Free AI models that actually work
Most API providers charge for every token. Izzi API gives you 14 production-grade models at zero cost. No trial period, no credit card, no catch. Sign up, get your key, and start building.
These aren't toy models. DeepSeek R1 scores 79.8% on SWE-Bench Verified — higher than Claude 3.5 Sonnet. Qwen3 235B handles multilingual tasks better than GPT-4o. Llama 4 Maverick has a 256K context window.
Complete free model directory
| Model | Parameters | Context | Best for | SWE-Bench |
|---|---|---|---|---|
| DeepSeek R1 0528 | 671B MoE | 128K | Reasoning, debugging | 79.8% |
| DeepSeek V3 0324 | 671B MoE | 128K | General coding | 65.4% |
| Qwen3 235B | 235B MoE | 128K | Multilingual, coding | 62.1% |
| Qwen3 30B | 30B | 128K | Fast responses | 45.2% |
| Llama 4 Maverick | 400B MoE | 256K | Long context analysis | 48.7% |
| Llama 3.3 70B | 70B | 128K | Balanced quality/speed | 42.3% |
| Llama 3.1 8B | 8B | 128K | Classification, simple tasks | — |
| Gemma 3 27B | 27B | 128K | General purpose | — |
| Gemma 3 12B | 12B | 128K | Quick tasks | — |
| Mistral Small 3.1 | 24B | 128K | European languages | — |
| Phi-4 Reasoning Plus | 14B | 32K | Math, logic | — |
| Phi-4 Reasoning | 14B | 32K | Compact reasoning | — |
| Dolphin3 R1 | 70B | 128K | Creative, uncensored | — |
| Dolphin3 Llama 3.3 | 70B | 128K | General uncensored | — |
Quick start with free models
Python
from openai import OpenAI
client = OpenAI(
api_key="izzi-YOUR_KEY_HERE",
base_url="https://api.izziapi.com/v1"
)
# DeepSeek R1 — best free reasoning model
response = client.chat.completions.create(
model="deepseek-r1-0528",
messages=[{
"role": "user",
"content": "Find and fix the bug in this Python function:\n\ndef merge_sort(arr):\n if len(arr) <= 1:\n return arr\n mid = len(arr) // 2\n left = merge_sort(arr[:mid])\n right = merge_sort(arr[mid:])\n return merge(left, right)"
}],
max_tokens=2000
)
print(response.choices[0].message.content)Model selection guide
Python
def choose_free_model(task_type: str) -> str:
"""Pick the best free model for your task."""
model_map = {
"debugging": "deepseek-r1-0528", # Best reasoning
"code_gen": "deepseek-v3-0324", # Best code generation
"multilingual": "qwen3-235b-a22b", # Best for non-English
"long_document": "llama-4-maverick", # 256K context
"quick_task": "qwen3-30b-a3b", # Fastest response
"math": "phi-4-reasoning-plus", # Best math
"creative": "dolphin3-r1", # Uncensored creative
}
return model_map.get(task_type, "deepseek-v3-0324")Cost comparison: free vs. paid
Running 1 million tokens through each model:
| Model | Cost on Izzi API | Equivalent paid model | You save |
|---|---|---|---|
| DeepSeek R1 0528 | $0 | Claude Sonnet 4 ($12.60) | $12.60 |
| Qwen3 235B | $0 | GPT-5 ($8.75) | $8.75 |
| Llama 4 Maverick | $0 | Gemini 2.5 Pro ($7.88) | $7.88 |
Build a free AI pipeline
Combine free models for a zero-cost workflow:
Python
async def free_ai_pipeline(task: str) -> str:
"""Process task through free model chain."""
# Step 1: Classify with fast model
classification = await call_model("qwen3-30b-a3b", f"Classify: {task}")
# Step 2: Process with appropriate model
if "code" in classification.lower():
return await call_model("deepseek-r1-0528", task)
elif "translate" in classification.lower():
return await call_model("qwen3-235b-a22b", task)
else:
return await call_model("llama-4-maverick", task)