Mistral-Small-24B-Instruct-2501
Mistral Small 3 ( 2501 ) sets a new benchmark in the "small" Large Language Models category below 70B, boasting 24B parameters and achieving state-of-the-art capabilities comparable to larger models! This model is an instruction-fine-tuned version of the base model: Mistral-Small-24B-Base-2501.
Params
23.6 B
Context
32,768
Downloads 30d
74 K
Likes
987
Download history
tracking started — chart appears after 7 days of snapshots (5 recorded)74 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 | 94.3 GB | 107.8 GB | ❌ Won’t fit |
| model.safetensors (bf16, full) | bf16 + 32K ctx | 94.3 GB | 118.4 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-small-24b-instruct-2501
{
"hf_id": "mistralai/Mistral-Small-24B-Instruct-2501",
"params_b": 23.57,
"context_length": 32768,
"license": { "id": "apache-2.0", "commercial": "yes" },
"downloads_30d": 73988,
"vram_estimates": [
{ "quant": "bf16", "gb": 107.8 }
],
"updated_at": "2026-10-19T06:01:20Z"
}
Use via API
via OpenRouter · USD per 1M tokens · synced 2026-10-24$0.05
input / 1M
$0.08
output / 1M
—
cached input / 1M
$0.13
1M in + 1M out
- Context window
- 32,768 tokens
- Max output
- 16,384 tokens
- Providers
- 1
- Cheapest provider
- DeepInfra · $0.05 / $0.08
- Best throughput
- —
- AA Intelligence / Coding / Agentic
- 6.7 / — / —
✗ Tool calling
✗ Reasoning
✓ Structured outputs
✗ Image input
✗ File input
✗ Audio input
✗ Prompt caching
Specifications
- Architecture
- MistralForCausalLM
- Parameters
- 23.6 B
- Tensor type
- BF16
- Context length
- 32,768
- Vocabulary
- 131,072
- Layers / heads
- 40 / 32
- Licence
- apache-2.0
- First seen on the Hub
- 2025-01-28
- Training datasets
- undisclosed
- Added to our catalog
- 2026-10-20
Compare with any text-generation model