Last refreshed 2026-06-29. Next refresh: weekly.
Why use Mistral NeMo (2407) on Vercel AI Gateway?
Vercel AI Gateway offers Mistral NeMo (2407) with pay-as-you-go pricing at $0.02/1M input tokens. Vercel AI Gateway is a unified AI proxy providing a single OpenAI-compatible API endpoint to 275+ models from 25+ providers including Anthropic, OpenAI, Google, Meta, Mistral, DeepSeek, xAI, Alibaba, Amazon, ByteDance, Cohere, MiniMax, MoonshotAI, KwaiPilot, Black Forest Labs, Recraft, Voyage AI, NVIDIA, and more.
Compare Mistral NeMo (2407) across 7 providers to find the best fit for your use caseSetup recipe
Python + curlpip install openaiexport AI_GATEWAY_API_KEY=...import os
from openai import OpenAI
client = OpenAI(
api_key=os.environ["AI_GATEWAY_API_KEY"],mistral/mistral-nemoRequest example
import os
from openai import OpenAI
client = OpenAI(
api_key=os.environ["AI_GATEWAY_API_KEY"],
base_url="https://ai-gateway.vercel.sh/v1"
)
response = client.chat.completions.create(
model="mistral/mistral-nemo",
messages=[{"role": "user", "content": "Hello"}]
)
print(response.choices[0].message.content)Gotchas
- Use provider model ID "mistral/mistral-nemo", not the LLMReference slug "mistral-nemo".
- creator/model-name e.g. kwaipilot/kat-coder-pro-v2
- The examples expect AI_GATEWAY_API_KEY; rename it only if your application config maps the new variable.
Compare Mistral NeMo (2407) Across Providers
| Provider | Input (per 1M) | Output (per 1M) |
|---|---|---|
| Mistral AI Studio | $0.15 | $0.15 |
| OpenRouter | $0.02 | $0.03 |
| Fireworks AI | $0.20 | $0.20 |
| Bitdeer AI | $0.18 | $0.54 |
| SiliconFlow | $0.30 | $0.30 |
Pricing
| Type | Price (per 1M) |
|---|---|
| Input tokens | $0.02 |
| Output tokens | $0.04 |
Capabilities
No model capability flags are currently sourced.
About Mistral NeMo (2407)
Mistral NeMo is a 12B parameter open-source language model developed by Mistral AI, designed for efficient performance and reasoning tasks. With a 128K token context window, it excels at handling long documents and complex reasoning. The model is optimized for fast inference while maintaining strong performance across multiple benchmarks, making it suitable for enterprise deployments where balance between performance and resource efficiency is critical.