Agentic
Not comparable- GPT Audio 1.5
- Not ranked
- o1-preview
- Not ranked
- Basis
- BenchAlign lane · 0 vs 0 public rows
- Reading
- Not comparable
GPT Audio 1.5 vs o1-preview
Updated September 10, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
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.
Long documents
Prompts that approach the documented context limit
o1-preview
o1-preview has the larger documented context window.
Confidence: documented
Chat turn cost
1K fresh input + 500 output tokens
GPT Audio 1.5
GPT Audio 1.5 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
GPT Audio 1.5
GPT Audio 1.5 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
GPT Audio 1.5 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
GPT Audio 1.5 and o1-preview are not ranked on the public lane for agentic, so no winner is named for agentic.
Confidence: limited
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. GPT Audio 1.5 does not fit this workload in one request. o1-preview does not fit this workload in one request. GPT Audio 1.5 has no published cached-input rate, so cached tokens use its listed input rate. o1-preview 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.
Evidence parity totals are not available.
- Shared results
- 0
- GPT Audio 1.5 only
- 0
- o1-preview 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- GPT Audio 1.5
- Not ranked
- o1-preview
- 44.0
- Estimated · #96/151
- Basis
- BenchAlign lane · 0 vs 0 public rows
- Reading
- Not comparable
Reasoning
Not comparable- GPT Audio 1.5
- Not ranked
- o1-preview
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Knowledge
Not comparable- GPT Audio 1.5
- Not ranked
- o1-preview
- 44.8
- Estimated · #110/183
- Basis
- BenchAlign lane · 0 vs 0 public rows
- Reading
- Not comparable
Math
Not comparable- GPT Audio 1.5
- Not ranked
- o1-preview
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- GPT Audio 1.5
- Not ranked
- o1-preview
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- GPT Audio 1.5
- Not ranked
- o1-preview
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- GPT Audio 1.5
- Not ranked
- o1-preview
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
| Category | GPT Audio 1.5 | o1-preview | Basis | Reading |
|---|---|---|---|---|
| Agentic | Not ranked | Not ranked | Not comparableBenchAlign lane · 0 vs 0 public rows | Not comparable |
| Coding | Not ranked | 44.0Estimated · #96/151 | Not comparableBenchAlign lane · 0 vs 0 public rows | Not comparable |
| Reasoning | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Knowledge | Not ranked | 44.8Estimated · #110/183 | Not comparableBenchAlign lane · 0 vs 0 public rows | Not comparable |
| Math | Not ranked | Not ranked | Not comparableProvisional lane · 0 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 | Not ranked | 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
- GPT Audio 1.5
- $0.0075
- Fits in one request
- o1-preview
- $0.045
- Fits in one request
GPT Audio 1.5 has the lower modeled cost
Costs use the listed standard API rates.
Repository review
50K fresh input + 3K output tokens
- GPT Audio 1.5
- $0.155
- Fits in one request
- o1-preview
- $0.93
- Fits in one request
GPT Audio 1.5 has the lower modeled cost
Costs use the listed standard API rates.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- GPT Audio 1.5
- $0.65
- Does not fit in one request
- Cached input priced at the published list-input rate
- o1-preview
- $3.90
- Does not fit in one request
- Cached input priced at the published list-input rate
GPT Audio 1.5 does not fit this workload in one request. o1-preview does not fit this workload in one request. GPT Audio 1.5 has no published cached-input rate, so cached tokens use its listed input rate. o1-preview 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.
SpecificationGPT Audio 1.5o1-preview
Context window
Maximum documented context; output-token limits may be lower.
GPT Audio 1.5
o1-preview
200K
API model ID
GPT Audio 1.5
gpt-audio-1.5
OpenAI GPT Audio 1.5 model documentationo1-preview
Not sourced
Cached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
GPT Audio 1.5
Not published
OpenAI model documentationo1-preview
Not published
Documented inputs
GPT Audio 1.5
text, audio
OpenAI GPT Audio 1.5 model documentationo1-preview
Not sourced
Documented outputs
GPT Audio 1.5
text, audio
OpenAI GPT Audio 1.5 model documentationo1-preview
Not sourced
Provider availability
GPT Audio 1.5
Generally Available · OpenAI API
OpenAI GPT Audio 1.5 model documentationo1-preview
Not sourced
Reasoning profile
GPT Audio 1.5
Non-Reasoning
o1-preview
Reasoning
Weight access
GPT Audio 1.5
Proprietary
o1-preview
Proprietary
License
GPT Audio 1.5
Proprietary
o1-preview
Proprietary
Release date
GPT Audio 1.5
2026-04-23
o1-preview
2024-09-12
If you already use one of these models
- 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: $0.155 vs $0.93. Cache-heavy agent loop: $0.65 vs $3.90.
- Context tradeoff
- o1-preview has the larger documented window (200K).
Run the same representative tasks against both endpoints before changing production traffic.
Frequently asked questions
Which is better, GPT Audio 1.5 or o1-preview?
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, GPT Audio 1.5 or o1-preview?
GPT Audio 1.5 is not ranked on the public lane for coding, so no winner is named for coding.
Which is better for agentic tasks, GPT Audio 1.5 or o1-preview?
GPT Audio 1.5 and o1-preview are not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.
Which costs less, GPT Audio 1.5 or o1-preview?
For the stated presets, chat costs $0.0075 on GPT Audio 1.5 and $0.045 on o1-preview; repository review costs $0.155 and $0.93; the cache-heavy agent loop costs $0.65 and $3.90. GPT Audio 1.5 does not fit this workload in one request. o1-preview does not fit this workload in one request. GPT Audio 1.5 has no published cached-input rate, so cached tokens use its listed input rate. o1-preview has no published cached-input rate, so cached tokens use its listed input rate.
Which has the larger context window, GPT Audio 1.5 or o1-preview?
o1-preview has the larger documented context window: 200K, compared with 128K.
Related comparisons
Last updated September 10, 2026
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