Agentic
Like-for-like- GPT-5.5
- 61.0
- Supported · #17/152
- Inkling-Small
- 36.9
- Supported · #128/152
- Basis
- BenchAlign lane · 14 vs 5 public rows
- Reading
- GPT-5.5 leads
GPT-5.5 vs Inkling-Small
Updated September 10, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
Decision reading
GPT-5.5 has the higher public score, 72.07 versus 59.25, and the 90% score intervals do not overlap.
16 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.
Coding work
Code generation, repair, and software-engineering tasks
GPT-5.5
GPT-5.5 leads on the public coding lane, 67.6 to 43.8, with Supported evidence for both models and non-overlapping 90% intervals.
Confidence: stronger
Agentic work
Tool use, computer use, and multi-step task completion
GPT-5.5
GPT-5.5 leads on the public agentic lane, 61 to 36.9, with Supported evidence for both models and non-overlapping 90% intervals.
Confidence: stronger
Chat turn cost
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
Show secondary and unsupported calls
Cache-heavy agent loop cost
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
Repository review cost
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
Long documents
Prompts that approach the documented context limit
No clear pick
The documented context windows are equal.
Confidence: documented
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
23 GPT-5.5 only
16 Shared
9 Inkling-Small only
- Shared results
- 16
- GPT-5.5 only
- 23
- Inkling-Small only
- 9
- Like-for-like categories
- 3 / 8
2 categories rest 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.
Coding
Like-for-like- GPT-5.5
- 67.6
- Supported · #7/151
- Inkling-Small
- 43.8
- Supported · #97/151
- Basis
- BenchAlign lane · 9 vs 6 public rows
- Reading
- GPT-5.5 leads
Knowledge
Like-for-like- GPT-5.5
- 72.9
- Supported · #7/183
- Inkling-Small
- 56.5
- Supported · #46/183
- Basis
- BenchAlign lane · 6 vs 6 public rows
- Reading
- GPT-5.5 leads · intervals overlap
Multimodal
Directional only- GPT-5.5
- 71.3
- #19/48
- Inkling-Small
- 48.8
- #39/48
- Basis
- Provisional lane · 2 vs 2 weighted rows
- Reading
- Directional only
Instruction following
Directional only- GPT-5.5
- 93.2
- #7/123
- Inkling-Small
- 89.6
- #25/123
- Basis
- Provisional lane · 0 vs 1 weighted rows
- Reading
- Directional only
Reasoning
Not comparable- GPT-5.5
- 64.0
- #13/20
- Inkling-Small
- 42.7
- Unranked · 3 rankable rows
- Basis
- Provisional lane · 2 vs 1 weighted rows
- Reading
- Not comparable
Math
Not comparable- GPT-5.5
- 69.6
- Unranked · 3 rankable rows
- Inkling-Small
- 76.9
- Unranked · 2 rankable rows
- Basis
- Provisional lane · 2 vs 2 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- GPT-5.5
- Not ranked
- Inkling-Small
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
| Category | GPT-5.5 | Inkling-Small | Basis | Reading |
|---|---|---|---|---|
| Agentic | 61.0Supported · #17/152 | 36.9Supported · #128/152 | Like-for-likeBenchAlign lane · 14 vs 5 public rows | GPT-5.5 leads |
| Coding | 67.6Supported · #7/151 | 43.8Supported · #97/151 | Like-for-likeBenchAlign lane · 9 vs 6 public rows | GPT-5.5 leads |
| Knowledge | 72.9Supported · #7/183 | 56.5Supported · #46/183 | Like-for-likeBenchAlign lane · 6 vs 6 public rows | GPT-5.5 leads · intervals overlap |
| Multimodal | 71.3#19/48 | 48.8#39/48 | Directional onlyProvisional lane · 2 vs 2 weighted rows | Directional only |
| Instruction following | 93.2#7/123 | 89.6#25/123 | Directional onlyProvisional lane · 0 vs 1 weighted rows | Directional only |
| Reasoning | 64.0#13/20 | 42.7Unranked · 3 rankable rows | Not comparableProvisional lane · 2 vs 1 weighted rows | Not comparable |
| Math | 69.6Unranked · 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.
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.
ARC-AGI-2
Reasoning
Terminal-Bench 2.0
Agentic
HLE w/o tools
Knowledge
MMMU-Pro
Multimodal
BrowseComp
Agentic
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-5.5
- $0.02
- Fits in one request
- Inkling-Small
- $0.0013
- Fits in one request
Inkling-Small has the lower modeled cost
Costs use the listed standard API rates.
Repository review
50K fresh input + 3K output tokens
- GPT-5.5
- $0.34
- Fits in one request
- Inkling-Small
- $0.03332
- Fits in one request
Inkling-Small 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-5.5
- $0.5
- Fits in one request
- Inkling-Small
- $0.0492
- Fits in one request
Inkling-Small has the lower modeled cost
Costs use the listed standard API rates.
Specification differences
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
SpecificationGPT-5.5Inkling-Small
Context window
Maximum documented context; output-token limits may be lower.
GPT-5.5
Inkling-Small
1M
API model ID
GPT-5.5
gpt-5.5
OpenAI pricingInkling-Small
Not sourced
Cached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
GPT-5.5
$0.5 per 1M cached input tokens
OpenAI pricingInkling-Small
$0.116 per 1M cached input tokens
Documented inputs
GPT-5.5
Not sourced
Inkling-Small
Not sourced
Documented outputs
GPT-5.5
Not sourced
Inkling-Small
Not sourced
Provider availability
GPT-5.5
Not sourced
Inkling-Small
Not sourced
Reasoning profile
GPT-5.5
Reasoning
Inkling-Small
Hybrid
Weight access
GPT-5.5
Proprietary
Inkling-Small
Open Weight
License
GPT-5.5
Proprietary
Inkling-Small
Open Weight
Release date
GPT-5.5
2026-04-23
Inkling-Small
2026-07-30
If you already use one of these models
- Deployment change
- The models list different providers, so authentication, endpoint behavior, limits, and feature support may change.
- Quality signal
- GPT-5.5 has the higher public score, 72.07 versus 59.25, and the 90% score intervals do not overlap.
- Workload cost
- Repository review: $0.34 vs $0.03332. Cache-heavy agent loop: $0.5 vs $0.0492.
- Context tradeoff
- Both models list 1M.
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 evidence48 rows
Agentic
Terminal-Bench 2.0
GPT-5.582%SourceInkling-Small64.7%SourceGPT-5.5 leads this result
CyberGym
GPT-5.581.8%SourceInkling-Small—Not directly comparable
BrowseComp
GPT-5.584.4%SourceInkling-Small77.4%SourceGPT-5.5 leads this result
OSWorld-Verified
GPT-5.578.7%SourceInkling-Small—Not directly comparable
MCP Atlas
GPT-5.575.3%SourceInkling-Small79.6%SourceInkling-Small leads this result
Toolathlon
GPT-5.555.6%SourceInkling-Small—Not directly comparable
τ²-bench results
GPT-5.598%SourceInkling-Small—Not directly comparable
Gert Labs
GPT-5.572.93%SourceInkling-Small—Not directly comparable
ResearchClawBench
GPT-5.517.0%SourceInkling-Small—Not directly comparable
OSWorld 2.0
GPT-5.513.0%SourceInkling-Small—Not directly comparable
JobBench
GPT-5.542.7%SourceInkling-Small—Not directly comparable
ExploitGym
GPT-5.513.4%SourceInkling-Small—Not directly comparable
Terminal-Bench 2.1 (Vals)
GPT-5.576.4%SourceInkling-Small55.1%SourceGPT-5.5 leads this result
ApprenticeBench
GPT-5.520%SourceInkling-Small—Not directly comparable
Toolathlon-Verified
GPT-5.5—Inkling-Small54.4%SourceNot directly comparable
Coding
SWE-bench Pro
GPT-5.558.6%SourceInkling-Small55.9%SourceGPT-5.5 leads this result
Terminal-Bench 2.0
GPT-5.582.0%SourceInkling-Small64.7%SourceGPT-5.5 leads this result
Vibe Code Bench
GPT-5.569.85%SourceInkling-Small—Not directly comparable
React Native Evals
GPT-5.584.7%SourceInkling-Small—Not directly comparable
cursorBench31
GPT-5.559.2%SourceInkling-Small—Not directly comparable
cursorBench32
GPT-5.558.4%SourceInkling-Small—Not directly comparable
FrontierCode 1.1 Main
GPT-5.543.0%SourceInkling-Small—Not directly comparable
LiveCodeBench (Vals)
GPT-5.585.3%SourceInkling-Small85.9%SourceInkling-Small leads this result
SWE-bench (Vals)
GPT-5.582.6%SourceInkling-Small82.2%SourceGPT-5.5 leads this result
SWE-bench Verified
GPT-5.5—Inkling-Small80.2%SourceNot directly comparable
SciCode
GPT-5.5—Inkling-Small48.7%SourceNot directly comparable
Reasoning
MRCR v2 64K-128K
GPT-5.583.1%SourceInkling-Small—Not directly comparable
MRCR v2 128K-256K
GPT-5.587.5%SourceInkling-Small—Not directly comparable
ARC-AGI-2
GPT-5.585%SourceInkling-Small40.1%SourceGPT-5.5 leads this result
ARC-AGI-3
GPT-5.50.4%SourceInkling-Small—Not directly comparable
CritPt
GPT-5.5—Inkling-Small8.3%SourceNot directly comparable
Knowledge
GPQA
GPT-5.593.6%SourceInkling-Small89.5%SourceGPT-5.5 leads this result
GPQA-D
GPT-5.593.6%SourceInkling-Small89.5%SourceGPT-5.5 leads this result
HLE
GPT-5.552.2%SourceInkling-Small47.8%SourceGPT-5.5 leads this result
HLE w/o tools
GPT-5.541.4%SourceInkling-Small31.6%SourceGPT-5.5 leads this result
GPQA Diamond (Vals)
GPT-5.593.2%SourceInkling-Small83.6%SourceGPT-5.5 leads this result
MMLU-Pro (Vals)
GPT-5.588.1%SourceInkling-Small85.6%SourceGPT-5.5 leads this result
Math
FrontierMath (legacy)
GPT-5.551.7%SourceInkling-Small—Not directly comparable
FrontierMath v2 (Tiers 1-3)
GPT-5.551.700%SourceInkling-Small—Not directly comparable
FrontierMath v2 (Tier 4)
GPT-5.535.400%SourceInkling-Small—Not directly comparable
AIME26
GPT-5.5—Inkling-Small95.5%SourceNot directly comparable
HMMT Feb 2026
GPT-5.5—Inkling-Small90.2%SourceNot directly comparable
Multimodal
MMMU-Pro
GPT-5.581.2%SourceInkling-Small74%SourceGPT-5.5 leads this result
MMMU-Pro w/ Python
GPT-5.583.2%SourceInkling-Small—Not directly comparable
OfficeQA Pro
GPT-5.554.1%SourceInkling-Small—Not directly comparable
CharXiv
GPT-5.5—Inkling-Small81.3%SourceNot directly comparable
CharXiv w/o tools
GPT-5.5—Inkling-Small77.4%SourceNot directly comparable
Instruction following
IFBench
GPT-5.5—Inkling-Small82.2%SourceNot directly comparable
Frequently asked questions
Which is better, GPT-5.5 or Inkling-Small?
GPT-5.5 has the higher public score, 72.07 versus 59.25, 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-5.5 or Inkling-Small?
GPT-5.5 leads the public coding lane, 67.6 to 43.8, with Supported evidence for both models and non-overlapping 90% intervals.
Which is better for agentic tasks, GPT-5.5 or Inkling-Small?
GPT-5.5 leads the public agentic tasks lane, 61 to 36.9, with Supported evidence for both models and non-overlapping 90% intervals.
Which costs less, GPT-5.5 or Inkling-Small?
For the stated presets, chat costs $0.02 on GPT-5.5 and $0.0013 on Inkling-Small; repository review costs $0.34 and $0.03332; the cache-heavy agent loop costs $0.5 and $0.0492. Costs use the listed standard API rates.
Which has the larger context window, GPT-5.5 or Inkling-Small?
Both models list the same context window, 1M.
Related comparisons
Last updated September 10, 2026
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