Intelligence
31 scoreOpenAI · Flagships Analysis
GPT-5.1 Codex mini (high)
Intelligence, Performance & Price Analysis
Canonical slug: gpt-5-1-codex-mini · Canonical model registry at build time
Speed
200.7 output tokens/secLatency
12.19s TTFTInput Price
$0.25 / 1M tokensOutput Price
$2.00 / 1M tokensExecutive Assessment
Routing Verdict & Tradeoffs
Ethen Routing Verdict
Frontier research / complex reasoning
GPT-5.1 Codex mini (high) scores 31 (estimated) on the Artificial Analysis Intelligence Index, placing it well above average among other reasoning models in a similar price tier (median: 15). GPT-5.1 Codex mini (high) generates output at 200.7 tokens per second (based on OpenAI's API), which is well above average compared to other reasoning models in a similar price tier (median: 99.0 t/s). GPT-5.1 Codex mini (high) costs $0.25 per 1M input tokens (better than average, median: $0.25) and $2.00 per 1M output tokens (at the higher end, median: $0.87), based on OpenAI's API.
Reserve this model for the hardest requests; simpler or repetitive work should stay on cheaper routes.
Task Fit Assessment
| Workload | Rating | Notes |
|---|---|---|
| Complex reasoning & agentic workflows | optimal | Intelligence score 31 places this model among top performers. Suitable for multi-step analysis and agentic loops. |
| High-volume chat & customer-facing | optimal | Output speed 200.7 tokens/sec and capable intelligence make this suitable for real-time chat at scale. |
| Latency-sensitive applications | viable | TTFT 12.19s — latency may be noticeable in interactive use. |
| Cost-sensitive pipelines | viable | Output pricing at $2.00 is premium. Route high-volume simple tasks to cheaper alternatives. |
Cost Pressure Analysis
Medium
Pricing is moderate — input $0.25, output $2.00. Costs accumulate at volume but are manageable for valuable tasks.
Technical Specifications
Architecture and Limits
| Specification | Value | Validation Authority |
|---|---|---|
| Model type | Proprietary | inferred |
| Reasoning | Yes | faq |
| Input modalities | GPT-5.1 Codex mini (high) supports text and image input. | faq |
| Output modalities | GPT-5.1 Codex mini (high) supports text output. | faq |
| Context window | 400k tokens | faq |
| Open weights / source | No, GPT-5.1 Codex mini (high) is proprietary. The model weights are not publicly available. | faq |
| Parameters | GPT-5.1 Codex mini (high) is a proprietary model and OpenAI has not disclosed the model size or parameter count. | faq |
| Knowledge cutoff | GPT-5.1 Codex mini (high) has a knowledge cutoff of September 2024. The model's training data includes information up to this date. | faq |
| API availability | Yes, GPT-5.1 Codex mini (high) is available via API through 2 providers. | faq |
Evidence charts
Profile visualizations
Charts are the same canonical evidence cards previously published for this slug, contained inside the D18D page grammar.
AA-Omniscience Index
AA-Omniscience 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. · Evaluation results measured independently by Artificial Analysis
Retrieved 2026-07-08
Chart source and provenance are listed in Methodology & sources below.
- Source:
- Published benchmark dataset
- Retrieval date:
- 2026-07-08
- Methodology:
- Independent test run by Artificial Analysis on dedicated hardware.
Bars intentionally show many models at once so the page feels like a benchmark report rather than a dashboard.
Artificial Analysis Intelligence Index by Open Weights / Proprietary
Artificial Analysis Intelligence Index v4.1 incorporates 9 evaluations: GDPval-AA v2, 𝜏³-Banking, Terminal-Bench v2.1, SciCode, Humanity's Last Exam, GPQA Diamond, CritPt, AA-Omniscience, AA-LCR · Evaluation results measured independently by Artificial Analysis
Retrieved 2026-07-08
Chart source and provenance are listed in Methodology & sources below.
- Source:
- Published benchmark dataset
- Retrieval date:
- 2026-07-08
- Methodology:
- Independent test run by Artificial Analysis on dedicated hardware.
Higher values indicate stronger overall capability. Bars show how models compare on composite intelligence metrics.
Artificial Analysis Intelligence Index
Artificial Analysis Intelligence Index v4.1 incorporates 9 evaluations: GDPval-AA v2, 𝜏³-Banking, Terminal-Bench v2.1, SciCode, Humanity's Last Exam, GPQA Diamond, CritPt, AA-Omniscience, AA-LCR · Evaluation results measured independently by Artificial Analysis
Retrieved 2026-07-08
Chart source and provenance are listed in Methodology & sources below.
- Source:
- Published benchmark dataset
- Retrieval date:
- 2026-07-08
- Methodology:
- Independent test run by Artificial Analysis on dedicated hardware.
Higher values indicate stronger overall capability. Bars show how models compare on composite intelligence metrics.
Intelligence
Artificial Analysis Intelligence Index · Higher is better · Evaluation results measured independently by Artificial Analysis
Retrieved 2026-07-08
Chart source and provenance are listed in Methodology & sources below.
- Source:
- Published benchmark dataset
- Retrieval date:
- 2026-07-08
- Methodology:
- Independent test run by Artificial Analysis on dedicated hardware.
Higher values indicate stronger overall capability. Bars show how models compare on composite intelligence metrics.
Output Speed
Output tokens per second · Higher is better · Evaluation results measured independently by Artificial Analysis
Retrieved 2026-07-08
Chart source and provenance are listed in Methodology & sources below.
- Source:
- Published benchmark dataset
- Retrieval date:
- 2026-07-08
- Methodology:
- Independent test run by Artificial Analysis on dedicated hardware.
Speed should be read together with latency and cost; fast output does not always mean better user-perceived performance.
Speed
Output tokens per second · Higher is better · Evaluation results measured independently by Artificial Analysis
Retrieved 2026-07-08
Chart source and provenance are listed in Methodology & sources below.
- Source:
- Published benchmark dataset
- Retrieval date:
- 2026-07-08
- Methodology:
- Independent test run by Artificial Analysis on dedicated hardware.
Speed should be read together with latency and cost; fast output does not always mean better user-perceived performance.
End-to-End Response Time
Seconds to output 500 tokens, including reasoning model 'thinking' time · Lower is better · Evaluation results measured independently by Artificial Analysis
Retrieved 2026-07-08
Chart source and provenance are listed in Methodology & sources below.
- Source:
- Published benchmark dataset
- Retrieval date:
- 2026-07-08
- Methodology:
- Independent test run by Artificial Analysis on dedicated hardware.
Bars intentionally show many models at once so the page feels like a benchmark report rather than a dashboard.
Latency: Time To First Answer Token
Seconds to first answer token received · Accounts for reasoning model 'thinking' time · Evaluation results measured independently by Artificial Analysis
Retrieved 2026-07-08
Chart source and provenance are listed in Methodology & sources below.
- Source:
- Published benchmark dataset
- Retrieval date:
- 2026-07-08
- Methodology:
- Independent test run by Artificial Analysis on dedicated hardware.
Latency includes thinking / reasoning time when applicable. Lower is better for interactive use cases.
Context Window
Context window: tokens limit · Higher is better · Evaluation results measured independently by Artificial Analysis
Retrieved 2026-07-08
Chart source and provenance are listed in Methodology & sources below.
- Source:
- Published benchmark dataset
- Retrieval date:
- 2026-07-08
- Methodology:
- Independent test run by Artificial Analysis on dedicated hardware.
Larger context windows enable complex multi-turn tasks but may increase latency and cost for long inputs.
Cost per Intelligence Index Task
Weighted average cost (USD) per Artificial Analysis Intelligence Index task, segmented by token type. Lower is better · Evaluation results measured independently by Artificial Analysis
Retrieved 2026-07-08
Chart source and provenance are listed in Methodology & sources below.
- Source:
- Published benchmark dataset
- Retrieval date:
- 2026-07-08
- Methodology:
- Independent test run by Artificial Analysis on dedicated hardware.
Higher values indicate stronger overall capability. Bars show how models compare on composite intelligence metrics.
Ethen Gateway should route away from this model when the task is simple, repetitive, or likely to generate many output tokens.
Cost per Task
Weighted average cost (USD) per Intelligence Index task · Lower is better · Evaluation results measured independently by Artificial Analysis
Retrieved 2026-07-08
Chart source and provenance are listed in Methodology & sources below.
- Source:
- Published benchmark dataset
- Retrieval date:
- 2026-07-08
- Methodology:
- Independent test run by Artificial Analysis on dedicated hardware.
Ethen Gateway should route away from this model when the task is simple, repetitive, or likely to generate many output tokens.
Cost to Run Artificial Analysis Intelligence Index
Cost (USD) to run all evaluations in the Artificial Analysis Intelligence Index · Evaluation results measured independently by Artificial Analysis
Retrieved 2026-07-08
Chart source and provenance are listed in Methodology & sources below.
- Source:
- Published benchmark dataset
- Retrieval date:
- 2026-07-08
- Methodology:
- Independent test run by Artificial Analysis on dedicated hardware.
Higher values indicate stronger overall capability. Bars show how models compare on composite intelligence metrics.
Ethen Gateway should route away from this model when the task is simple, repetitive, or likely to generate many output tokens.
Pricing: Cache Hit, Input, and Output
Price (USD per M Tokens) · Evaluation results measured independently by Artificial Analysis
Retrieved 2026-07-08
Chart source and provenance are listed in Methodology & sources below.
- Source:
- Published benchmark dataset
- Retrieval date:
- 2026-07-08
- Methodology:
- Independent test run by Artificial Analysis on dedicated hardware.
Bars intentionally show many models at once so the page feels like a benchmark report rather than a dashboard.
Time per Intelligence Index Task
Weighted average decode time (minutes) per task; excludes TTFT and overhead time · Lower is better · Evaluation results measured independently by Artificial Analysis
Retrieved 2026-07-08
Chart source and provenance are listed in Methodology & sources below.
- Source:
- Published benchmark dataset
- Retrieval date:
- 2026-07-08
- Methodology:
- Independent test run by Artificial Analysis on dedicated hardware.
Higher values indicate stronger overall capability. Bars show how models compare on composite intelligence metrics.
Methodology
Methodology & Provenance
This page is rendered from the normalized profile and page JSON for GPT-5.1 Codex mini (high).
Benchmark values are preserved as normalized; only layout, disclosure ordering, and typography are adjusted for readability.
Frequently Asked Questions
Model FAQs & Technical Disclosures
GPT-5.1 Codex mini (high) was released on November 13, 2025.
GPT-5.1 Codex mini (high) was created by OpenAI.
GPT-5.1 Codex mini (high) scores 31 (estimated) on the Artificial Analysis Intelligence Index, placing it well above average among other reasoning models in a similar price tier (median: 15).
GPT-5.1 Codex mini (high) generates output at 200.7 tokens per second (based on OpenAI's API), which is well above average compared to other reasoning models in a similar price tier (median: 99.0 t/s).
GPT-5.1 Codex mini (high) has a time to first token (TTFT) of 12.19s (based on OpenAI's API), which is at the higher end compared to other reasoning models in a similar price tier (median: 1.96s).
GPT-5.1 Codex mini (high) costs $0.25 per 1M input tokens (better than average, median: $0.25) and $2.00 per 1M output tokens (at the higher end, median: $0.87), based on OpenAI's API.
GPT-5.1 Codex mini (high) costs $0.25 per 1M input tokens and $2.00 per 1M output tokens (based on OpenAI's API). For a blended rate (7:2:1 cache hit/input/output ratio), this is $0.42 per 1M tokens. Pricing may vary by provider.
Yes, GPT-5.1 Codex mini (high) 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 (high) supports text and image input.
GPT-5.1 Codex mini (high) supports text output.
Yes, GPT-5.1 Codex mini (high) supports image input and can analyze, describe, and answer questions about images.
Yes, GPT-5.1 Codex mini (high) is multimodal. It can process text and image input and generate text output.
GPT-5.1 Codex mini (high) 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 (high) is proprietary. The model weights are not publicly available.
GPT-5.1 Codex mini (high) is a proprietary model and OpenAI has not disclosed the model size or parameter count.
GPT-5.1 Codex mini (high) achieves a score of 31 on the Artificial Analysis Intelligence Index. This composite benchmark evaluates models across reasoning, knowledge, mathematics, and coding.
GPT-5.1 Codex mini (high) has a knowledge cutoff of September 2024. The model's training data includes information up to this date.
Yes, GPT-5.1 Codex mini (high) is available via API through 2 providers.
GPT-5.1 Codex mini (high) is available through 2 API providers.