Mistral Large 4
Q4 GGUF download: unsloth/Mistral-Large-4-1T-A52B-GGUF - 604.79 GB on disk, read 2026-11-02. VRAM figures above are estimates; this is the actual file size.
Intelligence benchmarks
Artificial Analysis indexes - compared with the best open and proprietary models
Intelligence
26.0
AA Index
Coding
18.9
AA Index
Agentic
37.7
AA Index
Intelligence Index - Mistral Large 4 vs. the field
Best open-weight models (you can run locally) and leading proprietary models for context.
Coding Index comparison
Agentic Index comparison
Benchmark data from Artificial Analysis · updated 2026-11-02.
Hosted API cost & speed
What it costs to rent this model instead of running it locally
| Metric | Value |
|---|---|
| Input (per 1M tokens) | $0.70 |
| Output (per 1M tokens) | $2.10 |
| Median speed (hosted API) | 161 tok/s |
Median across hosted providers, measured by Artificial Analysis - updated 2026-11-02. Local llama.cpp speed depends on your hardware — see the hardware pages for estimates.
Will it run on your hardware?
Pick your GPU memory - see which quantizations fit, and the cheapest card for the rest
Need an exact figure for your context length? Use the VRAM calculator.
Run it locally
Copy-paste - running in under a minute
vllm serve mistralai/Mistral-Large-4-1T-A52BNew to this? Start with Ollama · serve to many users with vLLM.
Deep dive
Notes, sources, and the full write-up
Mistral Large 4 is a 1050B-parameter other model from Mistral. It scores 26 on the Artificial Analysis Intelligence Index (coding 18.9). At Q4_K_M it needs roughly 609 GB of VRAM, placing it in the 48 GB+ / multi-GPU hardware tier.
BenchmarksLink to this section
Artificial Analysis Intelligence Index - Mistral Large 4 vs. leading closed models:
| Model | Intelligence | Coding | GPQA |
|---|---|---|---|
| Mistral Large 4 | 26 | 18.9 | - |
| Gemini 4 Carbon | 59.4 | 50.4 | - |
| Claude Opus 5.5 | 58.4 | 51.7 | - |
| GPT-6 Astra | 56.9 | 49.1 | 96.1 |
| GPT-6.1 Sol | 54.5 | 46.8 | - |
| Claude Sonnet 5.5 | 54.2 | 50.5 | - |
Source: Artificial Analysis (2026-11-02).
PopularityLink to this section
Mistral Large 4 has 4,576 downloads in the last month on HuggingFace and 1,453 likes.
Frequently asked
Quick answers to common questions
How much VRAM does Mistral Large 4 need?
Mistral Large 4 with 1050B parameters needs significant VRAM depending on quantization. Use our VRAM calculator for an exact estimate.
Is Mistral Large 4 better than other mistralai models?
Mistral Large 4 has 1050B parameters with 1,048,576 context - a strong choice for general use.
What license is Mistral Large 4 under?
Mistral Large 4 is released under the other license, making it suitable for most commercial and personal projects.
What hardware runs Mistral Large 4 well?
With 1050B parameters, Mistral Large 4 requires adequate VRAM. High-end GPUs like the RTX 4090 (24GB), RTX 5090 (32GB), or Mac Studio with unified memory are good options. Check our hardware directory for specific recommendations.
What is the best quantization for Mistral Large 4?
Q4_K_M is the recommended sweet spot - ~98% of FP16 quality at ~27% of the size. Step up to Q5_K_M or Q8_0 only if you have spare VRAM. Use our VRAM calculator to compare.
How long can Mistral Large 4's context window handle?
Mistral Large 4 supports a 1,048,576-token context window - enough for very long documents, codebases, or multi-turn conversations. Real-world usable context may vary by implementation.
What models compete with Mistral Large 4?
Mistral Large 4 competes with other 525B–1575B. Browse our model directory for comparisons, benchmarks, and community reviews to find the best fit.
Nearby options
Similar models and compatible hardware by spec