Last refreshed 2026-04-19. Next refresh: weekly.
Why use Phind CodeLlama 34B V2 on Fireworks AI?
Fireworks AI offers Phind CodeLlama 34B V2 with pay-as-you-go pricing at $0.90/1M input tokens. Fireworks AI offers a generative AI platform as a service, focusing on rapid product iteration and cost-efficient AI deployment.
Compare Phind CodeLlama 34B V2 across 3 providers to find the best fit for your use caseSetup recipe
Python + curlpip install openaiexport FIREWORKS_API_KEY=...import os
from openai import OpenAI
client = OpenAI(
api_key=os.environ["FIREWORKS_API_KEY"],accounts/fireworks/models/phind-code-llama-34b-v2Request example
import os
from openai import OpenAI
client = OpenAI(
api_key=os.environ["FIREWORKS_API_KEY"],
base_url="https://api.fireworks.ai/inference/v1"
)
response = client.chat.completions.create(
model="accounts/fireworks/models/phind-code-llama-34b-v2",
messages=[{"role": "user", "content": "Hello"}]
)
print(response.choices[0].message.content)Gotchas
- Use provider model ID "accounts/fireworks/models/phind-code-llama-34b-v2", not the LLMReference slug "phind-codellama-34b-v2".
- Fireworks model IDs use "accounts/fireworks/models/{model-name}" format, e.g. "accounts/fireworks/models/llama4-scout-instruct-basic" or "accounts/fireworks/models/deepseek-r1".
- The examples expect FIREWORKS_API_KEY; rename it only if your application config maps the new variable.
Compare Phind CodeLlama 34B V2 Across Providers
| Provider | Input (per 1M) | Output (per 1M) |
|---|---|---|
| DeepInfra | $0.20 | $0.45 |
| Together AI | $0.80 | $0.80 |
| Fireworks AI | $0.90 | $0.90 |
Pricing
| Type | Price (per 1M) |
|---|---|
| Input tokens | $0.90 |
| Output tokens | $0.90 |
Capabilities
About Phind CodeLlama 34B V2
Phind CodeLlama 34B v2 is a large language model designed specifically for code generation tasks, built on the CodeLlama architecture. It generates high-quality code in multiple programming languages such as Python, C/C++, TypeScript, and Java, and is instruction-tuned for enhanced usability. The model demonstrates strong benchmark performance, achieving a 73.8% pass@1 score on the HumanEval benchmark. It has been fine-tuned on a proprietary dataset of 1.5 billion tokens, focusing on instruction-answer pairs. Additionally, it showcases multi-lingual capabilities beyond programming languages and offers various quantized versions like GPTQ and GGUF for optimized performance and reduced memory usage.