Coding work
Code generation, repair, and software-engineering tasks
GPT-5.6 Sol
GPT-5.6 Sol leads on the public coding lane, 71.6 to 64.7, with Supported evidence for both models, although the 90% intervals overlap.
Confidence: limited
GPT-5.6 Sol vs GPT-5.6 Terra
Updated September 24, 2026. Rank says GPT-5.6 Sol is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty. This is a same-family comparison, so migration details appear when the source data supports them.
Both of these models will change. Get the price, version and retirement notices for the pair, each with its source. Follow model changes
Share or export
Decision reading
GPT-5.6 Sol has the higher public score estimate, 78.49 versus 72.58, but the 90% score intervals overlap. Treat that as a lead, not a settled winner. 32 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
- Shared results
- 32
- GPT-5.6 Sol only
- 5
- GPT-5.6 Terra only
- 0
- Like-for-like categories
- 5 / 8
Supported: GPT-5.6 Sol and GPT-5.6 TerraHow the comparison works
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.
Agentic work
Tool use, computer use, and multi-step task completion
GPT-5.6 Sol
GPT-5.6 Sol leads on the public agentic lane, 69.6 to 59.5, with Supported evidence for both models, although the 90% intervals overlap.
Confidence: limitedChat 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
Cache-heavy agent loop cost
200K cached + 20K fresh input + 10K 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-ratesRepository 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-ratesLong documents
Prompts that approach the documented context limit
No clear pick
The documented context windows are equal.
Confidence: documented
Which one for a specific job
Choose a job from the LLM Selector's task catalog to see the category row it rests on, under the same basis rules as the table below. A directional row stays directional; choosing a job never creates a winner.
The same task catalog as the LLM Selector. Each job names the evidence surface it rests on; nothing here adds a new score.
71.6GPT-5.6 Sol64.7GPT-5.6 Terra
Like-for-like · BenchAlign v5.7
GPT-5.6 Sol leads the like-for-like coding row, although the 90% intervals overlap.
Coding scores combine specific tasks and setups. Match the editor, harness, and effort to your workflow.
Same basis rules as the category table below
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
5 GPT-5.6 Sol only
32 Shared
1 category rests on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.
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 (Tier 4)Math
Normalized gap 14.7GPT-5.6 Sol:83.000%GPT-5.6 Terra:68.300%OSWorld 2.0Agentic
Normalized gap 12.4GPT-5.6 Sol:62.6%GPT-5.6 Terra:50.2%ARC-AGI-2Reasoning
Normalized gap 8.6GPT-5.6 Sol:92.5%GPT-5.6 Terra:83.9%ARC-AGI-3Reasoning
Normalized gap 7.0GPT-5.6 Sol:7.8%GPT-5.6 Terra:0.8%BrowseCompAgentic
Normalized gap 4.7GPT-5.6 Sol:92.2%GPT-5.6 Terra:87.5%
Bars run 0–100 on each benchmark’s normalized display scale
Category results, on a stated basis
Each row shows the public-lane category score for both models: the BenchAlign v5.7 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
Like-for-like- GPT-5.6 Sol
- 69.6
- Supported · #7/105
- GPT-5.6 Terra
- 59.5
- Supported · #18/105
- Basis
- BenchAlign v5.7 lane · 9 vs 9 public rows
- Reading
- GPT-5.6 Sol leads · intervals overlap
Coding
Like-for-like- GPT-5.6 Sol
- 71.6
- Supported · #6/135
- GPT-5.6 Terra
- 64.7
- Supported · #8/135
- Basis
- BenchAlign v5.7 lane · 12 vs 8 public rows
- Reading
- GPT-5.6 Sol leads · intervals overlap
Reasoning
Like-for-like- GPT-5.6 Sol
- 72.1
- #8/19
- GPT-5.6 Terra
- 65.4
- #12/19
- Basis
- Provisional lane · 2 vs 2 weighted rows
- Reading
- GPT-5.6 Sol leads
Multimodal
Like-for-like- GPT-5.6 Sol
- 87.6
- #5/50
- GPT-5.6 Terra
- 77.2
- #16/50
- Basis
- Provisional lane · 1 vs 1 weighted rows
- Reading
- GPT-5.6 Sol leads
Knowledge
Like-for-like- GPT-5.6 Sol
- 78.8
- Supported · #7/158
- GPT-5.6 Terra
- 71.1
- Supported · #9/158
- Basis
- BenchAlign v5.7 lane · 8 vs 8 public rows
- Reading
- GPT-5.6 Sol leads · intervals overlap
Instruction following
Directional only- GPT-5.6 Sol
- 87.7
- #28/124
- GPT-5.6 Terra
- 85.7
- #35/124
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Directional only
Multilingual
Not comparable- GPT-5.6 Sol
- Not ranked
- GPT-5.6 Terra
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- GPT-5.6 Sol
- 96.8
- Unranked · 3 rankable rows
- GPT-5.6 Terra
- 96.8
- Unranked · 3 rankable rows
- Basis
- Provisional lane · 2 vs 2 weighted rows
- Reading
- Not comparable
| Category | GPT-5.6 Sol | GPT-5.6 Terra | Basis | Reading |
|---|---|---|---|---|
| Agentic | 69.6Supported · #7/105 | 59.5Supported · #18/105 | Like-for-likeBenchAlign v5.7 lane · 9 vs 9 public rows | GPT-5.6 Sol leads · intervals overlap |
| Coding | 71.6Supported · #6/135 | 64.7Supported · #8/135 | Like-for-likeBenchAlign v5.7 lane · 12 vs 8 public rows | GPT-5.6 Sol leads · intervals overlap |
| Reasoning | 72.1#8/19 | 65.4#12/19 | Like-for-likeProvisional lane · 2 vs 2 weighted rows | GPT-5.6 Sol leads |
| Multimodal | 87.6#5/50 | 77.2#16/50 | Like-for-likeProvisional lane · 1 vs 1 weighted rows | GPT-5.6 Sol leads |
| Knowledge | 78.8Supported · #7/158 | 71.1Supported · #9/158 | Like-for-likeBenchAlign v5.7 lane · 8 vs 8 public rows | GPT-5.6 Sol leads · intervals overlap |
| Instruction following | 87.7#28/124 | 85.7#35/124 | Directional onlyProvisional lane · 0 vs 0 weighted rows | Directional only |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | 96.8Unranked · 3 rankable rows | 96.8Unranked · 3 rankable rows | Not comparableProvisional lane · 2 vs 2 weighted rows | Not comparable |
Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign v5.7) differ from the provisional-lane categories. Unranked scores sit on the provisional lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.
Supported evidence per lane · bars run 0–100Methodology
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 Sol
- $0.014
- Fits in one request
- GPT-5.6 Terra
- $0.008
- 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 Sol
- $0.26
- Fits in one request
- GPT-5.6 Terra
- $0.136
- 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 Sol
- $0.36
- Fits in one request
- GPT-5.6 Terra
- $0.2
- Fits in one request
GPT-5.6 Terra has the lower modeled cost
Costs use the listed standard API rates.
Cached input falls back to the list input rate only where a cached rate is unpublished
Specification differences
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
SpecificationGPT-5.6 SolGPT-5.6 Terra
Context window
Maximum documented context; output-token limits may be lower.
GPT-5.6 Sol
1.05M
OpenAI model catalogGPT-5.6 Terra
1.05M
OpenAI model catalogAPI model ID
GPT-5.6 Sol
gpt-5.6-sol
OpenAI model catalogGPT-5.6 Terra
gpt-5.6-terra
OpenAI model catalogCached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
GPT-5.6 Sol
$0.4 per 1M cached input tokens
OpenAI pricingGPT-5.6 Terra
$0.2 per 1M cached input tokens
OpenAI pricingDocumented inputs
GPT-5.6 Sol
text, image
OpenAI model catalogGPT-5.6 Terra
text, image
OpenAI model catalogDocumented outputs
GPT-5.6 Sol
GPT-5.6 Terra
Provider availability
GPT-5.6 Sol
Generally Available · OpenAI Responses API
OpenAI model catalogGPT-5.6 Terra
Generally Available · OpenAI Responses API
OpenAI model catalogReasoning profile
GPT-5.6 Sol
Reasoning
GPT-5.6 Terra
Reasoning
Weight access
GPT-5.6 Sol
Proprietary
GPT-5.6 Terra
Proprietary
License
GPT-5.6 Sol
Proprietary
GPT-5.6 Terra
Proprietary
Release date
GPT-5.6 Sol
2026-07-09
GPT-5.6 Terra
2026-07-09
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
- GPT-5.6 Sol has the higher public score estimate, 78.49 versus 72.58, but the 90% score intervals overlap.
- Workload cost
- Repository review: $0.26 vs $0.136. Cache-heavy agent loop: $0.36 vs $0.2.
- Context tradeoff
- Both models list 1.05M.
Run the same representative tasks against both endpoints before changing production traffic.
Questions
Which is better, GPT-5.6 Sol or GPT-5.6 Terra?
GPT-5.6 Sol has the higher public score estimate, 78.49 versus 72.58, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.
Which is better for coding, GPT-5.6 Sol or GPT-5.6 Terra?
GPT-5.6 Sol leads the public coding lane, 71.6 to 64.7, with Supported evidence for both models, although the 90% intervals overlap.
Which is better for agentic tasks, GPT-5.6 Sol or GPT-5.6 Terra?
GPT-5.6 Sol leads the public agentic tasks lane, 69.6 to 59.5, with Supported evidence for both models, although the 90% intervals overlap.
Which costs less, GPT-5.6 Sol or GPT-5.6 Terra?
For the stated presets, chat costs $0.014 on GPT-5.6 Sol and $0.008 on GPT-5.6 Terra; repository review costs $0.26 and $0.136; the cache-heavy agent loop costs $0.36 and $0.2. Costs use the listed standard API rates.
Which has the larger context window, GPT-5.6 Sol or GPT-5.6 Terra?
Both models list the same context window, 1.05M.
Benchmark evidence
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Browse raw public benchmark evidence37 rows
Agentic
Terminal-Bench 3.0
Shared sourceGPT-5.6 Sol34.6%GPT-5.6 Terra20.8%GPT-5.6 Sol leads this result
Terminal-Bench 2.1
Shared sourceGPT-5.6 Sol91.9%GPT-5.6 Terra87.4%GPT-5.6 Sol leads this result
BrowseComp
Shared sourceGPT-5.6 Sol92.2%GPT-5.6 Terra87.5%GPT-5.6 Sol leads this result
OSWorld 2.0
Shared sourceGPT-5.6 Sol62.6%GPT-5.6 Terra50.2%GPT-5.6 Sol leads this result
CyberGym
Shared sourceGPT-5.6 Sol84.5%GPT-5.6 Terra81.8%GPT-5.6 Sol leads this result
ExploitGym
Shared sourceGPT-5.6 Sol33.7%GPT-5.6 Terra23.2%GPT-5.6 Sol leads this result
Toolathlon
Shared sourceGPT-5.6 Sol58%GPT-5.6 Terra53.1%GPT-5.6 Sol leads this result
Terminal-Bench 2.1 (Vals)
GPT-5.6 Sol85.8%SourceGPT-5.6 Terra77.5%SourceGPT-5.6 Sol leads this result
ApprenticeBench
Shared sourceGPT-5.6 Sol26%GPT-5.6 Terra16%GPT-5.6 Sol leads this result
Coding
Bug Hunt Bench
GPT-5.6 Sol42 fixesSourceGPT-5.6 Terra—Not directly comparable
SWE-bench Pro
Shared sourceGPT-5.6 Sol64.6%GPT-5.6 Terra63.4%GPT-5.6 Sol leads this result
Terminal-Bench 2.1
Shared sourceGPT-5.6 Sol91.9%GPT-5.6 Terra87.4%GPT-5.6 Sol leads this result
DeepSWE
Shared sourceGPT-5.6 Sol72.7%GPT-5.6 Terra69.6%GPT-5.6 Sol leads this result
FrontierCode 1.1 Extended
Shared sourceGPT-5.6 Sol60.6%GPT-5.6 Terra55.8%GPT-5.6 Sol leads this result
FrontierSWE v2
GPT-5.6 Sol32.2%SourceGPT-5.6 Terra—Not directly comparable
cursorBench32
Shared sourceGPT-5.6 Sol67.2%GPT-5.6 Terra64.9%GPT-5.6 Sol leads this result
VulcanBench v3
GPT-5.6 Sol87.0%SourceGPT-5.6 Terra87.0%SourceTie
VulcanBench CII v1
GPT-5.6 Sol86.5%SourceGPT-5.6 Terra—Not directly comparable
LiveCodeBench (Vals)
GPT-5.6 Sol82.6%SourceGPT-5.6 Terra85.9%SourceGPT-5.6 Terra leads this result
SWE-bench (Vals)
GPT-5.6 Sol96.2%SourceGPT-5.6 Terra95.4%SourceGPT-5.6 Sol leads this result
cursorBench40
GPT-5.6 Sol41.7%SourceGPT-5.6 Terra—Not directly comparable
Reasoning
ARC-AGI-2
Shared sourceGPT-5.6 Sol92.5%GPT-5.6 Terra83.9%GPT-5.6 Sol leads this result
ARC-AGI-3
Shared sourceGPT-5.6 Sol7.8%GPT-5.6 Terra0.8%GPT-5.6 Sol leads this result
GeneBench-Pro
GPT-5.6 Sol28.7%SourceGPT-5.6 Terra—Not directly comparable
Multimodal
MMMU-Pro
Shared sourceGPT-5.6 Sol83%GPT-5.6 Terra80.7%GPT-5.6 Sol leads this result
MMMU-Pro w/ Python
Shared sourceGPT-5.6 Sol84.6%GPT-5.6 Terra82%GPT-5.6 Sol leads this result
Knowledge
GPQA
Shared sourceGPT-5.6 Sol94.6%GPT-5.6 Terra92.9%GPT-5.6 Sol leads this result
GPQA-D
Shared sourceGPT-5.6 Sol94.6%GPT-5.6 Terra92.9%GPT-5.6 Sol leads this result
HLE-Verified
Shared sourceGPT-5.6 Sol54.5%GPT-5.6 Terra51.1%GPT-5.6 Sol leads this result
LABBench2
Shared sourceGPT-5.6 Sol82.1%GPT-5.6 Terra81.2%GPT-5.6 Sol leads this result
HealthBench Professional
Shared sourceGPT-5.6 Sol60.5%GPT-5.6 Terra57.7%GPT-5.6 Sol leads this result
HealthBench Hard
Shared sourceGPT-5.6 Sol33.1%GPT-5.6 Terra32.7%GPT-5.6 Sol leads this result
GPQA Diamond (Vals)
GPT-5.6 Sol95.2%SourceGPT-5.6 Terra90.9%SourceGPT-5.6 Sol leads this result
MMLU-Pro (Vals)
GPT-5.6 Sol89.1%SourceGPT-5.6 Terra86.7%SourceGPT-5.6 Sol leads this result
Math
FrontierMath (legacy)
Shared sourceGPT-5.6 Sol89%GPT-5.6 Terra84.9%GPT-5.6 Sol leads this result
FrontierMath v2 (Tiers 1-3)
Shared sourceGPT-5.6 Sol89.000%GPT-5.6 Terra84.900%GPT-5.6 Sol leads this result
FrontierMath v2 (Tier 4)
Shared sourceGPT-5.6 Sol83.000%GPT-5.6 Terra68.300%GPT-5.6 Sol leads this result
37 public results · 32 shared
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Last updated September 24, 2026