OpenAI o1 is the December 2024 model that started the reasoning line, and it remains competent at math and logic heavy debugging inside Atlas. What OpenAI o1 is not, in 2026, is a rational default. o1 gives you a 200K token context with a 100K max output at $15 per Mtok input and $60 per Mtok output, and o3 delivers the same window for $2 per Mtok input and $8 per Mtok output, which is 7.5x cheaper on both sides and newer. The remaining reason to run o1 in Atlas is as a fixed historical baseline when you are measuring reasoning progress against o3 and the GPT-5 line.
What is OpenAI o1 and why does it still matter?
OpenAI o1, released December 2024, is the original chain-of-thought reasoning model and the start of the entire o-series. OpenAI o1 gives Atlas a 200K token context with a 100K max output, and it remains competent at math and logic heavy debugging even though o3 has passed it.
OpenAI o1 is the model that made explicit reasoning a product category rather than a research demo. Inside Atlas, o1 still does what it was built for: it handles math and logic heavy debugging with real rigor, and its 100K max output tokens, matching the whole o-series, give it room to write a full derivation rather than a summary. The reason to know o1 in 2026 is mostly historical and comparative. When you want to say how much reasoning has improved, o1 is the fixed point you measure from, and Atlas makes running that comparison a single /models switch away.
How much does OpenAI o1 cost compared to o3?
OpenAI o1 costs $15 per Mtok input and $60 per Mtok output, which is 7.5x the price of o3 at $2 input and $8 output. Both models carry an identical 200K token context and a 100K max output, and o3 is the newer and stronger of the two.
The pricing gap on OpenAI o1 is the single most important fact on this page. You are paying 7.5x more per token on both sides for a model that is older and weaker than its own successor, on the same 200K token window. In an Atlas session that difference compounds fast, because the agent loop is read heavy: every file read, every tool result, and every unified diff Atlas surfaces flows back through input tokens at $15 per Mtok. Unless you are deliberately reproducing an o1 result, the honest recommendation is to pin openai/o3 instead and keep the $60 per Mtok output charge off your bill.
Is OpenAI o1 good at tool calling in an agent loop?
OpenAI o1 has weak tool calling compared to GPT-5 class models, and that matters a lot in an agent loop like Atlas, where a session is a long chain of reads, searches, edits, and shell commands. Expect fewer, chunkier tool calls from o1 rather than a fine grained sequence.
Tool calling is where OpenAI o1 shows its age most clearly. Atlas is a loop of tool calls, so a model that fires fewer, chunkier ones tends to guess at file contents instead of reading them. The mitigation is structural. Lean on Atlas's plan agent to pre-select files, so o1 is reasoning over a curated set rather than trying to discover it. Keep permissions on ask so an older model's shell commands are reviewed before they run, since every Atlas tool call is permission-gated against allow, ask, and deny rules. Atlas computes a unified diff for every file edit and surfaces it for approval before writing, which catches an o1 edit that was written from memory rather than from the file.
Does OpenAI o1's 200K context hold enough of a codebase?
OpenAI o1 offers a 200K token context with a 100K max output, matching the whole o-series. For a scoped Atlas session over one feature or package, 200K tokens is workable, but half that budget can vanish into a single long o1 derivation.
The 200K token window on OpenAI o1 was generous in December 2024 and is ordinary in 2026. What makes it usable in Atlas is retrieval quality: Atlas searches code with hybrid semantic and keyword retrieval fused by reciprocal rank fusion, and Atlas indexes code by AST declarations using tree-sitter, not blind line windows, so o1 receives whole declarations rather than truncated line ranges. The 100K max output deserves attention too. On a 200K window, a model that can emit 100K tokens can consume half the budget in one response, and at $60 per Mtok output that response has a real price attached to it.
When should you pick a different model instead of OpenAI o1?
Pick OpenAI o3 instead of o1 in essentially every case: same 200K context, same 100K max output, 7.5x cheaper at $2 per Mtok input and $8 per Mtok output, and newer. Reserve o1 for historical comparison, where the model identity is the point of the run.
OpenAI o1 is dominated on both price and capability by o3, and pretending otherwise would not help anyone making a real budget decision. The narrow case that survives is benchmarking: o1 as a fixed historical baseline when comparing reasoning progress against o3 and the GPT-5 line. If tool calling discipline is what you need, a GPT-5 class model is stronger than o1. If cost is what you need, o3 is 7.5x cheaper. Atlas lets you switch the active model and provider on the fly with favorites and recents, so keeping o1 available for a one-off comparison costs nothing while your default stays on something modern.
Setup
- 01Set OPENAI_API_KEY in your shell or store it in Atlas's credential store.
- 02Run `atlas models openai` and confirm o1 resolves in the model list.
- 03Pin "model": "openai/o1" only for historical comparison, since o3 is 7.5x cheaper and newer.
- 04Expect fewer, chunkier tool calls from o1, so lean on Atlas's plan agent to pre-select files.
- 05Keep permissions on ask so an older model's shell commands are reviewed before they run.
Frequently asked questions
- How much does OpenAI o1 cost per million tokens?
- OpenAI o1 costs $15 per Mtok input and $60 per Mtok output. That is 7.5x the price of o3, which charges $2 input and $8 output on the same 200K context.
- Should I use o1 or o3 for coding in 2026?
- Use o3. OpenAI o3 is newer, stronger, and 7.5x cheaper than o1 on both input and output, with the identical 200K token context and 100K max output.
- What is the context window of OpenAI o1?
- OpenAI o1 has a 200K token context window with a 100K max output, which matches the rest of the o-series.
- Is OpenAI o1 good at tool calling?
- No. OpenAI o1 has weak tool calling compared to GPT-5 class models, and expect fewer, chunkier tool calls, which matters a lot in an agent loop like Atlas.
- How do I run Atlas on OpenAI o1?
- Set OPENAI_API_KEY, run `atlas models openai` to confirm o1 resolves, then set "model": "openai/o1" in atlas.json. Pin it only for historical comparison.
- Why is OpenAI o1 still in the model registry?
- OpenAI o1 remains useful as a fixed historical baseline when comparing reasoning progress against o3 and the GPT-5 line, and it is still competent at math and logic heavy debugging.
- How do I stop an older model like o1 from running risky shell commands in Atlas?
- Keep permissions on ask. Every Atlas tool call is permission-gated against allow, ask, and deny rules before it runs, so o1's shell commands are reviewed first.
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