Agentic
Not comparable- o1
- Not ranked
- o1-pro
- Not ranked
- Basis
- BenchAlign lane · 0 vs 0 public rows
- Reading
- Not comparable
o1 vs o1-pro
Updated September 4, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload. This is a same-family comparison, so migration details appear when the source data supports them.
Decision reading
o1 has the higher public score estimate, 47.89 versus 45.5, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.
1 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
Share or export
Which one for your work
Recommendations appear only when a shared evidence basis or an explicit operating constraint supports the call. Secondary and unsupported use cases stay disclosed below the initial list.
Chat turn cost
1K fresh input + 500 output tokens
o1
o1 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
Show secondary and unsupported calls
Repository review cost
50K fresh input + 3K output tokens
o1
o1 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
Coding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
O1-pro is not ranked on the public lane for coding, so no winner is named for coding.
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
O1 and o1-pro are not ranked on the public lane for agentic, so no winner is named for agentic.
Confidence: limited
Long documents
Prompts that approach the documented context limit
No clear pick
The documented context windows are equal.
Confidence: documented
Cache-heavy agent loop cost
200K cached + 20K fresh input + 10K output tokens
Not enough matched evidence
The page does not recommend a cost winner because at least one model cannot fit the stated workload in one request. o1 does not fit this workload in one request. o1-pro does not fit this workload in one request. o1 has no published cached-input rate, so cached tokens use its listed input rate. o1-pro has no published cached-input rate, so cached tokens use its listed input rate.
Confidence: rate-fallback
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
3 o1 only
1 Shared
- Shared results
- 1
- o1 only
- 3
- o1-pro only
- 0
- Like-for-like categories
- 0 / 8
Category results, on a stated basis
Each row shows the public-lane category score for both models: the BenchAlign lane for agentic, coding, and knowledge, and the weighted public rows elsewhere, with the same rank each model page reports. A row is like-for-like only when both scores rest on Supported evidence or the same weighted set. Directional and not-comparable rows remain visible, but they never receive a winner.
Coding
Not comparable- o1
- 46.1
- Estimated · #101/183
- o1-pro
- Not ranked
- Basis
- BenchAlign lane · 0 vs 0 public rows
- Reading
- Not comparable
Reasoning
Not comparable- o1
- 66.5
- Unranked · 2 rankable rows
- o1-pro
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Knowledge
Not comparable- o1
- 46.6
- Supported · #106/181
- o1-pro
- Not ranked
- Basis
- BenchAlign lane · 2 vs 1 public rows
- Reading
- Not comparable
Math
Not comparable- o1
- 32.6
- Unranked · 1 rankable row
- o1-pro
- Not ranked
- Basis
- Provisional lane · 1 vs 0 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- o1
- Not ranked
- o1-pro
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- o1
- Not ranked
- o1-pro
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- o1
- 85.7
- #39/120
- o1-pro
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
| Category | o1 | o1-pro | Basis | Reading |
|---|---|---|---|---|
| Agentic | Not ranked | Not ranked | Not comparableBenchAlign lane · 0 vs 0 public rows | Not comparable |
| Coding | 46.1Estimated · #101/183 | Not ranked | Not comparableBenchAlign lane · 0 vs 0 public rows | Not comparable |
| Reasoning | 66.5Unranked · 2 rankable rows | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Knowledge | 46.6Supported · #106/181 | Not ranked | Not comparableBenchAlign lane · 2 vs 1 public rows | Not comparable |
| Math | 32.6Unranked · 1 rankable row | Not ranked | Not comparableProvisional lane · 1 vs 0 weighted rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multimodal | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Instruction following | 85.7#39/120 | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign) differ from the provisional-lane categories. Unranked scores sit on the lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.
Shape of the matched evidence
Only shared public evidence is shown. Sparse evidence stays a ruled list rather than being closed into a radar shape.
Too few matched category axes support a radar. The ruled list below shows only shared benchmark results; positions use each benchmark’s normalized display scale when available.
GPQA
Knowledge
What each workload costs
Three fixed token mixes turn per-token rates into comparable decisions. Each scenario states context fit and whether cached input had to fall back to the published list-input rate.
Chat turn
1K fresh input + 500 output tokens
- o1
- $0.045
- Fits in one request
- o1-pro
- $0.45
- Fits in one request
o1 has the lower modeled cost
Costs use the listed standard API rates.
Repository review
50K fresh input + 3K output tokens
- o1
- $0.93
- Fits in one request
- o1-pro
- $9.30
- Fits in one request
o1 has the lower modeled cost
Costs use the listed standard API rates.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- o1
- $3.90
- Does not fit in one request
- Cached input priced at the published list-input rate
- o1-pro
- $39.00
- Does not fit in one request
- Cached input priced at the published list-input rate
o1 does not fit this workload in one request. o1-pro does not fit this workload in one request. o1 has no published cached-input rate, so cached tokens use its listed input rate. o1-pro has no published cached-input rate, so cached tokens use its listed input rate.
Specification differences
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
Specificationo1o1-pro
Context window
Maximum documented context; output-token limits may be lower.
o1
200K
o1-pro
200K
API model ID
o1
Not sourced
o1-pro
Not sourced
Cached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
o1
Not published
o1-pro
Not published
Documented inputs
o1
Not sourced
o1-pro
Not sourced
Documented outputs
o1
Not sourced
o1-pro
Not sourced
Provider availability
o1
Not sourced
o1-pro
Not sourced
Reasoning profile
o1
Reasoning
o1-pro
Reasoning
Weight access
o1
Proprietary
o1-pro
Proprietary
License
o1
Proprietary
o1-pro
Proprietary
Release date
o1
2024-12-01
o1-pro
2024-12-01
If you are choosing between sibling variants
- Deployment change
- Both entries list OpenAI as the provider. Confirm endpoint, model ID, limits, and feature support before switching.
- Quality signal
- o1 has the higher public score estimate, 47.89 versus 45.5, but the 90% score intervals overlap.
- Workload cost
- Repository review: $0.93 vs $9.30. Cache-heavy agent loop: $3.90 vs $39.00.
- Context tradeoff
- Both models list 200K.
Run the same representative tasks against both endpoints before changing production traffic.
Benchmark evidence
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Browse raw public benchmark evidence4 rows
Frequently asked questions
Which is better, o1 or o1-pro?
o1 has the higher public score estimate, 47.89 versus 45.5, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.
Which is better for coding, o1 or o1-pro?
O1-pro is not ranked on the public lane for coding, so no winner is named for coding.
Which is better for agentic tasks, o1 or o1-pro?
O1 and o1-pro are not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.
Which costs less, o1 or o1-pro?
For the stated presets, chat costs $0.045 on o1 and $0.45 on o1-pro; repository review costs $0.93 and $9.30; the cache-heavy agent loop costs $3.90 and $39.00. o1 does not fit this workload in one request. o1-pro does not fit this workload in one request. o1 has no published cached-input rate, so cached tokens use its listed input rate. o1-pro has no published cached-input rate, so cached tokens use its listed input rate.
Which has the larger context window, o1 or o1-pro?
Both models list the same context window, 200K.
Related comparisons
Last updated September 4, 2026
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