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chutes: list free models
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Jun Siang Cheah committed
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@@ -246,12 +246,22 @@ This lists various services that provide free access or credits towards API-base
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<tr><td>Phi-4</td><td></td></tr>
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<tr><td>Phi-4-mini-instruct</td><td></td></tr>
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<tr><td>Phi-4-multimodal-instruct</td><td></td></tr>
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<tr>
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<td><a href="https://chutes.ai/">Chutes</a></td>
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<td>Distributed, decentralized crypto-based compute. Data is sent to individual hosts.</td>
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<td>Various open models</td>
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<td></td>
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</tr><tr><td rowspan="44"><a href="https://developers.cloudflare.com/workers-ai" target="_blank">Cloudflare Workers AI</a></td><td rowspan="44"><a href="https://developers.cloudflare.com/workers-ai/platform/pricing/#free-allocation">10,000 neurons/day</a></td><td>DeepSeek R1 Distill Qwen 32B</td><td></td></tr>
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<tr><td rowspan="15"><a href="https://chutes.ai/" target="_blank">Chutes</a></td><td rowspan="15">Distributed, decentralized crypto-based compute. Data is sent to individual hosts.</td><td>DeepHermes 3 Llama 3 8B Preview</td><td></td></tr>
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<tr><td>DeepSeek V3 0324</td><td></td></tr>
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<tr><td>DeepSeek V3 Base</td><td></td></tr>
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<tr><td>Dolphin 3.0 Mistral 24B</td><td></td></tr>
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<tr><td>Dolphin 3.0 R1 Mistral 24B</td><td></td></tr>
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<tr><td>Gemma 3 12B Instruct</td><td></td></tr>
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<tr><td>Gemma 3 1B Instruct</td><td></td></tr>
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<tr><td>Gemma 3 4B Instruct</td><td></td></tr>
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<tr><td>Llama 4 Maverick 17B 128E Instruct FP8</td><td></td></tr>
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<tr><td>Llama 4 Scout 17B 16E Instruct</td><td></td></tr>
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<tr><td>Mistral Small 3.1 24B Instruct 2503</td><td></td></tr>
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<tr><td>OlympicCoder 32B</td><td></td></tr>
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<tr><td>OlympicCoder 7B</td><td></td></tr>
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<tr><td>Qwen 2.5 VL 32B Instruct</td><td></td></tr>
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<tr><td>Reka Flash 3</td><td></td></tr>
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<tr><td rowspan="44"><a href="https://developers.cloudflare.com/workers-ai" target="_blank">Cloudflare Workers AI</a></td><td rowspan="44"><a href="https://developers.cloudflare.com/workers-ai/platform/pricing/#free-allocation">10,000 neurons/day</a></td><td>DeepSeek R1 Distill Qwen 32B</td><td></td></tr>
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<tr><td>Deepseek Coder 6.7B Base (AWQ)</td><td></td></tr>
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<tr><td>Deepseek Coder 6.7B Instruct (AWQ)</td><td></td></tr>
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<tr><td>Deepseek Math 7B Instruct</td><td></td></tr>
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@@ -247,6 +247,20 @@ MODEL_TO_NAME_MAPPING = {
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"@cf/meta/llama-4-scout-17b-16e-instruct": "Llama 4 Scout Instruct",
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"meta-llama/llama-4-scout:free": "Llama 4 Scout",
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"meta-llama/llama-4-maverick:free": "Llama 4 Maverick",
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"rekaai/reka-flash-3": "Reka Flash 3",
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"cognitivecomputations/dolphin3.0-mistral-24b": "Dolphin 3.0 Mistral 24B",
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"unsloth/gemma-3-12b-it": "Gemma 3 12B Instruct",
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"chutesai/llama-4-maverick-17b-128e-instruct-fp8": "Llama 4 Maverick 17B 128E Instruct FP8",
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"unsloth/gemma-3-1b-it": "Gemma 3 1B Instruct",
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"deepseek-ai/deepseek-v3-base": "DeepSeek V3 Base",
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"unsloth/gemma-3-4b-it": "Gemma 3 4B Instruct",
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"open-r1/olympiccoder-32b": "OlympicCoder 32B",
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"chutesai/llama-4-scout-17b-16e-instruct": "Llama 4 Scout 17B 16E Instruct",
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"cognitivecomputations/dolphin3.0-r1-mistral-24b": "Dolphin 3.0 R1 Mistral 24B",
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"open-r1/olympiccoder-7b": "OlympicCoder 7B",
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"nousresearch/deephermes-3-llama-3-8b-preview": "DeepHermes 3 Llama 3 8B Preview",
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"chutesai/mistral-small-3.1-24b-instruct-2503": "Mistral Small 3.1 24B Instruct 2503",
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"qwen/qwen2.5-vl-32b-instruct": "Qwen 2.5 VL 32B Instruct",
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}
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@@ -808,6 +822,35 @@ ONLY OUTPUT JSON!""",
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return ret_models
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def fetch_chutes_models(logger):
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logger.info("Fetching Chutes models...")
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r = requests.get(
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"https://api.chutes.ai/chutes/?include_public=true&limit=1000",
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headers={
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"Content-Type": "application/json",
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},
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)
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r.raise_for_status()
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models = r.json()["items"]
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logger.info(f"Fetched {len(models)} models from Chutes")
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# Filter for free models based on per_million_token price
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free_models = []
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for model in models:
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price_info = model.get("current_estimated_price", {})
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# Check if per_million_tokens field exists and is set to 0 for USD
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if (price_info.get("per_million_tokens", {}).get("usd", 1) == 0):
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model_name = model.get("name", "Unknown model")
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free_models.append({
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"id": model_name,
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"name": get_model_name(model_name),
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"description": model.get("tagline", "")
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})
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logger.info(f"Found {len(free_models)} free models from Chutes")
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return sorted(free_models, key=lambda x: x["name"])
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def get_human_limits(model):
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if "limits" not in model:
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return ""
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@@ -827,6 +870,7 @@ def main():
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# lambda_logger = create_logger("Lambda Labs")
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samba_logger = create_logger("SambaNova")
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scaleway_logger = create_logger("Scaleway")
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chutes_logger = create_logger("Chutes")
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fetch_concurrently = os.getenv("FETCH_CONCURRENTLY", "false").lower() == "true"
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@@ -841,6 +885,7 @@ def main():
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executor.submit(fetch_github_models, github_logger),
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executor.submit(fetch_samba_models, samba_logger),
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executor.submit(fetch_scaleway_models, scaleway_logger),
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executor.submit(fetch_chutes_models, chutes_logger),
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]
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(
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gemini_models,
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@@ -851,6 +896,7 @@ def main():
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github_models,
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samba_models,
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scaleway_models,
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chutes_models,
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) = [f.result() for f in futures]
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# Fetch groq models after others complete
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@@ -865,6 +911,7 @@ def main():
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# lambda_models = fetch_lambda_models(lambda_logger)
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samba_models = fetch_samba_models(samba_logger)
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scaleway_models = fetch_scaleway_models(scaleway_logger)
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chutes_models = fetch_chutes_models(chutes_logger)
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groq_models = fetch_groq_models(groq_logger)
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table = """<table>
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@@ -1040,12 +1087,16 @@ def main():
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table += "<td></td>"
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table += "</tr>\n"
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table += """<tr>
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<td><a href="https://chutes.ai/">Chutes</a></td>
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<td>Distributed, decentralized crypto-based compute. Data is sent to individual hosts.</td>
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<td>Various open models</td>
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<td></td>
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</tr>"""
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for idx, model in enumerate(chutes_models):
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table += "<tr>"
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if idx == 0:
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table += '<td rowspan="' + str(len(chutes_models)) + '">'
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table += '<a href="https://chutes.ai/" target="_blank">Chutes</a>'
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table += "</td>"
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table += '<td rowspan="' + str(len(chutes_models)) + '">Distributed, decentralized crypto-based compute. Data is sent to individual hosts.</td>'
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table += f"<td>{model['name']}</td>"
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table += "<td></td>"
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table += "</tr>\n"
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for idx, model in enumerate(cloudflare_models):
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table += "<tr>"
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