Last refreshed 2026-06-15. Next refresh: weekly.
Why use Phind CodeLlama 34B V2 on Together AI?
Together AI offers Phind CodeLlama 34B V2 with pay-as-you-go pricing at $0.80/1M input tokens. Together AI is a platform for running open-source and proprietary LLMs with fast serverless and dedicated endpoints at competitive inference pricing.
Compare Phind CodeLlama 34B V2 across 3 providers to find the best fit for your use caseSetup recipe
Python + curlpip install togetherexport TOGETHER_API_KEY=...from together import Together
client = Together() # reads TOGETHER_API_KEY from env
response = client.chat.completions.create(
model="phind-codellama-34b-v2",phind-codellama-34b-v2Request example
from together import Together
client = Together() # reads TOGETHER_API_KEY from env
response = client.chat.completions.create(
model="phind-codellama-34b-v2",
messages=[{"role": "user", "content": "Hello"}]
)
print(response.choices[0].message.content)Gotchas
- Together uses "organization/model-name" format, e.g. "meta-llama/Llama-4-Scout-17B-16E-Instruct" or "Qwen/QwQ-32B". See the Together model catalog for the exact ID.
- The examples expect TOGETHER_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 | $0.80 |
| Output tokens | $0.80 |
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.