Knowledge
Directional only- o3
- 50.0
- Estimated · #91/181
- o3-pro
- 50.2
- Estimated · #90/181
- Basis
- BenchAlign lane · 0 vs 0 public rows
- Reading
- Directional only
o3 vs o3-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
The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. Use the documented cost, context, and runtime rows instead.
0 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
o3
o3 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
o3
o3 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
O3-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
O3 and o3-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. o3 does not fit this workload in one request. o3-pro does not fit this workload in one request. o3 has no published cached-input rate, so cached tokens use its listed input rate. o3-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.
2 o3 only
- Shared results
- 0
- o3 only
- 2
- o3-pro only
- 0
- Like-for-like categories
- 0 / 8
1 category rests on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.
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.
Agentic
Not comparable- o3
- Not ranked
- o3-pro
- Not ranked
- Basis
- BenchAlign lane · 0 vs 0 public rows
- Reading
- Not comparable
Coding
Not comparable- o3
- 48.8
- Estimated · #83/183
- o3-pro
- Not ranked
- Basis
- BenchAlign lane · 0 vs 0 public rows
- Reading
- Not comparable
Reasoning
Not comparable- o3
- 73.7
- Unranked · 2 rankable rows
- o3-pro
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- o3
- 37.8
- Unranked · 2 rankable rows
- o3-pro
- Not ranked
- Basis
- Provisional lane · 2 vs 0 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- o3
- Not ranked
- o3-pro
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- o3
- 63.3
- Unranked · 1 rankable row
- o3-pro
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- o3
- 87.1
- #33/120
- o3-pro
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
| Category | o3 | o3-pro | Basis | Reading |
|---|---|---|---|---|
| Knowledge | 50.0Estimated · #91/181 | 50.2Estimated · #90/181 | Directional onlyBenchAlign lane · 0 vs 0 public rows | Directional only |
| Agentic | Not ranked | Not ranked | Not comparableBenchAlign lane · 0 vs 0 public rows | Not comparable |
| Coding | 48.8Estimated · #83/183 | Not ranked | Not comparableBenchAlign lane · 0 vs 0 public rows | Not comparable |
| Reasoning | 73.7Unranked · 2 rankable rows | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | 37.8Unranked · 2 rankable rows | Not ranked | Not comparableProvisional lane · 2 vs 0 weighted rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multimodal | 63.3Unranked · 1 rankable row | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Instruction following | 87.1#33/120 | 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.
A shared-evidence shape is not available.
BenchLM does not draw a radar or infer missing axes when the matched evidence is too sparse.
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
- o3
- $0.006
- Fits in one request
- o3-pro
- $0.06
- Fits in one request
o3 has the lower modeled cost
Costs use the listed standard API rates.
Repository review
50K fresh input + 3K output tokens
- o3
- $0.124
- Fits in one request
- o3-pro
- $1.24
- Fits in one request
o3 has the lower modeled cost
Costs use the listed standard API rates.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- o3
- $0.52
- Does not fit in one request
- Cached input priced at the published list-input rate
- o3-pro
- $5.20
- Does not fit in one request
- Cached input priced at the published list-input rate
o3 does not fit this workload in one request. o3-pro does not fit this workload in one request. o3 has no published cached-input rate, so cached tokens use its listed input rate. o3-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.
Specificationo3o3-pro
Context window
Maximum documented context; output-token limits may be lower.
o3
200K
o3-pro
200K
API model ID
o3
Not sourced
o3-pro
Not sourced
Cached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
o3
Not published
o3-pro
Not published
Documented inputs
o3
Not sourced
o3-pro
Not sourced
Documented outputs
o3
Not sourced
o3-pro
Not sourced
Provider availability
o3
Not sourced
o3-pro
Not sourced
Reasoning profile
o3
Reasoning
o3-pro
Reasoning
Weight access
o3
Proprietary
o3-pro
Proprietary
License
o3
Proprietary
o3-pro
Proprietary
Release date
o3
2025-04-16
o3-pro
2025-04-16
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
- The public evidence has no benchmark result shared by both models, so it does not support a quality verdict.
- Workload cost
- Repository review: $0.124 vs $1.24. Cache-heavy agent loop: $0.52 vs $5.20.
- 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.
Frequently asked questions
Which is better, o3 or o3-pro?
The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. The page therefore keeps the decision tied to the specific documented workload.
Which is better for coding, o3 or o3-pro?
O3-pro is not ranked on the public lane for coding, so no winner is named for coding.
Which is better for agentic tasks, o3 or o3-pro?
O3 and o3-pro are not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.
Which costs less, o3 or o3-pro?
For the stated presets, chat costs $0.006 on o3 and $0.06 on o3-pro; repository review costs $0.124 and $1.24; the cache-heavy agent loop costs $0.52 and $5.20. o3 does not fit this workload in one request. o3-pro does not fit this workload in one request. o3 has no published cached-input rate, so cached tokens use its listed input rate. o3-pro has no published cached-input rate, so cached tokens use its listed input rate.
Which has the larger context window, o3 or o3-pro?
Both models list the same context window, 200K.
Related comparisons
Last updated September 4, 2026
Watch o3 vs o3-pro
One weekly email when material rank, price, or benchmark evidence changes make this matchup worth revisiting.
Read a sample issueJoin 2,000+ readers.