Mistral Small 4 (Reasoning) logo
Mistral
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Open weights model

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Released March 2026

Mistral Small 4 (Reasoning) Intelligence, Performance & Price Analysis

API Provider Benchmarks

Model summary

Intelligence

28
Artificial Analysis Intelligence Index
4 out of 4 units for Intelligence.

Speed

156.8
Output tokens per second
4 out of 4 units for Speed.

Input Price

$0.15

USD per 1M tokens

2 out of 4 units for Input Price.

Output Price

$0.60
USD per 1M tokens
3 out of 4 units for Output Price.

Verbosity

53M
Output tokens from Intelligence Index
3 out of 4 units for Verbosity.

Mistral Small 4 (Reasoning) is amongst the leading models in intelligence and reasonably priced when comparing to other open weight models of similar size. It's also notably fast, however somewhat verbose. The model supports text and image input, outputs text, and has a 256k tokens context window.

Mistral Small 4 (Reasoning) scores 28 on the Artificial Analysis Intelligence Index, placing it well above average among comparable models (averaging 15). When evaluating the Intelligence Index, it generated 53M tokens, which is somewhat verbose in comparison to the average of 16M.

Pricing for Mistral Small 4 (Reasoning) is $0.15 per 1M input tokens (moderately priced, average: $0.15) and $0.60 per 1M output tokens (somewhat expensive, average: $0.57). In total, it cost $47.88 to evaluate Mistral Small 4 (Reasoning) on the Intelligence Index.

At 157 tokens per second, Mistral Small 4 (Reasoning) is notably fast (70).

ReasoningYes

This page shows the reasoning version of this model.

A non-reasoning variant may also exist.

Input modality

Supports: text, image

Output modality

Supports: text

Context window256k
~384 A4 pages of size 12 Arial font
Total parameters119B
Active parameters6.5B
Number of parameters active per token during inference
LicenseApache 2.0
Model weightsHugging 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

Highlights

Intelligence

Artificial Analysis Intelligence Index · Higher is better

Speed

Output tokens per second · Higher is better

Price

USD per 1M tokens (3:1 input-output ratio) · Lower is better

Intelligence

Artificial Analysis Intelligence Index

Artificial Analysis Intelligence Index v4.0 incorporates 10 evaluations: GDPval-AA, 𝜏²-Bench Telecom, Terminal-Bench Hard, SciCode, AA-LCR, AA-Omniscience, IFBench, Humanity's Last Exam, GPQA Diamond, CritPt
Reasoning models are indicated by a lightbulb icon

Artificial Analysis Intelligence Index v4.0 includes: GDPval-AA, 𝜏²-Bench Telecom, Terminal-Bench Hard, SciCode, AA-LCR, AA-Omniscience, IFBench, Humanity's Last Exam, GPQA Diamond, CritPt. 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

Artificial Analysis Intelligence Index v4.0 incorporates 10 evaluations: GDPval-AA, 𝜏²-Bench Telecom, Terminal-Bench Hard, SciCode, AA-LCR, AA-Omniscience, IFBench, Humanity's Last Exam, GPQA Diamond, CritPt
Reasoning models are indicated by a lightbulb icon

Artificial Analysis Intelligence Index v4.0 includes: GDPval-AA, 𝜏²-Bench Telecom, Terminal-Bench Hard, SciCode, AA-LCR, AA-Omniscience, IFBench, Humanity's Last Exam, GPQA Diamond, CritPt. See Intelligence Index methodology for further details, including a breakdown of each evaluation and how we run them.

Indicates whether the model weights are available. Models are labelled as 'Commercial Use Restricted' if the weights are available but commercial use is limited (typically requires obtaining a paid license).

Intelligence Evaluations

Intelligence evaluations measured independently by Artificial Analysis · Higher is better
Results claimed by AI Lab (not yet independently verified)
GDPval-AA

Agentic real-world work tasks, (ELO-500)/2000

Terminal-Bench Hard

Agentic coding & terminal use

𝜏²-Bench Telecom

Agentic tool use

AA-LCR

Long context reasoning

AA-Omniscience Accuracy

Knowledge

AA-Omniscience Non-Hallucination Rate

1 - hallucination rate

Humanity's Last Exam

Reasoning & knowledge

GPQA Diamond

Scientific reasoning

SciCode

Coding

IFBench

Instruction following

CritPt

Physics reasoning

APEX-Agents-AA

Long-horizon agentic tasks

MMMU-Pro

Visual reasoning

Reasoning models are indicated by a lightbulb icon.

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

Artificial Analysis Intelligence Index v4.0 includes: GDPval-AA, 𝜏²-Bench Telecom, Terminal-Bench Hard, SciCode, AA-LCR, AA-Omniscience, IFBench, Humanity's Last Exam, GPQA Diamond, CritPt. See Intelligence Index methodology for further details, including a breakdown of each evaluation and how we run them.

Openness

Artificial Analysis Openness Index: Results

Openness Index assesses model openness on a 0 to 100 normalized scale (higher is more open)
Reasoning models are indicated by a lightbulb icon

Intelligence Index Comparisons

Intelligence vs. Price

Blended at 7:2:1 (Cache Hit : Input : Output) · Price: USD per 1M tokens
Most attractive quadrant
Alibaba
Anthropic
DeepSeek
Google
InclusionAI
Kimi
Mistral
NVIDIA
OpenAI
xAI
Xiaomi
Z AI
Reasoning models are indicated by a lightbulb icon.

While higher intelligence models are typically more expensive, they do not all follow the same price-quality curve.

Artificial Analysis Intelligence Index v4.0 includes: GDPval-AA, 𝜏²-Bench Telecom, Terminal-Bench Hard, SciCode, AA-LCR, AA-Omniscience, IFBench, Humanity's Last Exam, GPQA Diamond, CritPt. See Intelligence Index methodology for further details, including a breakdown of each evaluation and how we run them.

Price per token, represented as USD per million Tokens. Price is a blend of cache hit, input, and output token prices using the selected ratio.

The blended bar shown here uses cache hit price only. Other caching costs differ by provider:

  • Anthropic: charges a separate cache write fee, with different rates for 5-minute and 1-hour TTLs (1-hour TTL is more expensive). Blended price charts use Anthropic cache write price for the input leg.
  • Google (Vertex/Gemini): charges a per-hour cache storage fee in addition to cache hit pricing. Some providers also use tiered pricing for prompts above 200K tokens.
  • OpenAI, DeepSeek, others: typically charge only cache hit pricing with no write or storage fee.

See Prompt Caching for the full breakdown.

Figures represent performance of the model's first-party API (e.g. OpenAI for o1) or the median across providers where a first-party API is not available (e.g. Meta's Llama models).

Intelligence Index Token Use & Cost

Output Tokens Used to Run Artificial Analysis Intelligence Index

Tokens used to run all evaluations in the Artificial Analysis Intelligence Index
Reasoning models are indicated by a lightbulb icon

The number of tokens required to run all evaluations in the Artificial Analysis Intelligence Index (excluding repeats).

Cost to Run Artificial Analysis Intelligence Index

Cost (USD) to run all evaluations in the Artificial Analysis Intelligence Index
Reasoning models are indicated by a lightbulb icon

The cost to run the evaluations in the Artificial Analysis Intelligence Index, calculated using the model's input and output token pricing and the number of tokens used across evaluations (excluding repeats).

Context Window

Context Window

Context window: tokens limit · Higher is better
Reasoning models are indicated by a lightbulb icon

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).

Pricing

Pricing now includes a “Cache Hit Price” alongside Input and Output pricing, with new blend ratios.

Pricing: Cache Hit, Input, and Output

Price: USD per 1M tokens
Reasoning models are indicated by a lightbulb icon

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.

Price per token included in the request/message sent to the API, represented as USD per million Tokens.

The blended bar shown here uses cache hit price only. Other caching costs differ by provider:

  • Anthropic: charges a separate cache write fee, with different rates for 5-minute and 1-hour TTLs (1-hour TTL is more expensive). Blended price charts use Anthropic cache write price for the input leg.
  • Google (Vertex/Gemini): charges a per-hour cache storage fee in addition to cache hit pricing. Some providers also use tiered pricing for prompts above 200K tokens.
  • OpenAI, DeepSeek, others: typically charge only cache hit pricing with no write or storage fee.

See Prompt Caching for the full breakdown.

Price per token generated by the model (received from the API), represented as USD per million Tokens.

Figures represent performance of the model's first-party API (e.g. OpenAI for o1) or the median across providers where a first-party API is not available (e.g. Meta's Llama models).

Speed

Measured by Output Speed (tokens per second)

Output Speed

Output tokens per second · Higher is better
Reasoning models are indicated by a lightbulb icon

Tokens per second received while the model is generating tokens (ie. after first chunk has been received from the API for models which support streaming).

Figures represent performance of the model's first-party API (e.g. OpenAI for o1) or the median across providers where a first-party API is not available (e.g. Meta's Llama models).

Output Speed vs. Price

Output speed: output tokens per second · Price: USD per 1M tokens
Most attractive quadrant
Alibaba
Anthropic
DeepSeek
Google
Kimi
Mistral
NVIDIA
OpenAI
xAI
Xiaomi
Z AI
Reasoning models are indicated by a lightbulb icon.

Tokens per second received while the model is generating tokens (ie. after first chunk has been received from the API for models which support streaming).

Price per token, represented as USD per million Tokens. Price is a blend of cache hit, input, and output token prices using the selected ratio.

Latency

Measured by Time (seconds) to First Token

Latency: Time To First Answer Token

Seconds to first answer token received · Accounts for reasoning model 'thinking' time
Reasoning models are indicated by a lightbulb icon

Time to first answer token received, in seconds, after API request sent. For reasoning models, this includes the 'thinking' time of the model before providing an answer. For models which do not support streaming, this represents time to receive the completion.

End-to-End Response Time

Seconds to output 500 tokens, calculated based on time to first token, 'thinking' time for reasoning models, and output speed

End-to-End Response Time

Seconds to output 500 tokens, including reasoning model 'thinking' time · Lower is better
Reasoning models are indicated by a lightbulb icon

Seconds to receive a 500 token response. Key components:

  • Input time: Time to receive the first response token
  • Thinking time (only for reasoning models): Time reasoning models spend outputting tokens to reason prior to providing an answer. Amount of tokens based on the average reasoning tokens across a diverse set of 60 prompts (methodology details).
  • Answer time: Time to generate 500 output tokens, based on output speed

Figures represent performance of the model's first-party API (e.g. OpenAI for o1) or the median across providers where a first-party API is not available (e.g. Meta's Llama models).

Model Size (Open Weights Models Only)

Model Size: Total and Active Parameters

Comparison between total model parameters and parameters active during inference
Reasoning models are indicated by a lightbulb icon

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 Small 4 (Reasoning)

Mistral Small 4 (Reasoning) was released on March 16, 2026.

Mistral Small 4 (Reasoning) was created by Mistral.

Mistral Small 4 (Reasoning) scores 28 on the Artificial Analysis Intelligence Index, placing it well above average among other open weight models of similar size (median: 15).

Mistral Small 4 (Reasoning) generates output at 156.8 tokens per second (based on Mistral's API), which is well above average compared to other open weight models of similar size (median: 69.8 t/s).

Mistral Small 4 (Reasoning) has a time to first token (TTFT) of 0.76s (based on Mistral's API), which is very competitive compared to other open weight models of similar size (median: 1.83s).

Mistral Small 4 (Reasoning) costs $0.15 per 1M input tokens (very competitive, median: $0.36) and $0.60 per 1M output tokens (better than average, median: $0.90), based on Mistral's API.

Mistral Small 4 (Reasoning) costs $0.15 per 1M input tokens and $0.60 per 1M output tokens (based on Mistral's API). For a blended rate (3:1 input to output ratio), this is $0.26 per 1M tokens. Pricing may vary by provider. Compare provider pricing

When evaluated on the Intelligence Index, Mistral Small 4 (Reasoning) generated 53M output tokens, which is somewhat higher than average compared to other open weight models of similar size (median: 16M).

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

Mistral Small 4 (Reasoning) supports text and image input.

Mistral Small 4 (Reasoning) supports text output.

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

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

Mistral Small 4 (Reasoning) has a context window of 260k tokens. This determines how much text and conversation history the model can process in a single request.

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

Mistral Small 4 (Reasoning) has 119 billion parameters (6.5 billion active).

Mistral Small 4 (Reasoning) is a Mixture of Experts (MoE) model with 119 billion total parameters, but only 6.5 billion active parameters are used during inference.

Mistral Small 4 (Reasoning) is released under the Apache 2.0 license. This license allows commercial use. View license

Mistral Small 4 (Reasoning) achieves a score of 28 on the Artificial Analysis Intelligence Index. This composite benchmark evaluates models across reasoning, knowledge, mathematics, and coding.

Mistral Small 4 (Reasoning) is an open weights model that can be self-hosted. View providers

Mistral Small 4 (Reasoning) is an open weights model that can be downloaded and self-hosted. Compare providers