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Mistral AI • Vision Multimodal

Mistral Pixtral 12B Vision API Pricing & Multimodal Benchmarks (2026)

Complete 2026 developer pricing guide for Pixtral 12B. Benchmark token economics, context window limits, prompt caching discounts, and live inference latency across official provider endpoints.

⚡ 60-Second Pixtral Verdict ✓ Verified October 2026
128k Multimodal Window • Native Image Reasoning

Mistral Pixtral 12B Pricing Verdict: Pixtral 12B is Mistral's premier open multimodal model, priced at $0.15 per 1M input tokens and $0.15 per 1M output tokens. Processing high-resolution images natively with a 128k context window, it extracts complex tables, charts, and technical schematics at a 75% cost reduction compared to GPT-4o ($2.50/$10.00). Sizing your image tokens accurately with our Image Token Sizer and compare alternatives in Image & Video Gen Hub.

Input & Image Rate $0.15 / 1M Tokens
Output Text Rate $0.15 / 1M Tokens
Context Capacity 128,000 Tokens
Primary Sweet Spot PDF • OCR Document RAG
Input Token Rate
$0.15
per 1M input tokens
Output Token Rate
$0.15
per 1M generated tokens
Context Window
128,000
maximum token buffer
Avg Throughput
95
tokens/sec (typical speed)
Billing Dimension Standard Rate Cached / Optimized Rate Batch (50% Off) Context Window
Input Prompt $0.15 / M $0.08 / M 50% discount (24h SLA) 128,000 tokens
Output Generation $0.15 / M Standard metered 50% discount (24h SLA) Up to max model limit
Tool / Function Calling Standard input tokens Eligible for prefix cache Batch supported Shared context budget

Interactive Monthly Cost Estimator

Estimate your total bill based on prompt size, response volume, and request volume.

Estimated Monthly Bill
$0.00
Calculated using live 2026 rates for Pixtral 12B

Architectural Breakdown & Cost Optimization

Pixtral 12B represents a compelling value proposition in the 2026 developer ecosystem, balancing high accuracy with predictable operating margins. When designing agentic workflows or production RAG systems around Pixtral 12B, prompt engineering and caching strategy represent the single largest levers on your monthly bill.

To maximize unit economics:

Frequently Asked Questions

What is the token pricing for Pixtral 12B in 2026?
Pixtral 12B is billed at $0.15 per million input tokens and $0.15 per million output tokens under standard API tiers.
What is the context window capacity of Pixtral 12B?
Pixtral 12B supports up to 128,000 tokens, allowing long-document ingestion and extensive conversational history.
Does Pixtral 12B support prompt caching or batch discounts?
Yes, prompt caching reduces recurring prefix token costs to roughly $0.08 per million tokens for cached blocks.

Related Frontier Showdowns & Hubs

Mistral Pixtral 12B: Native Multimodal Vision Architecture & 128k Context TCO

Mistral's Pixtral 12B is a 12-billion-parameter open-weights multimodal vision-language model trained from scratch with native image reasoning. Unlike models that patch separate vision encoders onto text LLMs with fixed resolution downsampling, Pixtral uses a 400M parameter vision encoder that supports native arbitrary image resolutions and aspect ratios across a 128,000-token context window. Serving multiple high-resolution images in a single session costs just $0.15/1M input tokens on Mistral's API or runs locally on a single 16GB GPU.

Deployment Model Input / 1M Tokens Output / 1M Tokens Hardware Footprint Monthly Cost (50k Images)
Mistral API (Pixtral 12B) $0.15 / 1M $0.60 / 1M Managed Cloud Endpoint $15.00 / month
GPT-4o Vision (High-Res) $2.50 / 1M $10.00 / 1M Managed Cloud Endpoint $190.00 / month (12.6× higher)
Self-Hosted vLLM (1x RTX 4080) $0.00 $0.00 1x 16GB VRAM GPU (FP8) $180.00 / mo (Rented RunPod)

Production Pipeline: High-Resolution Document OCR with Mistral SDK

Extracting structured tabular data from complex receipts and PDF images using Pixtral 12B:

from mistralai import Mistral

client = Mistral(api_key="your-mistral-api-key")

def extract_invoice_ocr(image_url: str):
    response = client.chat.complete(
        model="pixtral-12b-2409",
        messages=[
            {
                "role": "user",
                "content": [
                    {"type": "text", "text": "Extract all line items, tax rates, and total amount into JSON."},
                    {"type": "image_url", "image_url": image_url}
                ]
            }
        ],
        response_format={"type": "json_object"}
    )
    return response.choices[0].message.content

Production Best Practices & Vision Optimization

Vision Encoder Patch Token Math & Multimodal VRAM Footprint

Mistral's Pixtral 12B integrates a 400M parameter native vision encoder directly with the 12B parameter language decoder, processing variable-resolution images and arbitrary aspect ratios without pre-cropping. In multimodal inference, image inputs are subdivided into 16x16 pixel patches which are converted into sequence tokens. A standard 1024x1024 resolution image produces approximately 4,096 vision tokens. Because vision tokens compete directly with text tokens for KV-cache allocation, deploying Pixtral 12B requires calculating image patch overhead against available GPU memory.

Pixtral 12B Image Resolution, Token Count, and Latency Table

Image Dimensions Vision Patch Tokens Inference VRAM (FP16) P95 TTFT API Cost / 1,000 Images
512 x 512 (Thumbnail) ~1,024 tokens 24.5 GB 320 ms $0.150
1024 x 1024 (Standard HD) ~4,096 tokens 27.2 GB 580 ms $0.614
2048 x 2048 (4K Technical PDF) ~16,384 tokens 36.8 GB 1,620 ms $2.457

Multimodal Ingestion Pipeline in Python with Mistral AI

Pass image URLs directly to Pixtral 12B for technical schematic extraction:

from mistralai import Mistral

client = Mistral(api_key=os.environ["MISTRAL_API_KEY"])

response = client.chat.complete(
    model="pixtral-12b-2409",
    messages=[
        {
            "role": "user",
            "content": [
                {"type": "text", "text": "Extract all equipment model numbers from this circuit diagram:"},
                {"type": "image_url", "image_url": "https://storage.company.com/blueprints/chiller-schematic.png"}
            ]
        }
    ]
)
print(response.choices[0].message.content)
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