Knowledge
Like-for-like- GPT-5.4 nano
- 43.8
- Inkling-Small
- 53.4
- Weighted basis
- 2 vs 2 rows
- Reading
- Inkling-Small leads
Model comparison
Updated July 30, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner.
6 results are shared. Category rows based on different benchmark sets are marked directional and do not name a winner.
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.
Prompts that approach the documented context limit
Inkling-Small
Inkling-Small has the larger documented context window.
Confidence: documented
1K fresh input + 500 output tokens
GPT-5.4 nano
GPT-5.4 nano 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
GPT-5.4 nano
GPT-5.4 nano 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
GPT-5.4 nano
GPT-5.4 nano has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
Code generation, repair, and software-engineering tasks
Not enough matched evidence
No shared weighted benchmark basis supports a winner.
Confidence: limited
Tool use, computer use, and multi-step task completion
Not enough matched evidence
The category averages use different weighted benchmark sets, so they are directional rather than like-for-like.
Confidence: limited
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
2 categories use different evidence sets. Those rows remain visible for coverage context but do not name a winner.
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.
| Category | GPT-5.4 nano | Inkling-Small | Weighted basis | Reading |
|---|---|---|---|---|
| Knowledge | 43.8 | 53.4 | Like-for-like2 vs 2 rows | Inkling-Small leads |
| Agentic | 42.9 | 70.1 | Directional only2 vs 2 rows | Directional only |
| Multimodal | 66.1 | 76.6 | Directional only1 vs 2 rows | Directional only |
| Coding | Not measured | 62.4 | Not comparable0 vs 3 rows | Not comparable |
| Reasoning | Not measured | 40.1 | Not comparable0 vs 1 rows | Not comparable |
| Math | 21.0 | 92.9 | Not comparable2 vs 2 rows | Not comparable |
| Multilingual | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Instruction following | Not measured | 82.2 | Not comparable0 vs 1 rows | Not comparable |
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.
Terminal-Bench 2.0
Agentic
HLE
Knowledge
MMMU-Pro
Multimodal
GPQA
Knowledge
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
GPT-5.4 nano has the lower modeled cost
Costs use the listed standard API rates.
50K fresh input + 3K output tokens
GPT-5.4 nano has the lower modeled cost
Costs use the listed standard API rates.
200K cached + 20K fresh input + 10K output tokens
GPT-5.4 nano 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-5.4 nano
Inkling-Small
1M
GPT-5.4 nano
gpt-5.4-nano
OpenAI GPT-5.4 nano model documentationInkling-Small
Not sourced
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
GPT-5.4 nano
$0.02 per 1M cached input tokens
OpenAI pricingInkling-Small
$0.116 per 1M cached input tokens
GPT-5.4 nano
text, image
OpenAI model catalogInkling-Small
Not sourced
GPT-5.4 nano
Inkling-Small
Not sourced
GPT-5.4 nano
Generally Available · OpenAI Responses API
OpenAI model catalogInkling-Small
Not sourced
GPT-5.4 nano
Reasoning
Inkling-Small
Hybrid
GPT-5.4 nano
Proprietary
Inkling-Small
Open Weight
GPT-5.4 nano
Proprietary
Inkling-Small
Open Weight
GPT-5.4 nano
2026-03-17
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 2.0
Inkling-Small leads this result
OSWorld-Verified
Not directly comparable
MCP Atlas
Inkling-Small leads this result
Toolathlon
Not directly comparable
τ²-bench results
Not directly comparable
BrowseComp
Not directly comparable
Toolathlon-Verified
Not directly comparable
Vibe Code Bench
Not directly comparable
SWE-bench Verified
Not directly comparable
SWE-bench Pro
Not directly comparable
Terminal-Bench 2.0
Not directly comparable
SciCode
Not directly comparable
GPQA
Inkling-Small leads this result
HLE
Inkling-Small leads this result
HLE w/o tools
Inkling-Small leads this result
GPQA-D
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
Inkling-Small 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
At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner. The page therefore keeps the decision tied to the specific documented workload.
The published evidence does not provide a shared weighted coding basis for both models, so BenchLM does not name a coding winner.
The current agentic tasks averages use different weighted benchmark sets, so BenchLM does not name a winner from them. Read the shared benchmark rows directly and test the models on the same task set.
For the stated presets, chat costs $0.00082 on GPT-5.4 nano and $0.0013 on Inkling-Small; repository review costs $0.01375 and $0.03332; the cache-heavy agent loop costs $0.0205 and $0.0492. Costs use the listed standard API rates.
Inkling-Small has the larger documented context window: 1M, compared with 400K.
Last updated July 30, 2026
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