Knowledge
Like-for-like- GPT-5.4 mini
- 47.8
- Inkling
- 51.6
- Weighted basis
- 2 vs 2 rows
- Reading
- Inkling leads
Five or fewer confirmed AI changes, with original sources, on mornings when something changed.A free source-linked morning brief.
Start the free Radar BriefUpdated August 29, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
Decision reading
Inkling has the higher public score estimate, 67.02 versus 56.96, but the 90% score intervals overlap. Treat that as a lead, not a settled 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
Inkling has the larger documented context window.
Confidence: documented
1K fresh input + 500 output tokens
GPT-5.4 mini
GPT-5.4 mini 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 mini
GPT-5.4 mini 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 mini
GPT-5.4 mini 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 mini | Inkling | Weighted basis | Reading |
|---|---|---|---|---|
| Knowledge | 47.8 | 51.6 | Like-for-like2 vs 2 rows | Inkling leads |
| Agentic | 65.7 | 69.4 | Directional only2 vs 2 rows | Directional only |
| Multimodal | 76.6 | 76.5 | Directional only1 vs 2 rows | Directional only |
| Coding | Not measured | 68.6 | Not comparable0 vs 2 rows | Not comparable |
| Reasoning | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Math | 21.7 | 97.1 | Not comparable2 vs 1 rows | Not comparable |
| Multilingual | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Instruction following | Not measured | 79.8 | 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.
HLE
Knowledge
Terminal-Bench 2.0
Agentic
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 mini has the lower modeled cost
Costs use the listed standard API rates.
50K fresh input + 3K output tokens
GPT-5.4 mini has the lower modeled cost
Costs use the listed standard API rates.
200K cached + 20K fresh input + 10K output tokens
GPT-5.4 mini 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 mini
Inkling
1M
GPT-5.4 mini
gpt-5.4-mini
OpenAI GPT-5.4 mini model documentationInkling
Not sourced
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
GPT-5.4 mini
$0.075 per 1M cached input tokens
OpenAI pricingInkling
$0.374 per 1M cached input tokens
GPT-5.4 mini
text, image
OpenAI model catalogInkling
Not sourced
GPT-5.4 mini
Inkling
Not sourced
GPT-5.4 mini
Generally Available · OpenAI Responses API
OpenAI model catalogInkling
Not sourced
GPT-5.4 mini
Reasoning
Inkling
Hybrid
GPT-5.4 mini
Proprietary
Inkling
Open Weight
GPT-5.4 mini
Proprietary
Inkling
Open Weight
GPT-5.4 mini
2026-03-17
Inkling
2026-07-15
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 leads this result
OSWorld-Verified
Not directly comparable
MCP Atlas
Inkling leads this result
Toolathlon
Not directly comparable
τ²-bench results
Not directly comparable
BrowseComp
Not directly comparable
Vibe Code Bench
Not directly comparable
FrontierCode 1.1 Main
Not directly comparable
SWE-bench Verified
Not directly comparable
SWE-bench Pro
Not directly comparable
Terminal-Bench 2.0
Not directly comparable
IFBench
Not directly comparable
Inkling has the higher public score estimate, 67.02 versus 56.96, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.
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 $0.003 on GPT-5.4 mini and $0.00421 on Inkling; repository review costs $0.051 and $0.10754; the cache-heavy agent loop costs $0.075 and $0.159. Costs use the listed standard API rates.
Inkling has the larger documented context window: 1M, compared with 400K.
Last updated August 29, 2026
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