 1M tokens | n/a | n/a |
| Output cost / 1M tokens | n/a | n/a |
| Context window | 16K tokens | 16K tokens |
| Max output tokens | 4,096 | 4,096 |
| Avg. latency | โ | โ |
| Featured | โ | โ |
| New | โ | โ |
| Capabilities | text | text |
Pricing example
A typical chat workload of 100,000 input tokens plus 50,000 output tokens.
100K in ร n/a + 50K out ร n/a
100K in ร n/a + 50K out ร n/a
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 โ using WizardCoder 33B
let r = await client.chat.completions.create({
model: "lucataco/wizardcoder-33b-v1.1-gguf",
messages: [{ role: "user", content: "Hello" }],
});
// After โ switched to Code Llama 70B Instruct
r = await client.chat.completions.create({
model: "meta/codellama-70b-instruct",
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 โ using WizardCoder 33B
r = client.chat.completions.create(
model="lucataco/wizardcoder-33b-v1.1-gguf",
messages=[{"role": "user", "content": "Hello"}],
)
# After โ switched to Code Llama 70B Instruct
r = client.chat.completions.create(
model="meta/codellama-70b-instruct",
messages=[{"role": "user", "content": "Hello"}],
)# Before โ using WizardCoder 33B
curl https://railwail.com/v1/chat/completions \
-H "Authorization: Bearer $RAILWAIL_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "lucataco/wizardcoder-33b-v1.1-gguf",
"messages": [{"role": "user", "content": "Hello"}]
}'
# After โ switched to Code Llama 70B Instruct
curl https://railwail.com/v1/chat/completions \
-H "Authorization: Bearer $RAILWAIL_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "meta/codellama-70b-instruct",
"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 WizardCoder 33B and Code Llama 70B Instruct side by side
One API key, one endpoint, both models. Start free โ no credit card required.