ModellerSupportVirksomhedBlog
500+ AI Model API, Alt I Én API. Kun I CometAPI
Modeller API
Udvikler
Hurtig StartDokumentationAPI Dashboard
Ressourcer
AI-modellerBlogVirksomhedÆndringslogOm os
2025 CometAPI. Alle rettigheder forbeholdes.PrivatlivspolitikServicevilkår
Home/Models/OpenAI/o1-mini-all
O

o1-mini-all

Per anmodning:$0.08
Kommersiel brug
Oversigt
Funktioner
Priser
API

Technical Specifications of o1-mini-all

SpecificationDetails
Model IDo1-mini-all
Provider familyOpenAI o1-mini, exposed through CometAPI’s aggregated model catalog. (cometapi.com)
Model typeReasoning-focused large language model optimized for complex problem solving, especially STEM and coding tasks.
PositioningA faster, lower-cost variant of the original o1 reasoning line.
StrengthsMulti-step reasoning, mathematics, software engineering, structured problem decomposition, and analytical workflows.
Latency profileDesigned to be faster than larger reasoning models while preserving strong reasoning quality for technical use cases.
Cost profilePositioned as a more affordable reasoning option than larger flagship reasoning models.
Availability noteOpenAI’s public documentation describes o1-mini, while CometAPI lists o1-mini-all as its platform-specific model identifier. (cometapi.com)
Lifecycle noteOpenAI recommends newer small reasoning models such as o3-mini/o4-mini for some use cases, so developers should verify current platform availability before production rollout.

What is o1-mini-all

o1-mini-all is CometAPI’s platform identifier for access to the OpenAI o1-mini reasoning model family through a unified API layer. CometAPI’s model catalog explicitly lists o1-mini-all, while OpenAI’s own documentation describes the underlying family as o1-mini. (cometapi.com)

The underlying o1-mini model was introduced as a cost-efficient reasoning model built for hard problems that require multi-step thought, with especially strong performance in coding, mathematics, and other STEM-heavy tasks. OpenAI positions it as a faster and cheaper reasoning alternative to larger o1-class models.

In practice, o1-mini-all is best understood as a developer-facing endpoint for applications that need stronger reasoning than general-purpose chat models, but with better speed and cost efficiency than larger premium reasoning models. That makes it a practical option for code generation, technical assistants, logic-heavy workflows, math tutoring, and internal automation systems that benefit from deliberate reasoning behavior.

Main features of o1-mini-all

  • Reasoning-first design: Built for complex, multi-step problem solving rather than only fast conversational generation, making it suitable for analytical and technical tasks.
  • Strong STEM performance: OpenAI highlights its capabilities in mathematics, coding, and structured technical reasoning, where it approaches stronger flagship reasoning performance on some benchmarks.
  • Faster response profile: Compared with larger reasoning models, o1-mini is positioned for lower latency and quicker turnaround on hard prompts.
  • Lower-cost reasoning: The model is intended to provide more affordable access to reasoning workflows, which is useful for iterative development and higher-volume API workloads.
  • Useful for coding workflows: It is well suited for code explanation, debugging, algorithm design, test generation, and engineering Q&A.
  • Good fit for structured outputs: Because it is optimized for deliberate reasoning, it can be effective in stepwise business logic, classification with justification, and technical decision support when prompts are clearly structured. This is an inference based on the model’s documented reasoning orientation and developer use cases.
  • Aggregator-friendly access: CometAPI exposes this model through its own catalog, allowing teams to use a single integration pattern across multiple model providers. (cometapi.com)
  • Version-awareness recommended: Since OpenAI has documented newer successor mini reasoning models, teams should validate ongoing support, pricing, and migration plans when adopting o1-mini-all for long-term systems.

How to access and integrate o1-mini-all

Step 1: Sign Up for API Key

Sign up on CometAPI and generate your API key from the dashboard. After obtaining the key, store it securely and use it in the Authorization header for all requests.

Step 2: Send Requests to o1-mini-all API

Use CometAPI’s OpenAI-compatible API format and specify the model as o1-mini-all.

curl --request POST \
  --url https://api.cometapi.com/v1/chat/completions \
  --header "Authorization: Bearer YOUR_COMETAPI_KEY" \
  --header "Content-Type: application/json" \
  --data '{
    "model": "o1-mini-all",
    "messages": [
      {
        "role": "user",
        "content": "Solve this step by step: Write a Python function for binary search."
      }
    ]
  }'

Step 3: Retrieve and Verify Results

Parse the JSON response, read the generated content from the first completion choice, and validate the output against your application requirements. For production use, add retries, logging, schema checks, and prompt-based evaluation to verify quality, reasoning accuracy, and safety before downstream use.

Funktioner til o1-mini-all

Udforsk de vigtigste funktioner i o1-mini-all, designet til at forbedre ydeevne og brugervenlighed. Opdag hvordan disse muligheder kan gavne dine projekter og forbedre brugeroplevelsen.

Priser for o1-mini-all

Udforsk konkurrencedygtige priser for o1-mini-all, designet til at passe til forskellige budgetter og brugsbehov. Vores fleksible planer sikrer, at du kun betaler for det, du bruger, hvilket gør det nemt at skalere, efterhånden som dine krav vokser. Opdag hvordan o1-mini-all kan forbedre dine projekter, mens omkostningerne holdes håndterbare.
Comet-pris (USD / M Tokens)Officiel Pris (USD / M Tokens)Rabat
Per anmodning:$0.08
Per anmodning:$0.1
-20%

Eksempelkode og API til o1-mini-all

Få adgang til omfattende eksempelkode og API-ressourcer for o1-mini-all for at strømline din integrationsproces. Vores detaljerede dokumentation giver trin-for-trin vejledning, der hjælper dig med at udnytte det fulde potentiale af o1-mini-all i dine projekter.

Flere modeller

G

Nano Banana 2

Indtast:$0.4/M
Output:$2.4/M
Oversigt over kernefunktioner: Opløsning: Op til 4K (4096×4096), på niveau med Pro. Konsistens for referencebilleder: Op til 14 referencebilleder (10 objekter + 4 figurer), med bevaret stil-/figurkonsistens. Ekstreme aspektforhold: Nye 1:4, 4:1, 1:8, 8:1-forhold tilføjet, velegnet til lange billeder, plakater og bannere. Tekstrendering: Avanceret tekstgenerering, egnet til infografikker og layout til markedsføringsplakater. Søgeforbedring: Integreret Google-søgning + billedsøgning. Forankring: Indbygget tænkeproces; komplekse prompts ræsonneres før generering.
A

Claude Opus 4.6

Indtast:$4/M
Output:$20/M
Claude Opus 4.6 er Anthropic’s "Opus"-klasse store sprogmodel, lanceret i februar 2026. Den er positioneret som en arbejdshest til vidensarbejde og forskningsarbejdsgange — med forbedret langkontekstuel ræsonnering, flertrinsplanlægning, brug af værktøjer (herunder agent-baserede softwarearbejdsgange) og computeropgaver såsom automatiseret generering af slides og regneark.
A

Claude Sonnet 4.6

Indtast:$2.4/M
Output:$12/M
Claude Sonnet 4.6 er vores hidtil mest kapable Sonnet-model. Det er en fuld opgradering af modellens færdigheder på tværs af kodning, computerbrug, langkontekstlig ræsonnering, agentplanlægning, vidensarbejde og design. Sonnet 4.6 har også et kontekstvindue på 1M tokens i beta.
O

GPT-5.4 nano

Indtast:$0.16/M
Output:$1/M
GPT-5.4 nano er designet til opgaver, hvor hastighed og omkostninger er vigtigst, såsom klassificering, dataudtræk, rangering og subagenter.
O

GPT-5.4 mini

Indtast:$0.6/M
Output:$3.6/M
GPT-5.4 mini samler styrkerne fra GPT-5.4 i en hurtigere og mere effektiv model, der er designet til arbejdsbelastninger i stor skala.
A

Claude Mythos Preview

A

Claude Mythos Preview

Kommer snart
Indtast:$60/M
Output:$240/M
Claude Mythos Preview er vores hidtil mest kapable frontier-model og viser et markant spring i resultaterne på tværs af mange benchmark-tests sammenlignet med vores tidligere frontier-model, Claude Opus 4.6.