This model is deprecated. We still benchmark its performance, but less often: every 18 hours instead of every 6.

OpenAI has launched a newer release, GPT-5.2 Codex. We suggest considering it instead.

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OpenAI

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Proprietary model

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Released November 2025

GPT-5.1 Codex mini: Intelligence, Performance & Price Analysis

Name: Intelligence
Creator: Artificial Analysis
License: https://artificialanalysis.ai/docs/legal/Terms-of-Use.pdf

Try it out

Available providers

Microsoft Azure

IntelligenceUpdated

N/A

Artificial Analysis Intelligence Index

Performance

Time to first answer token 21.99sOutput speed 145 tok/s

N/A

Time per Intelligence Index task (minutes)

Cost

In $0.25Out $2.00Cache discount 90%

N/A

Cost per Intelligence Index task

Verbosity

N/A

Output tokens from Intelligence Index

GPT-5.1 Codex mini (High) supports text and image input, outputs text, and has a 400k tokens context window with knowledge up to September 2024.

Pricing for GPT-5.1 Codex mini (High) is $0.25 per 1M input tokens (somewhat expensive, median: $0.24) and $2.00 per 1M output tokens (expensive, median: $0.83).

Reasoning	Yes
Input modality	Supports: text and image
Output modality	Supports: text
Knowledge cutoff	Sep 30, 2024
Context window	400k ~600 A4 pages of size 12 Arial font

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

Time per Task

Inference time (minutes) per Intelligence Index task · Lower 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 v5.0.1 incorporates 9 evaluations: AA-Briefcase v1.1, AutomationBench-AA, Terminal-Bench 4.0, FrontierCode-AA 2.0, AA-Omniscience v1.1, GDP.pdf, HLE-Diamond, CritPt v2 (Hard Subset), and Terminal-Bench-Science 0.1

Artificial Analysis Intelligence Index v5.0.1 includes: AA-Briefcase v1.1, AutomationBench-AA, Terminal-Bench 4.0, FrontierCode-AA 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

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.

Benchmarks

Evaluations

Intelligence evaluations measured independently by Artificial Analysis · Higher is better

MMMU-Pro

Visual reasoning

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

Cost

Cost per Intelligence Index Task

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

Cost to Run Artificial Analysis Intelligence Index

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

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)

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.

Performance

Measured by time per task (minutes), output speed (tokens per second), time to first answer token and time to first token (seconds)

Time per Intelligence Index Task

Output tokens divided by end-to-end output speed (minutes per Intelligence Index task) · Speed at the model's own endpoint or the median across providers · Lower is better

The weighted average time (minutes) per Artificial Analysis Intelligence Index task: output tokens per task divided by output speed, weighted by each benchmark's weight in the Intelligence Index.

Speed and latency metrics average each endpoint's runs over the past 5 days, and the error rate pools them. We run the benchmark every 6 hours, and endpoints of deprecated models every 18 hours.

Output Speed

Output tokens divided by total request time, including time to first token · Higher is better

Output tokens, including reasoning tokens, received per second over a request's full duration, from sending the request to receiving the final token. The headline value averages each Performance v3 workload over the past 5 days, then averages the workloads.

Time to First Answer Token

Seconds to first answer token received · Accounts for reasoning model 'thinking' time

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.

Context Window

Context window: tokens limit · Higher is better

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).

Further details

								Weights
GPT-5.1 Codex mini (High)	OpenAI	-	-	400k	$0.27	-	145	Not available

Frequently Asked Questions

Common questions about GPT-5.1 Codex mini

GPT-5.1 Codex mini was released on November 13, 2025.

GPT-5.1 Codex mini was created by OpenAI.

GPT-5.1 Codex mini has a time to first answer token of 21.99s (based on the median across providers serving the model), which is somewhat higher than average compared to other models in a similar price tier (median: 8.81s).

GPT-5.1 Codex mini costs $0.25 per 1M input tokens (somewhat higher than average, median: $0.24) and $2.00 per 1M output tokens (at the higher end, median: $0.83), based on the median across providers serving the model.

GPT-5.1 Codex mini costs $0.25 per 1M input tokens and $2.00 per 1M output tokens (based on the median across providers serving the model). For a blended rate (7:2:1 cache hit/input/output ratio), this is $0.27 per 1M tokens. Pricing may vary by provider. Compare provider pricing

Yes, GPT-5.1 Codex mini is a reasoning model. It uses extended thinking or chain-of-thought reasoning to work through complex problems before providing an answer.

GPT-5.1 Codex mini supports text and image input.

GPT-5.1 Codex mini supports text output.

Yes, GPT-5.1 Codex mini supports image input and can analyze, describe, and answer questions about images.

Yes, GPT-5.1 Codex mini is multimodal. It can process text and image input and generate text output.

GPT-5.1 Codex mini has a context window of 400k tokens. This determines how much text and conversation history the model can process in a single request.

No, GPT-5.1 Codex mini is proprietary. The model weights are not publicly available.

GPT-5.1 Codex mini is a proprietary model and OpenAI has not disclosed the model size or parameter count.

GPT-5.1 Codex mini has a knowledge cutoff of September 2024. The model's training data includes information up to this date.

Yes, GPT-5.1 Codex mini is available via API through 1 provider. Compare API providers

GPT-5.1 Codex mini is available through 1 API provider. Compare providers

GPT-5.1 Codex mini: Intelligence, Performance & Price Analysis

Model summary

IntelligenceUpdated

Performance

Cost

Verbosity

Technical specifications

164 models in this class

Intelligence

Time per Task

Cost per Task

IntelligenceUpdated

Artificial Analysis Intelligence Index

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

Benchmarks

Evaluations

Intelligence Evaluation Relevance

Artificial Analysis Intelligence Index

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

Performance

Time per Intelligence Index Task

Time per Intelligence Index Task

Time Period

Output Speed

End-to-End Output Speed

Time to First Answer Token

Time to First Answer Token

Context Window

Context Window

Context Window for RAG

Context Window

Further details

More from OpenAI

Frequently Asked Questions

When was GPT-5.1 Codex mini released?

Who created GPT-5.1 Codex mini?

What is the latency of GPT-5.1 Codex mini?

How much does GPT-5.1 Codex mini cost?

What is GPT-5.1 Codex mini API pricing?

Is GPT-5.1 Codex mini a reasoning model?

What input modalities does GPT-5.1 Codex mini support?

What output modalities does GPT-5.1 Codex mini support?

Can GPT-5.1 Codex mini process images?

Is GPT-5.1 Codex mini multimodal?

What is the context window of GPT-5.1 Codex mini?

Is GPT-5.1 Codex mini open source?

How many parameters does GPT-5.1 Codex mini have?

What is the knowledge cutoff date for GPT-5.1 Codex mini?

Is GPT-5.1 Codex mini available via API?

Where can I use GPT-5.1 Codex mini?