o1 model profile

Evidence snapshot: 2026-09-06

o1 is a OpenAI model profile with a 200,000-token context window. The dated catalog snapshot records text, image, file modalities and interfaces for file input, prompt caching, reasoning controls, structured output, text generation.

The latest and strongest model family from OpenAI, o1 is designed to spend more time thinking before responding. The o1 model series is trained with large-scale reinforcement learning to reason...

o1 is a reference profile: OpenAI publishes it, Kendr documents it for comparison, and it carries no Kendr alias to call.

o1 is an independent knowledge profile. It does not claim that Kendr hosts the model, quote a Kendr customer price, promise API availability, or advertise a Kendr routing receipt.

o1 accepts text, image, and file and returns text. The snapshot records file input, prompt caching, reasoning controls, structured output, text generation, tool calling, vision, and web search pricing as capabilities or interfaces.

The dated reference snapshot lists $15 input and $60 output per million tokens. Cached input is $7.5 per million tokens.

No comparable popularity rank or traffic share is present in the 2026-09-06 snapshot. No comparable routing rank or traffic-share observation is attached to this dated profile; missing adoption evidence is not estimated.

Profile data was reviewed for the 2026-09-06 snapshot. Claims retain their source dates, and unavailable fields are shown as unavailable instead of estimated.

Profile facts

Profile type: Model research profile
Provider or publisher: OpenAI
Context window: 200,000 tokens
Snapshot date: 2026-09-06
Knowledge cutoff: 2023-10-31
Reference catalog ID: openai/o1

When to pick o1

o1 is a reference profile: OpenAI publishes it, Kendr documents it for comparison, and it carries no Kendr alias to call.

Each condition below is derived from this model's own values in the 2026-09-06 snapshot, compared only against models publishing the same field.

Reach for it when the input includes images. The snapshot records image input, so screenshots, scans, and diagrams can be sent directly instead of described.
Reach for it when the model has to call tools. Tool calling is recorded in the snapshot, so this route can drive an agent loop rather than only answer in prose.
Look elsewhere when you need to call it through Kendr today. This is a research reference profile with no Kendr alias. The facts and evidence here are published for comparison; the model is not routable on Kendr.
Look elsewhere when the work does not justify the rate. At $15 in and $60 out per 1M tokens, this is in the most expensive tenth of the 146 models publishing both rates. Route routine or high-volume traffic to a cheaper model and keep this one for work that needs it.
Look elsewhere when the prompt is long. The 200K-token context window is in the bottom quarter of the 163 models publishing a limit. Long documents need chunking or a wider-context route.

Context, modalities, and identity

Provider or publisher: OpenAI
Context window: 200,000 tokens
Maximum output: 100,000 tokens
Input modalities: text, image, and file
Output modalities: text
Knowledge cutoff: 2023-10-31

Model overview

The latest and strongest model family from OpenAI, o1 is designed to spend more time thinking before responding. The o1 model series is trained with large-scale reinforcement learning to reason...

Reference model ID: openai/o1
Canonical version: openai/o1-2024-12-17
Hugging Face ID: Not separately documented

Capabilities and supported controls

Capabilities and parameters are reported from the dated catalog and source records; their presence does not guarantee identical behavior across every provider route.

Supported parameters: include_reasoning, max_tokens, reasoning, response_format, seed, structured_outputs, tool_choice, tools

file input
prompt caching
reasoning controls
structured output
text generation
tool calling
vision
web search pricing

Dated pricing snapshot

The dated reference snapshot lists $15 input and $60 output per million tokens. Cached input is $7.5 per million tokens.

These are dated third-party reference prices, not Kendr prices or an availability offer. Routes, tiers, caching, region, and provider policy can change the landed price.

Price date: 2026-09-06
Input: $15 per 1M tokens
Cached input: $7.5 per 1M tokens
Output: $60 per 1M tokens

Provider routes, performance, and uptime

The snapshot retains 1 provider endpoint. Provider prices, context limits, p50 performance, and uptime can differ by route and are not Kendr guarantees.

Active provider endpoints: 1
Best p50 latency: No recent metric
Best p50 throughput: No recent metric
Availability with routing: No recent metric
Availability without routing: No recent metric
Performance date: 2026-09-06

Provider	Quantization	Input / 1M	Output / 1M	Cache read / 1M	Context	p50 latency	p50 throughput	Uptime (1d)
OpenAI	unknown	$15 / 1M	$60 / 1M	$7.5 / 1M	200,000 tokens	No recent p50	No recent p50	100.00%

Benchmark evidence and limitations

Third-party catalog fields normalized from the cited model dataset; benchmark methodology and coverage differ by source.

Coding Index: 39.7

Popularity and market context

Third-party catalog rank: No verified rank
Global market share: Not inferred
Observation date: 2026-09-06

Frequently asked questions

When should I use o1?

o1 is a reference profile: OpenAI publishes it, Kendr documents it for comparison, and it carries no Kendr alias to call. Reach for it when the input includes images: The snapshot records image input, so screenshots, scans, and diagrams can be sent directly instead of described. Reach for it when the model has to call tools: Tool calling is recorded in the snapshot, so this route can drive an agent loop rather than only answer in prose. Look elsewhere when you need to call it through Kendr today: This is a research reference profile with no Kendr alias. The facts and evidence here are published for comparison; the model is not routable on Kendr. Look elsewhere when the work does not justify the rate: At $15 in and $60 out per 1M tokens, this is in the most expensive tenth of the 146 models publishing both rates. Route routine or high-volume traffic to a cheaper model and keep this one for work that needs it. Look elsewhere when the prompt is long: The 200K-token context window is in the bottom quarter of the 163 models publishing a limit. Long documents need chunking or a wider-context route.

What is o1?

What context window does o1 have?

The dated profile lists 200,000 tokens of context and up to 100,000 output tokens. Provider routes, variants, and runtime configuration can impose lower effective limits.

What benchmark evidence is available for o1?

The 2026-09-06 snapshot includes 1 Artificial Analysis index. Publisher-reported launch results are labeled and are not presented as Kendr measurements; scores from different suites are not treated as interchangeable. Third-party catalog fields normalized from the cited model dataset; benchmark methodology and coverage differ by source.

Is o1 available through Kendr?

This is an independent knowledge profile, not a Kendr-hosted availability claim. Check Kendr's live public model API for currently enabled Kendr aliases.

Does the popularity rank represent o1's global market share?

Sources and evidence dates

o1 third-party catalog record (catalog, checked 2026-09-06)
Third-party model catalog methodology (methodology, checked 2026-09-06)
OpenAI official model documentation (primary, checked 2026-09-06)
Artificial Analysis capability indices methodology (benchmark, checked 2026-09-06)