Mistral-Nemo-Instruct-2407
The Mistral-Nemo-Instruct-2407 Large Language Model (LLM) is an instruct fine-tuned version of the Mistral-Nemo-Base-2407. Trained jointly by Mistral AI and NVIDIA, it significantly outperforms existing models smaller or similar in size.
Params
12.2 B
Context
131,072
Downloads 30d
191 K
Likes
1,735
Download history
tracking started — chart appears after 7 days of snapshots (6 recorded)191 K downloads in the last 30 days
Can you run it?
Estimated VRAM at 8K context unless noted. Pick your hardware to see the verdict per quantization.
| File | Quant | Size | Est. VRAM | Verdict on RTX 4090 · 24 GB |
|---|---|---|---|---|
| model.safetensors | bf16 | 49.0 GB | 56.2 GB | ❌ Won’t fit |
| model.safetensors (bf16, full) | bf16 + 128K ctx | 49.0 GB | 83.8 GB | ❌ Won’t fit |
Estimate: file size × 1.1 + KV cache at 8K + 0.5 GB overhead. Not a benchmark — how we calculate this.
Run it
copy-paste, exact tags checked against the Hub$ curl -s https://aimodelscomparison.com/api/v1/models/mistral-nemo-instruct-2407
{
"hf_id": "mistralai/Mistral-Nemo-Instruct-2407",
"params_b": 12.25,
"context_length": 131072,
"license": { "id": "apache-2.0", "commercial": "yes" },
"downloads_30d": 191347,
"vram_estimates": [
{ "quant": "bf16", "gb": 56.2 }
],
"updated_at": "2026-10-19T06:01:40Z"
}
Use via API
via OpenRouter · USD per 1M tokens · synced 2026-10-24$0.029
input / 1M
$0.03
output / 1M
—
cached input / 1M
$0.059
1M in + 1M out
- Context window
- 131,072 tokens
- Max output
- 16,384 tokens
- Providers
- 5
- Cheapest provider
- Io Net · $0.017 / $0.022
- Best throughput
- —
- AA Intelligence / Coding / Agentic
- — / — / —
✓ Tool calling
✗ Reasoning
✓ Structured outputs
✗ Image input
✗ File input
✗ Audio input
✗ Prompt caching
Specifications
- Architecture
- MistralForCausalLM
- Parameters
- 12.2 B
- Tensor type
- BF16
- Context length
- 131,072
- Vocabulary
- 131,072
- Layers / heads
- 40 / 32
- Licence
- apache-2.0
- First seen on the Hub
- 2024-07-17
- Training datasets
- undisclosed
- Added to our catalog
- 2026-10-20
Compare with any text-generation model