Mistral Large 4 (Medium) is below average in intelligence, but reasonably priced when comparing to other open weight models of similar size. The model supports text and image input, outputs text, and has a 1M tokens context window.

Mistral Large 4 (Medium) scores 23 on the Artificial Analysis Intelligence Index, placing it below average among comparable models (median: 23). When evaluating the Intelligence Index, it generated 73M tokens, which is fairly concise in comparison to the median of 100M.

Pricing for Mistral Large 4 (Medium) is $0.70 per 1M input tokens (somewhat expensive, median: $0.43) and $2.10 per 1M output tokens (somewhat expensive, median: $1.50). On average, it costs $1.41 per task to evaluate Mistral Large 4 (Medium) on the Intelligence Index.

Reasoning	Yes This page shows the reasoning version of this model. A non-reasoning variant may also exist.
Input modality	Supports: text and image
Output modality	Supports: text
Context window	1M ~1500 A4 pages of size 12 Arial font
Total parameters	1.1T
Active parameters	52B Number of parameters active per token during inference
License	Mistral Ecosystem Open-Weight License
Model weights	Hugging Face

Metrics are compared against models of the same class:

Non-reasoning models → compared only with other non-reasoning models
Reasoning models → compared across both reasoning and non-reasoning
Open weights models → compared only with other open weights models of the same size class:

Tiny: ≤4B parameters
Small: 4B–40B parameters
Medium: 40B–150B parameters
Large: >150B parameters

Proprietary models → compared across proprietary and open weights models of the same price range, using a blended 3:1 input/output price ratio:

<$0.15 per 1M tokens
$0.15–$1 per 1M tokens
>$1 per 1M tokens

Updated

Intelligence

Artificial Analysis Intelligence Index · Higher is better

No publicly available API

Speed

Output tokens per second · Higher is better

No publicly available API

Cost per Task

Weighted average cost (USD) per Intelligence Index task · Lower is better

No publicly available API

IntelligenceUpdated

Artificial Analysis Intelligence Index

Artificial Analysis Intelligence Index v5.0 incorporates 9 evaluations: AA-Briefcase v1.1, AutomationBench-AA, Terminal-Bench 4.0, FrontierCode 2.0, AA-Omniscience v1.1, GDP.pdf, HLE-Diamond, CritPt v2 (Hard Subset), and Terminal-Bench-Science 0.1

No publicly available API

Artificial Analysis Intelligence Index v5.0 includes: AA-Briefcase v1.1, AutomationBench-AA, Terminal-Bench 4.0, FrontierCode 2.0, AA-Omniscience v1.1, GDP.pdf, HLE-Diamond, CritPt v2 (Hard Subset), Terminal-Bench-Science 0.1. See Intelligence Index methodology for further details, including a breakdown of each evaluation and how we run them.

Artificial Analysis Intelligence Index by Open Weights / Proprietary

No publicly available API

Indicates whether the model weights are available. Models are labelled as 'Commercial Use Restricted' if commercial use is limited by conditions, and as 'Non-commercial' if the license prohibits commercial use.

Intelligence Index vs. Cost per Task

Artificial Analysis Intelligence Index · Weighted average cost (USD) per Artificial Analysis Intelligence Index task

Most attractive quadrant

Pareto line

Weighted average cost per Intelligence Index task. Each evaluation’s cost is calculated from input, cache hit, cache write, reasoning, and answer token prices, divided by task count, and weighted by its Intelligence Index weight.

Capability Indexes

Measures the performance of models on specific capabilities and industries

Artificial Analysis Finance & Accounting Index

Incorporates 5 evaluations: AA-Briefcase v1.1, AA-Omniscience v1.1, HLE-Diamond, AutomationBench-AA, GDP.pdf · Higher is better

No publicly available API

Benchmarks

Evaluations

Evaluations measured independently by Artificial Analysis · AA-PersonalityBench scores are percentile ranks from 1 to 100

No publicly available API

AA-Briefcase v1.1

Agentic knowledge work, Elo

AA-Omniscience v1.1 AccuracyUpdated

Knowledge

AA-Omniscience v1.1 Non-Hallucination RateUpdated

1 - hallucination rate

AutomationBench-AA

Agentic SaaS workflows

Terminal-Bench-Science 0.1New

Agentic scientific research workflows in a terminal

FrontierCode 2.0New

Agentic software engineering in open-source repositories

HLE-DiamondNew

Reasoning & knowledge

GDP.pdf

Professional document reasoning, All-pass

Terminal-Bench 4.0

Agentic coding & terminal use

CritPt v2 (Hard Subset)

Physics reasoning

AA-AnalystAgent

Quantitative analysis on spreadsheets & documents

Harvey LAB-AA v1.1Updated

Legal agentic work, Hallucination-Gated All-Pass Rate

EnterpriseOps-Gym-AA

Agentic business operations

GDPval-AA v2.1

Agentic real-world work tasks, Elo

AA-3DNew

Agentic interactive 3D experiences, Elo

Code2Video-AANew

Product and brand videos designed in code, Elo

ITBench-AA

Kubernetes incident root-cause analysis

MLCR-AAUpdated

Medical long context reasoning

MMMU-Pro

Visual reasoning

AA-PersonalityBench: Expressiveness ScoreNew

Rich, varied, well-crafted language

AA-PersonalityBench: Organization ScoreNew

Well-structured, scannable, answer-first

AA-PersonalityBench: Transparency ScoreNew

Truthful, acknowledges uncertainty, non-sycophantic

AA-PersonalityBench: Slop ScoreNew

Use of filler and generic language

While model intelligence generally translates across use cases, specific evaluations may be more relevant for certain use cases.

AA-Briefcase v1.1

AA-Briefcase Elo

AA-Briefcase v1.1 is an agentic knowledge work benchmark developed by Artificial Analysis. AA-Briefcase Elo is a combined metric that aggregates rubric pass rate, analytical quality Elo and presentation Elo · Higher is better

No publicly available API

AA-Briefcase Elo is a combined metric that aggregates analytical quality Elo, presentation Elo, and rubric pass rate, with rubric performance converted into Elo via synthetic head-to-head matches. Elo and 95% confidence interval bounds are clamped at 0.

AA-Omniscience v1.1

AA-Omniscience Index

AA-Omniscience v1.1 Index (higher is better) measures knowledge reliability and hallucination. It rewards correct answers, penalizes hallucinations, and has no penalty for refusing to answer. Scores range from -100 to 100, where 0 means as many correct as incorrect answers, and negative scores mean more incorrect than correct.

No publicly available API

Openness Index

Artificial Analysis Openness Index: Score

Openness Index assesses model openness on a 0 to 100 normalized scale (higher is more open)

Intelligence Index Comparisons

Intelligence Index vs. Cost per Task

Artificial Analysis Intelligence Index · Weighted average cost (USD) per Artificial Analysis Intelligence Index task

Most attractive quadrant

Pareto line

Token Use

Output Tokens per Intelligence Index Task

Weighted average number of output tokens used to run one task in the Artificial Analysis Intelligence Index

No publicly available API

The number of tokens required per Intelligence Index task. This is calculated by multiplying the output tokens per eval by the relative weights of each benchmark in the Intelligence Index, then dividing by task count (excluding repeats).

Cost

Cost per Intelligence Index Task

Weighted average cost (USD) per Intelligence Index task · Segmented by token type · Lower is better

No publicly available API

Cost to Run Artificial Analysis Intelligence Index

Cost (USD) to run all evaluations in the Artificial Analysis Intelligence Index

No publicly available API

The cost to run the evaluations in the Artificial Analysis Intelligence Index, calculated using the model's input, cache hit, cache write, reasoning, and answer token prices and the number of tokens used across evaluations (excluding repeats).

Pricing: Cache Hit, Input, and Output

Price (USD per M Tokens)

No publicly available API

Price per token for cached prompts (previously processed), typically offering a significant discount compared to regular input price, represented as USD per million tokens. The values shown here are the cache hit price; cache write and cache storage are billed separately and vary by provider — see "Cache pricing by provider" for detail.

Context Window

Context window: tokens limit · Higher is better

No publicly available API

Larger context windows are relevant to RAG (Retrieval Augmented Generation) LLM workflows which typically involve reasoning and information retrieval of large amounts of data.

Maximum number of combined input & output tokens. Output tokens commonly have a significantly lower limit (varied by model).

Model Size (Open Weights Models Only)

Model Size: Total and Active Parameters

Comparison between total model parameters and parameters active during inference (billions)

The total number of trainable weights and biases in the model, expressed in billions. These parameters are learned during training and determine the model's ability to process and generate responses.

The number of parameters actually executed during each inference forward pass, expressed in billions. For Mixture of Experts (MoE) models, a routing mechanism selects a subset of experts per token, resulting in fewer active than total parameters. Dense models use all parameters, so active equals total.

Frequently Asked Questions

Common questions about Mistral Large 4 (Medium)

Mistral Large 4 (Medium) was released on October 29, 2026.

Mistral Large 4 (Medium) was created by Mistral.

Mistral Large 4 (Medium) scores 23 on the Artificial Analysis Intelligence Index, placing it above average among other open weight models of similar size (median: 23).

Mistral Large 4 (Medium) costs $0.70 per 1M input tokens (somewhat higher than average, median: $0.43) and $2.10 per 1M output tokens (somewhat higher than average, median: $1.50), based on Mistral's API.

Mistral Large 4 (Medium) costs $0.70 per 1M input tokens and $2.10 per 1M output tokens (based on Mistral's API). For a blended rate (7:2:1 cache hit/input/output ratio), this is $0.40 per 1M tokens. Pricing may vary by provider. Compare provider pricing

When evaluated on the Intelligence Index, Mistral Large 4 (Medium) generated 73M output tokens, which is better than average compared to other open weight models of similar size (median: 100M).

Yes, Mistral Large 4 (Medium) is a reasoning model. It uses extended thinking or chain-of-thought reasoning to work through complex problems before providing an answer.

Mistral Large 4 (Medium) supports text and image input.

Mistral Large 4 (Medium) supports text output.

Yes, Mistral Large 4 (Medium) supports image input and can analyze, describe, and answer questions about images.

Yes, Mistral Large 4 (Medium) is multimodal. It can process text and image input and generate text output.

Mistral Large 4 (Medium) has a context window of 1.0M tokens. This determines how much text and conversation history the model can process in a single request.

Yes, Mistral Large 4 (Medium) is open weights. The model weights are publicly available and can be downloaded for self-hosting.

Mistral Large 4 (Medium) has 1.1 trillion parameters (52 billion active).

Mistral Large 4 (Medium) is a Mixture of Experts (MoE) model with 1.1 trillion total parameters, but only 52 billion active parameters are used during inference.

Mistral Large 4 (Medium) is released under the Mistral Ecosystem Open-Weight License license. Commercial use is allowed with restrictions. View license

Mistral Large 4 (Medium) achieves a score of 23 on the Artificial Analysis Intelligence Index. This composite benchmark evaluates models across knowledge work, coding, science, and general knowledge.

Yes, Mistral Large 4 (Medium) is available via API through 1 provider. Compare API providers

Mistral Large 4 (Medium) is available through 1 API provider. Compare providers

Mistral Large 4 (Medium) Intelligence, Performance & Price Analysis

Model summary

IntelligenceUpdated

Speed

Comparison Summary

Technical specifications

126 models in this class

Speed

IntelligenceUpdated

Artificial Analysis Intelligence Index

Artificial Analysis Intelligence Index by Open Weights / Proprietary

Artificial Analysis Intelligence Index

Open Weights

Intelligence Index vs. Cost per Task

Cost per Intelligence Index Task

Artificial Analysis Intelligence Index

Evaluations

Intelligence Evaluation Relevance

Artificial Analysis Intelligence Index

AA-Briefcase v1.1

AA-Briefcase Elo

AA-Briefcase Elo

AA-Omniscience v1.1

AA-Omniscience Index

AA-Omniscience Index

Openness Index

Artificial Analysis Openness Index: Score

Intelligence Index Comparisons

Intelligence Index vs. Cost per Task

Cost per Intelligence Index Task

Artificial Analysis Intelligence Index

Token Use

Output Tokens per Intelligence Index Task

Output Tokens per Intelligence Index Task

Cost

Cost per Intelligence Index Task

Cost per Intelligence Index Task

Cost to Run Artificial Analysis Intelligence Index

Cost to Run Artificial Analysis Intelligence Index

Pricing: Cache Hit, Input, and Output

Cache Hit

Context Window

Context Window

Context Window for RAG

Context Window

Model Size (Open Weights Models Only)

Model Size: Total and Active Parameters

Total Parameters

Active Parameters at Inference Time

Frequently Asked Questions

When was Mistral Large 4 (Medium) released?

Who created Mistral Large 4 (Medium)?

How intelligent is Mistral Large 4 (Medium)?

How much does Mistral Large 4 (Medium) cost?

What is Mistral Large 4 (Medium) API pricing?

How verbose is Mistral Large 4 (Medium)?

Is Mistral Large 4 (Medium) a reasoning model?

What input modalities does Mistral Large 4 (Medium) support?

What output modalities does Mistral Large 4 (Medium) support?

Can Mistral Large 4 (Medium) process images?

Is Mistral Large 4 (Medium) multimodal?

What is the context window of Mistral Large 4 (Medium)?

Is Mistral Large 4 (Medium) open source?

How many parameters does Mistral Large 4 (Medium) have?

What are the active parameters of Mistral Large 4 (Medium)?

What is the license for Mistral Large 4 (Medium)?

How does Mistral Large 4 (Medium) perform on benchmarks?

Is Mistral Large 4 (Medium) available via API?

Where can I use Mistral Large 4 (Medium)?