Knowledge
Like-for-like- GPT-5.6 Terra
- 92.9
- o1
- 75.7
- Weighted basis
- 1 vs 1 rows
- Reading
- GPT-5.6 Terra leads
Model comparison
GPT-5.6 Terra vs o1
Updated August 10, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
GPT-5.6 Terra has the higher public score, 72.29 versus 47.17, and the 90% score intervals do not overlap.
2 results are shared. Category rows based on different benchmark sets are marked directional and do not name a winner.
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
GPT-5.6 Terra
GPT-5.6 Terra has the larger documented context window.
Confidence: documented
Chat turn cost
1K fresh input + 500 output tokens
GPT-5.6 Terra
GPT-5.6 Terra 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-5.6 Terra
GPT-5.6 Terra 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
No shared weighted benchmark basis supports a winner.
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
No shared weighted benchmark basis supports a winner.
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. o1 does not fit this workload in one request. o1 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.
20 GPT-5.6 Terra only
2 Shared
2 o1 only
- Shared results
- 2
- GPT-5.6 Terra only
- 20
- o1 only
- 2
- Like-for-like categories
- 1 / 8
1 category uses different evidence sets. Those rows remain visible for coverage context but do not name a winner.
Category results, on a stated basis
Each row states whether both averages use the same weighted benchmark set. Directional and not-comparable rows remain visible, but they never receive a winner in this template.
Math
Directional only- GPT-5.6 Terra
- 80.8
- o1
- 9.3
- Weighted basis
- 2 vs 1 rows
- Reading
- Directional only
Agentic
Not comparable- GPT-5.6 Terra
- 87.4
- o1
- Not measured
- Weighted basis
- 2 vs 0 rows
- Reading
- Not comparable
Coding
Not comparable- GPT-5.6 Terra
- 63.4
- o1
- Not measured
- Weighted basis
- 1 vs 0 rows
- Reading
- Not comparable
Reasoning
Not comparable- GPT-5.6 Terra
- 83.9
- o1
- Not measured
- Weighted basis
- 1 vs 0 rows
- Reading
- Not comparable
Multilingual
Not comparable- GPT-5.6 Terra
- Not measured
- o1
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Multimodal
Not comparable- GPT-5.6 Terra
- 80.7
- o1
- Not measured
- Weighted basis
- 1 vs 0 rows
- Reading
- Not comparable
Instruction following
Not comparable- GPT-5.6 Terra
- Not measured
- o1
- 92.2
- Weighted basis
- 0 vs 1 rows
- Reading
- Not comparable
| Category | GPT-5.6 Terra | o1 | Weighted basis | Reading |
|---|---|---|---|---|
| Knowledge | 92.9 | 75.7 | Like-for-like1 vs 1 rows | GPT-5.6 Terra leads |
| Math | 80.8 | 9.3 | Directional only2 vs 1 rows | Directional only |
| Agentic | 87.4 | Not measured | Not comparable2 vs 0 rows | Not comparable |
| Coding | 63.4 | Not measured | Not comparable1 vs 0 rows | Not comparable |
| Reasoning | 83.9 | Not measured | Not comparable1 vs 0 rows | Not comparable |
| Multilingual | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Multimodal | 80.7 | Not measured | Not comparable1 vs 0 rows | Not comparable |
| Instruction following | Not measured | 92.2 | Not comparable0 vs 1 rows | Not comparable |
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.
FrontierMath v2 (Tiers 1-3)
Math
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
- GPT-5.6 Terra
- $0.008
- Fits in one request
- o1
- $0.045
- Fits in one request
GPT-5.6 Terra has the lower modeled cost
Costs use the listed standard API rates.
Repository review
50K fresh input + 3K output tokens
- GPT-5.6 Terra
- $0.136
- Fits in one request
- o1
- $0.93
- Fits in one request
GPT-5.6 Terra 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-5.6 Terra
- $0.2
- Fits in one request
- o1
- $3.90
- 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 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-5.6 Terrao1
Context window
Maximum documented context; output-token limits may be lower.
GPT-5.6 Terra
1.05M
OpenAI model catalogo1
200K
API model ID
GPT-5.6 Terra
gpt-5.6-terra
OpenAI model catalogo1
Not sourced
Cached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
GPT-5.6 Terra
$0.2 per 1M cached input tokens
OpenAI pricingo1
Not published
Documented inputs
GPT-5.6 Terra
text, image
OpenAI model catalogo1
Not sourced
Documented outputs
GPT-5.6 Terra
o1
Not sourced
Provider availability
GPT-5.6 Terra
Generally Available · OpenAI Responses API
OpenAI model catalogo1
Not sourced
Reasoning profile
GPT-5.6 Terra
Reasoning
o1
Reasoning
Weight access
GPT-5.6 Terra
Proprietary
o1
Proprietary
License
GPT-5.6 Terra
Proprietary
o1
Proprietary
Release date
GPT-5.6 Terra
2026-07-09
o1
2024-12-01
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
- GPT-5.6 Terra has the higher public score, 72.29 versus 47.17, and the 90% score intervals do not overlap.
- Workload cost
- Repository review: $0.136 vs $0.93. Cache-heavy agent loop: $0.2 vs $3.90.
- Context tradeoff
- GPT-5.6 Terra has the larger documented window (1.05M).
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 evidence24 rows
Agentic
Terminal-Bench 2.0
GPT-5.6 Terra87.4%Sourceo1—Not directly comparable
BrowseComp
GPT-5.6 Terra87.5%Sourceo1—Not directly comparable
OSWorld 2.0
GPT-5.6 Terra50.2%Sourceo1—Not directly comparable
CyberGym
GPT-5.6 Terra81.8%Sourceo1—Not directly comparable
ExploitGym
GPT-5.6 Terra23.2%Sourceo1—Not directly comparable
Toolathlon
GPT-5.6 Terra53.1%Sourceo1—Not directly comparable
Coding
SWE-bench Pro
GPT-5.6 Terra63.4%Sourceo1—Not directly comparable
Terminal-Bench 2.0
GPT-5.6 Terra87.4%Sourceo1—Not directly comparable
deepSwe
GPT-5.6 Terra69.6%Sourceo1—Not directly comparable
FrontierCode 1.1 Extended
GPT-5.6 Terra55.8%Sourceo1—Not directly comparable
cursorBench32
GPT-5.6 Terra64.9%Sourceo1—Not directly comparable
Reasoning
Knowledge
GPQA
GPT-5.6 Terra92.9%Sourceo175.7%SourceGPT-5.6 Terra leads this result
GPQA-D
GPT-5.6 Terra92.9%Sourceo1—Not directly comparable
HealthBench Professional
GPT-5.6 Terra57.7%Sourceo1—Not directly comparable
HealthBench Hard
GPT-5.6 Terra32.7%Sourceo1—Not directly comparable
MMLU
GPT-5.6 Terra—o191.8%SourceNot directly comparable
Math
Multimodal
Instruction following
IFEval
GPT-5.6 Terra—o192.2%SourceNot directly comparable
Frequently asked questions
Which is better, GPT-5.6 Terra or o1?
GPT-5.6 Terra has the higher public score, 72.29 versus 47.17, and the 90% score intervals do not overlap. That is the clearest overall quality signal in the current public data.
Which is better for coding, GPT-5.6 Terra or o1?
The published evidence does not provide a shared weighted coding basis for both models, so BenchLM does not name a coding winner.
Which is better for agentic tasks, GPT-5.6 Terra or o1?
The published evidence does not provide a shared weighted agentic tasks basis for both models, so BenchLM does not name a agentic tasks winner.
Which costs less, GPT-5.6 Terra or o1?
For the stated presets, chat costs $0.008 on GPT-5.6 Terra and $0.045 on o1; repository review costs $0.136 and $0.93; the cache-heavy agent loop costs $0.2 and $3.90. o1 does not fit this workload in one request. o1 has no published cached-input rate, so cached tokens use its listed input rate.
Which has the larger context window, GPT-5.6 Terra or o1?
GPT-5.6 Terra has the larger documented context window: 1.05M, compared with 200K.
Related comparisons
Last updated August 10, 2026
Watch GPT-5.6 Terra vs o1
One weekly email when material rank, price, or benchmark evidence changes make this matchup worth revisiting.
Read a sample issueJoin 2,000+ readers.