Agentic
Directional only- GPT-5.4 Pro
- 89.3
- Inkling
- 69.4
- Weighted basis
- 1 vs 2 rows
- Reading
- Directional only
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 61.53, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.
4 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
GPT-5.4 Pro
GPT-5.4 Pro has the larger documented context window.
Confidence: documented
1K fresh input + 500 output tokens
Inkling
Inkling 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
Inkling has the lower estimated token cost for this stated workload. GPT-5.4 Pro has no published cached-input rate, so cached tokens use its listed input rate.
Confidence: rate-fallback
50K fresh input + 3K output tokens
Inkling
Inkling 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.
3 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 Pro | Inkling | Weighted basis | Reading |
|---|---|---|---|---|
| Agentic | 89.3 | 69.4 | Directional only1 vs 2 rows | Directional only |
| Knowledge | 58.7 | 51.6 | Directional only1 vs 2 rows | Directional only |
| Multimodal | 94.0 | 76.5 | Directional only1 vs 2 rows | Directional only |
| Coding | Not measured | 68.6 | Not comparable0 vs 2 rows | Not comparable |
| Reasoning | 83.3 | Not measured | Not comparable1 vs 0 rows | Not comparable |
| Math | 46.9 | 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.
MMMU-Pro
Multimodal
HLE
Knowledge
BrowseComp
Agentic
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 has the lower modeled cost
Costs use the listed standard API rates.
50K fresh input + 3K output tokens
Inkling has the lower modeled cost
Costs use the listed standard API rates.
200K cached + 20K fresh input + 10K output tokens
Inkling has the lower modeled cost
GPT-5.4 Pro has no published cached-input rate, so cached tokens use its listed input rate.
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 Pro
Inkling
1M
GPT-5.4 Pro
gpt-5.4-pro
OpenAI GPT-5.4 Pro 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 Pro
Not published
OpenAI pricingInkling
$0.374 per 1M cached input tokens
GPT-5.4 Pro
text, image
OpenAI model catalogInkling
Not sourced
GPT-5.4 Pro
Inkling
Not sourced
GPT-5.4 Pro
Generally Available · OpenAI Responses API
OpenAI model catalogInkling
Not sourced
GPT-5.4 Pro
Reasoning
Inkling
Hybrid
GPT-5.4 Pro
Proprietary
Inkling
Open Weight
GPT-5.4 Pro
Proprietary
Inkling
Open Weight
GPT-5.4 Pro
2026-03-05
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.
ARC-AGI-2
Not directly comparable
HLE
GPT-5.4 Pro leads this result
FrontierScience
Not directly comparable
FrontierScience Research
Not directly comparable
HLE w/o tools
GPT-5.4 Pro leads this result
GPQA
Not directly comparable
GPQA-D
Not directly comparable
IPhO 2025 (Theory)
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
IFBench
Not directly comparable
Inkling has the higher public score estimate, 67.02 versus 61.53, 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 $0.12 on GPT-5.4 Pro and $0.00421 on Inkling; repository review costs $2.04 and $0.10754; the cache-heavy agent loop costs $8.40 and $0.159. GPT-5.4 Pro has no published cached-input rate, so cached tokens use its listed input rate.
GPT-5.4 Pro has the larger documented context window: 1.05M, compared with 1M.
Last updated August 29, 2026
One weekly email when material rank, price, or benchmark evidence changes make this matchup worth revisiting.
Read a sample issueJoin 2,000+ readers.