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Phind CodeLlama 34B v2 vs Code Llama 13B Instruct: 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.

Replicate
Code
vs
Replicate
Code
Verdict

Phind CodeLlama 34B v2 and Code Llama 13B Instruct are closely matched on pricing and context. The right choice depends on your specific workload — see the table below for the full breakdown.

Side-by-side specs

SpecPhind CodeLlama 34B v2Code Llama 13B Instruct
ProviderReplicateReplicate
CategoryCodeCode
Input cost / 1M tokensn/an/a
Output cost / 1M tokensn/an/a
Context window16K tokens16K tokens
Max output tokens4,0964,096
Avg. latency——
Featured——
New——
Capabilities
text
text

Pricing example

A typical chat workload of 100,000 input tokens plus 50,000 output tokens.

Phind CodeLlama 34B v2
n/a

100K in × n/a + 50K out × n/a

Code Llama 13B Instruct
n/a

100K in × n/a + 50K out × n/a

Switch in one line

Both models live behind Railwail's OpenAI-compatible endpoint. Replace the model string and you are done.

JavaScript / TypeScript
import OpenAI from "openai";

const client = new OpenAI({
  apiKey: process.env.RAILWAIL_API_KEY,
  baseURL: "https://railwail.com/v1",
});

// Before — using Phind CodeLlama 34B v2
let r = await client.chat.completions.create({
  model: "kcaverly/phind-codellama-34b-v2-gguf",
  messages: [{ role: "user", content: "Hello" }],
});

// After — switched to Code Llama 13B Instruct
r = await client.chat.completions.create({
  model: "meta/codellama-13b-instruct",
  messages: [{ role: "user", content: "Hello" }],
});
Python
from openai import OpenAI

client = OpenAI(
    api_key=os.environ["RAILWAIL_API_KEY"],
    base_url="https://railwail.com/v1",
)

# Before — using Phind CodeLlama 34B v2
r = client.chat.completions.create(
    model="kcaverly/phind-codellama-34b-v2-gguf",
    messages=[{"role": "user", "content": "Hello"}],
)

# After — switched to Code Llama 13B Instruct
r = client.chat.completions.create(
    model="meta/codellama-13b-instruct",
    messages=[{"role": "user", "content": "Hello"}],
)
cURL
# Before — using Phind CodeLlama 34B v2
curl https://railwail.com/v1/chat/completions \
  -H "Authorization: Bearer $RAILWAIL_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "kcaverly/phind-codellama-34b-v2-gguf",
    "messages": [{"role": "user", "content": "Hello"}]
  }'

# After — switched to Code Llama 13B Instruct
curl https://railwail.com/v1/chat/completions \
  -H "Authorization: Bearer $RAILWAIL_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "meta/codellama-13b-instruct",
    "messages": [{"role": "user", "content": "Hello"}]
  }'

Which one wins for...

Quick verdicts derived from public specs. Always validate on your own workload.

Coding
Phind CodeLlama 34B v2

Higher coding category match or larger context wins.

Writing
Phind CodeLlama 34B v2

Bigger context window helps maintain long-form coherence.

Long documents
Phind CodeLlama 34B v2

The larger context window is the deciding factor.

Vision
Tie

Multimodal/vision support is required for image inputs.

Real-time chat
Tie

Lower average latency wins for interactive UX.

Cost-sensitive
Tie

The model with the lower input-token price wins.

Frequently asked questions

Which is cheaper, Phind CodeLlama 34B v2 or Code Llama 13B Instruct?
Phind CodeLlama 34B v2 and Code Llama 13B Instruct cannot be compared on per-token price: at least one of them has no per-token price listed (it is priced per run or not yet priced). See each model page for its current price.
Which has more context, Phind CodeLlama 34B v2 or Code Llama 13B Instruct?
Phind CodeLlama 34B v2 and Code Llama 13B Instruct have similar context windows (16K tokens vs 16K tokens).
Is Phind CodeLlama 34B v2 better than Code Llama 13B Instruct for coding?
For coding-heavy workloads we lean toward Phind CodeLlama 34B v2 on this comparison — it scores higher on the relevant heuristics (category, tags, or context window). Both models are usable for code via Railwail's OpenAI-compatible endpoint, so the safest path is to A/B test on your own prompts.
Can I use both Phind CodeLlama 34B v2 and Code Llama 13B Instruct via Railwail?
Yes. Both Phind CodeLlama 34B v2 and Code Llama 13B Instruct are accessible through a single Railwail API key and the OpenAI-compatible /v1/chat/completions endpoint. You only change the "model" parameter to switch between them — no SDK swap, no separate billing.
How do I switch from Phind CodeLlama 34B v2 to Code Llama 13B Instruct?
Replace the model identifier "kcaverly/phind-codellama-34b-v2-gguf" with "meta/codellama-13b-instruct" in your request payload. Everything else — API key, base URL, request shape — stays the same. See the code example on this page for the exact one-line change.

Try Phind CodeLlama 34B v2 and Code Llama 13B Instruct side by side

One API key, one endpoint, both models. Start free — no credit card required.

Get Started FreeView Phind CodeLlama 34B v2View Code Llama 13B Instruct