Agentic
Like-for-like- GPT-5.6 Terra
- 60.2
- Supported · #20/152
- Inkling-Small
- 36.9
- Supported · #128/152
- Basis
- BenchAlign lane · 9 vs 5 public rows
- Reading
- GPT-5.6 Terra leads
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Follow model changesUpdated September 10, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
Decision reading
GPT-5.6 Terra has the higher public score estimate, 71.13 versus 59.25, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.
13 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
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.
Code generation, repair, and software-engineering tasks
GPT-5.6 Terra
GPT-5.6 Terra leads on the public coding lane, 67 to 43.8, with Supported evidence for both models and non-overlapping 90% intervals.
Confidence: stronger
Tool use, computer use, and multi-step task completion
GPT-5.6 Terra
GPT-5.6 Terra leads on the public agentic lane, 60.2 to 36.9, with Supported evidence for both models and non-overlapping 90% intervals.
Confidence: stronger
Prompts that approach the documented context limit
GPT-5.6 Terra
GPT-5.6 Terra has the larger documented context window.
Confidence: documented
1K fresh input + 500 output tokens
Inkling-Small
Inkling-Small has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
200K cached + 20K fresh input + 10K output tokens
Inkling-Small
Inkling-Small has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
50K fresh input + 3K output tokens
Inkling-Small
Inkling-Small has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
2 categories rest on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.
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.
| Category | GPT-5.6 Terra | Inkling-Small | Basis | Reading |
|---|---|---|---|---|
| Agentic | 60.2Supported · #20/152 | 36.9Supported · #128/152 | Like-for-likeBenchAlign lane · 9 vs 5 public rows | GPT-5.6 Terra leads |
| Coding | 67.0Supported · #8/151 | 43.8Supported · #97/151 | Like-for-likeBenchAlign lane · 9 vs 6 public rows | GPT-5.6 Terra leads |
| Knowledge | 69.2Supported · #16/183 | 56.5Supported · #46/183 | Like-for-likeBenchAlign lane · 8 vs 6 public rows | GPT-5.6 Terra leads · intervals overlap |
| Multimodal | 77.1#15/48 | 48.8#39/48 | Directional onlyProvisional lane · 1 vs 2 weighted rows | Directional only |
| Instruction following | 87.1#34/123 | 89.6#25/123 | Directional onlyProvisional lane · 0 vs 1 weighted rows | Directional only |
| Reasoning | 63.7#14/20 | 42.7Unranked · 3 rankable rows | Not comparableProvisional lane · 2 vs 1 weighted rows | Not comparable |
| Math | 97.0Unranked · 3 rankable rows | 76.9Unranked · 2 rankable rows | Not comparableProvisional lane · 2 vs 2 weighted rows | Not comparable |
| Multilingual | 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.
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.
ARC-AGI-2
Reasoning
Terminal-Bench 2.0
Agentic
BrowseComp
Agentic
SWE-bench Pro
Coding
MMMU-Pro
Multimodal
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.
1K fresh input + 500 output tokens
Inkling-Small has the lower modeled cost
Costs use the listed standard API rates.
50K fresh input + 3K output tokens
Inkling-Small has the lower modeled cost
Costs use the listed standard API rates.
200K cached + 20K fresh input + 10K output tokens
Inkling-Small has the lower modeled cost
Costs use the listed standard API rates.
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
Maximum documented context; output-token limits may be lower.
GPT-5.6 Terra
1.05M
OpenAI model catalogInkling-Small
1M
GPT-5.6 Terra
gpt-5.6-terra
OpenAI model catalogInkling-Small
Not sourced
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 pricingInkling-Small
$0.116 per 1M cached input tokens
GPT-5.6 Terra
text, image
OpenAI model catalogInkling-Small
Not sourced
GPT-5.6 Terra
Inkling-Small
Not sourced
GPT-5.6 Terra
Generally Available · OpenAI Responses API
OpenAI model catalogInkling-Small
Not sourced
GPT-5.6 Terra
Reasoning
Inkling-Small
Hybrid
GPT-5.6 Terra
Proprietary
Inkling-Small
Open Weight
GPT-5.6 Terra
Proprietary
Inkling-Small
Open Weight
GPT-5.6 Terra
2026-07-09
Inkling-Small
2026-07-30
Run the same representative tasks against both endpoints before changing production traffic.
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Terminal-Bench 3.0
Not directly comparable
Terminal-Bench 2.0
GPT-5.6 Terra leads this result
BrowseComp
GPT-5.6 Terra leads this result
OSWorld 2.0
Not directly comparable
CyberGym
Not directly comparable
ExploitGym
Not directly comparable
Toolathlon
Not directly comparable
Terminal-Bench 2.1 (Vals)
GPT-5.6 Terra leads this result
ApprenticeBench
Not directly comparable
MCP Atlas
Not directly comparable
Toolathlon-Verified
Not directly comparable
SWE-bench Pro
GPT-5.6 Terra leads this result
Terminal-Bench 2.0
GPT-5.6 Terra leads this result
DeepSWE
Not directly comparable
FrontierCode 1.1 Extended
Not directly comparable
cursorBench32
Not directly comparable
VulcanBench v3
Not directly comparable
LiveCodeBench (Vals)
Tie
SWE-bench (Vals)
GPT-5.6 Terra leads this result
cursorBench40
Not directly comparable
SWE-bench Verified
Not directly comparable
SciCode
Not directly comparable
GPQA
GPT-5.6 Terra leads this result
GPQA-D
GPT-5.6 Terra leads this result
HLE-Verified
Not directly comparable
LABBench2
Not directly comparable
HealthBench Professional
Not directly comparable
HealthBench Hard
Not directly comparable
GPQA Diamond (Vals)
GPT-5.6 Terra leads this result
MMLU-Pro (Vals)
GPT-5.6 Terra leads this result
HLE
Not directly comparable
HLE w/o tools
Not directly comparable
FrontierMath (legacy)
Not directly comparable
FrontierMath v2 (Tiers 1-3)
Not directly comparable
FrontierMath v2 (Tier 4)
Not directly comparable
AIME26
Not directly comparable
HMMT Feb 2026
Not directly comparable
MMMU-Pro
GPT-5.6 Terra leads this result
MMMU-Pro w/ Python
Not directly comparable
CharXiv
Not directly comparable
CharXiv w/o tools
Not directly comparable
IFBench
Not directly comparable
GPT-5.6 Terra has the higher public score estimate, 71.13 versus 59.25, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.
GPT-5.6 Terra leads the public coding lane, 67 to 43.8, with Supported evidence for both models and non-overlapping 90% intervals.
GPT-5.6 Terra leads the public agentic tasks lane, 60.2 to 36.9, with Supported evidence for both models and non-overlapping 90% intervals.
For the stated presets, chat costs $0.008 on GPT-5.6 Terra and $0.0013 on Inkling-Small; repository review costs $0.136 and $0.03332; the cache-heavy agent loop costs $0.2 and $0.0492. Costs use the listed standard API rates.
GPT-5.6 Terra has the larger documented context window: 1.05M, compared with 1M.
Last updated September 10, 2026
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